Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench | Paper Citation Record

Observation c01e7bc0-e8b3-455e-9f03-15912fb9bb68 · outbound

This paper cites Social Skill Training with Large Language Models.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Social Skill Training with Large Language Models

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.505709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 37ab29bd-9789-4aef-acda-bdbd30d454ae · outbound

This paper cites AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-20T15:33:25.466243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a826ecc9-96ad-4843-86cc-7c9db0a0e154 · outbound

This paper cites Trendsim: Simulating trending topics in social media under poisoning attacks with llm-based multi-agent system.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Trendsim: Simulating trending topics in social media under poisoning attacks with llm-based multi-agent system

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.877913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 71595ced-b51e-45ce-be15-a5d4eb251d4d · outbound

This paper cites User behavior simulation with large language model-based agents.ACM Transactions on Information Systems, 43(2):1–37.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench User behavior simulation with large language model-based agents.ACM Transactions on Information Systems, 43(2):1–37

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.881977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8936240e-e6b2-43bf-a39d-e4a552b6309c · outbound

This paper cites Llm-based multi-agent system for simulating and analyzing marketing and consumer behavior.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Llm-based multi-agent system for simulating and analyzing marketing and consumer behavior

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.832395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 71c724c9-c2d5-4352-a5d8-1cfd0fd82c05 · outbound

This paper cites From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.478906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 795fa78a-53ca-4faa-84e0-20b63f7a7cf7 · outbound

This paper cites Performance and biases of large language models in public opinion simulation.Humanities and Social Sciences Communications, 11(1):1–13.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Performance and biases of large language models in public opinion simulation.Humanities and Social Sciences Communications, 11(1):1–13

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.874078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 39729a45-696b-44f3-b706-f41eef5f010d · outbound

This paper cites LLM-based Human Simulations Have Not Yet Been Reliable.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench LLM-based Human Simulations Have Not Yet Been Reliable

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-07T03:18:07.787194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3474ac14-f526-4a52-a4ae-23d4a46b994f · outbound

This paper cites Theory of mind in large language models: Assessment and enhancement.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Theory of mind in large language models: Assessment and enhancement

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.858285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a2ec728a-189f-4b57-bd95-f6211c1108c7 · outbound

This paper cites Recusersim: A realistic and diverse user simulator for evaluating conversational recommender systems.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Recusersim: A realistic and diverse user simulator for evaluating conversational recommender systems

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.824710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f4d5b36b-6722-4653-914b-45fe3370781f · outbound

This paper cites Llms reproduce human purchase intent via semantic similarity elicitation of likert ratings.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Llms reproduce human purchase intent via semantic similarity elicitation of likert ratings

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.425726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 38a18771-a4e5-43d6-b053-b98381ad76b0 · outbound

This paper cites Simulating public opinion: Comparing distributional and individual-level predictions from llms and random forests.Entropy, 27(9):923.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Simulating public opinion: Comparing distributional and individual-level predictions from llms and random forests.Entropy, 27(9):923

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.845184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1fc70ad9-be0f-48ac-969f-79ac495623ac · outbound

This paper cites LLM Agents Predict Social Media Reactions but Do Not Outperform Text Classifiers: Benchmarking Simulation Accuracy Using 120K+ Personas of 1511 Humans.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench LLM Agents Predict Social Media Reactions but Do Not Outperform Text Classifiers: Benchmarking Simulation Accuracy Using 120K+ Personas of 1511 Humans

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-20T15:33:25.540041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 178b2804-16cb-42be-aa98-a7db25c0d6fa · outbound

This paper cites Towards simulating social media users with llms: Evaluating the operational validity of conditioned comment prediction.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Towards simulating social media users with llms: Evaluating the operational validity of conditioned comment prediction

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.927899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 49d33115-04b2-4354-9965-8dfcc1300047 · outbound

This paper cites Smp challenge: An overview and analysis of social media prediction challenge.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Smp challenge: An overview and analysis of social media prediction challenge

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.932210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cd1abd17-5196-419b-bcc1-9e5e2f16d4a1 · outbound

This paper cites SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-20T15:33:25.531041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 068be4a2-5e0b-4f45-97e7-9fe897de3428 · outbound

This paper cites Charactereval: A chinese benchmark for role-playing conversational agent evaluation.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Charactereval: A chinese benchmark for role-playing conversational agent evaluation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.908476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3a016d27-9765-4e04-bb08-d2bd95cb4dcb · outbound

This paper cites Roleagent: Building, interacting, and benchmarking high-quality role-playing agents from scripts.Advances in Neural Information Processing Systems, 37:49403–49428.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Roleagent: Building, interacting, and benchmarking high-quality role-playing agents from scripts.Advances in Neural Information Processing Systems, 37:49403–49428

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.919655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ab4846b8-f80d-4827-ad06-5e72f93461f1 · outbound

This paper cites A Survey on LLM-as-a-Judge.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench A Survey on LLM-as-a-Judge

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-05-20T15:33:25.536603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6ff4e135-e758-499d-b16a-62bad03ddb6d · outbound

This paper cites Humans or LLMs as the judge? a study on judgement bias.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Humans or LLMs as the judge? a study on judgement bias

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.897068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6c54b21c-718e-4c0d-aae6-8ae3e665491d · outbound

This paper cites Mmlu-pro: A more robust and challenging multi-task language understanding benchmark.Advances in Neural Information Processing Systems, 37:95266–95290.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Mmlu-pro: A more robust and challenging multi-task language understanding benchmark.Advances in Neural Information Processing Systems, 37:95266–95290

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.899622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b2adfedd-5309-4552-a679-72f318d8a7ed · outbound

This paper cites SWE-bench: Can Language Models Resolve Real-World GitHub Issues?.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-20T15:33:25.527940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 715c29b0-5284-465f-88ec-2763e48ffdd8 · outbound

This paper cites Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-20T15:33:25.519246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 47588c25-c158-481c-8111-8af767a24f72 · outbound

This paper cites Fantom: A benchmark for stress-testing machine theory of mind in interactions.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Fantom: A benchmark for stress-testing machine theory of mind in interactions

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.905972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7829a499-67d9-47d3-879e-bab3a4582435 · outbound

This paper cites Opentom: A comprehensive benchmark for evaluating theory-of-mind reasoning capabilities of large language models.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Opentom: A comprehensive benchmark for evaluating theory-of-mind reasoning capabilities of large language models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.923864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0bf46556-9f86-4050-a148-28be1118dc92 · outbound

This paper cites O’Brien, Carrie J.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench O’Brien, Carrie J

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.879801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6762a6f3-1f8c-4049-9377-a31d093700b8 · outbound

This paper cites Arriaga, and Adam Tauman Kalai.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Arriaga, and Adam Tauman Kalai

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.884104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 982e131c-ae28-4476-87a3-27feb3bcb765 · outbound

This paper cites Large language models as psychological simulators: A methodological guide.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Large language models as psychological simulators: A methodological guide

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.872010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6a73177f-b558-4b3c-a368-34c8fef5fde5 · outbound

This paper cites A Cross-Cultural Comparison of LLM-based Public Opinion Simulation: Evaluating Chinese and U.S. Models on Diverse Societies.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench A Cross-Cultural Comparison of LLM-based Public Opinion Simulation: Evaluating Chinese and U.S. Models on Diverse Societies

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.445659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d9866878-64d7-42f9-a98b-123bfdc6653d · outbound

This paper cites Extracting consumer insight from text: A large language model approach to emotion and evaluation measurement.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Extracting consumer insight from text: A large language model approach to emotion and evaluation measurement

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.435018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 660293aa-6527-4527-a5f3-b595b808e5a2 · outbound

This paper cites Large language models for market research: A data-augmentation approach.Marketing Science.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Large language models for market research: A data-augmentation approach.Marketing Science

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.886232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5b1ea826-bbf0-4d1b-a170-c48402c9596c · outbound

This paper cites Predicting results of social science experiments using large language models.Preprint.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Predicting results of social science experiments using large language models.Preprint

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.854088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b8046455-6f75-4c2c-b3a0-c031bd5c9153 · outbound

This paper cites LLM Social Simulations Are a Promising Research Method.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench LLM Social Simulations Are a Promising Research Method

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Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T15:33:25.450992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ac4d0d13-4393-417e-a9a8-da0472802c60 · outbound

This paper cites Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.472667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5992c688-c2d8-46bb-8b6f-c481675a3727 · outbound

This paper cites Evaluating the ability of large language models to predict human social decisions.Scientific Reports, 15(1):32290.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Evaluating the ability of large language models to predict human social decisions.Scientific Reports, 15(1):32290

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.847690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f01acf2a-ebf9-4b87-ab77-5484c7a92d59 · outbound

This paper cites Using Large Language Models to Simulate Human Behavioural Experiments: Port of Mars.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Using Large Language Models to Simulate Human Behavioural Experiments: Port of Mars

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Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.469812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation edc61dc6-d7d7-4633-8cf6-5c15330a0022 · outbound

This paper cites OASIS: Open Agent Social Interaction Simulations with One Million Agents.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench OASIS: Open Agent Social Interaction Simulations with One Million Agents

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Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.456855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c02ffb49-d792-4301-a5fd-655ca4a10490 · outbound

This paper cites SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents

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Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.492532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 40066b96-9045-4e39-a047-92ef8431e9bb · outbound

This paper cites Think socially via cognitive reasoning.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Think socially via cognitive reasoning

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.533789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0c3a4f59-4e59-4a1d-8dec-972c6eec63eb · outbound

This paper cites Infusing Theory of Mind into Socially Intelligent LLM Agents.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Infusing Theory of Mind into Socially Intelligent LLM Agents

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Resolution: verified exact; local_arxiv, observed 2026-05-20T15:33:25.463149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5088f59f-5287-48d0-b613-24305d0d86b3 · outbound

This paper cites A foundation model to predict and capture human cognition.Nature, 644(8078):1002–1009.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench A foundation model to predict and capture human cognition.Nature, 644(8078):1002–1009

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.840996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1b39f71d-d1fd-4eb2-9c32-4b35a8e541c8 · outbound

This paper cites Customer-r1: Personal- ized simulation of human behaviors via rl-based llm agent in online shopping.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Customer-r1: Personal- ized simulation of human behaviors via rl-based llm agent in online shopping

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Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.547663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 17f985c0-07b1-43fb-bc92-83fe902b3ab3 · outbound

This paper cites Customer-r1: per- sonalized simulation of human behaviors via rl-based llm agent in online shopping.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Customer-r1: per- sonalized simulation of human behaviors via rl-based llm agent in online shopping

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.843015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation db4a6149-e32b-4074-829f-8fdd1bdce0a1 · outbound

This paper cites Using llms for market research.Harvard Business School Marketing Unit Working Paper, (23-062).

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Using llms for market research.Harvard Business School Marketing Unit Working Paper, (23-062)

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.834882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2b2c3e5d-ebdb-4d61-ad5a-700d045e2d0f · outbound

This paper cites The silicon sample: Benchmarking synthetic users against human respondents in market research.Available at SSRN 5835122.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench The silicon sample: Benchmarking synthetic users against human respondents in market research.Available at SSRN 5835122

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.837161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7ab7ca4d-3575-43b0-8dbf-bc726a30ac98 · outbound

This paper cites Twinmarket: A scalable behavioral and social simulation for financial markets.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Twinmarket: A scalable behavioral and social simulation for financial markets

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Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.508922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 39013794-015c-4535-b16f-e2c1ba56e2a9 · outbound

This paper cites Econagent: large language model-empowered agents for simulating macroeconomic activities.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Econagent: large language model-empowered agents for simulating macroeconomic activities

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.888507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0869a374-2271-440c-8aa5-4656ba34b56d · outbound

This paper cites an unresolved cited work.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-05-20T15:33:25.839010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d2012155-d397-445e-97d2-a805534a4dea · outbound

This paper cites Sell More, Play Less: Benchmarking LLM Realistic Selling Skill.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Sell More, Play Less: Benchmarking LLM Realistic Selling Skill

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-05-20T15:33:25.525136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 042e5ff0-7b40-4cbf-be00-7de2a274aa4b · outbound

This paper cites DEBATE: A Large-Scale Benchmark for Evaluating Opinion Dynamics in Role-Playing LLM Agents.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench DEBATE: A Large-Scale Benchmark for Evaluating Opinion Dynamics in Role-Playing LLM Agents

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-06-01T02:02:21.388075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ce38863d-f1a8-4266-9729-eb0be4ec3376 · outbound

This paper cites Benchmark- ing overton pluralism in llms.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Benchmark- ing overton pluralism in llms

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.453979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e67efb38-2b08-401e-908e-6b7901a976d5 · outbound

This paper cites an unresolved cited work.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-05-20T15:33:25.828688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4860810f-253d-42de-9ebe-a9eeb71a11df · outbound

This paper cites arXiv preprint arXiv:2601.17087 , year=.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench arXiv preprint arXiv:2601.17087 , year=

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.515664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bc7c261d-24d0-4010-8c03-ba1b4b234d8b · outbound

This paper cites War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.475698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 84154301-bf51-4574-a4d9-8e8123efaae9 · outbound

This paper cites S$^3$: Social-network Simulation System with Large Language Model-Empowered Agents.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench S$^3$: Social-network Simulation System with Large Language Model-Empowered Agents

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-05-20T15:33:25.428676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 07eb2c06-7b83-410e-8066-c57417409ad5 · outbound

This paper cites Gensim: A general social simulation platform with large language model based agents.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Gensim: A general social simulation platform with large language model based agents

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.818590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8140fc86-338e-45d4-8672-d9d7c9c48807 · outbound

This paper cites SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.459810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f83298ed-7945-4add-bf68-0483c7a8d757 · outbound

This paper cites Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.860444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 81cf94cc-f494-4294-96d9-79467c828b62 · outbound

This paper cites Megaagent: A large-scale autonomous llm-based multi-agent system without predefined sops.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Megaagent: A large-scale autonomous llm-based multi-agent system without predefined sops

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.862629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8842821f-91da-4602-bd4e-beef06232e7b · outbound

This paper cites Can AI automatically analyze public opinion? A LLM agents-based agentic pipeline for timely public opinion analysis.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Can AI automatically analyze public opinion? A LLM agents-based agentic pipeline for timely public opinion analysis

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.481805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fe346f48-4da1-46ed-9117-35be36366bf6 · outbound

This paper cites Large language models empowered agent-based modeling and simulation: A survey and perspectives.Humanities and Social Sciences Communications, 11(1):1–24.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Large language models empowered agent-based modeling and simulation: A survey and perspectives.Humanities and Social Sciences Communications, 11(1):1–24

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.814351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a8c521e4-b883-4591-90cd-f5ed6d352667 · outbound

This paper cites From persona to personalization: A survey on role-playing language agents.Transactions on Machine Learning Research.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench From persona to personalization: A survey on role-playing language agents.Transactions on Machine Learning Research

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.816420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 45c49806-32a5-4b74-b70b-635997ea5c0d · outbound

This paper cites Synthetic replacements for human survey data? the perils of large language models.Political Analysis, 32(4):401–416.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Synthetic replacements for human survey data? the perils of large language models.Political Analysis, 32(4):401–416

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.820599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e53e248b-09f9-40b9-bb40-3e35b739feba · outbound

This paper cites Social iqa: Commonsense reasoning about social interactions.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Social iqa: Commonsense reasoning about social interactions

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.826665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8cb3ad73-1102-4d5e-b226-b80e8d608fb8 · outbound

This paper cites Do llms have distinct and consistent personality? trait: Personality testset designed for llms with psychometrics.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Do llms have distinct and consistent personality? trait: Personality testset designed for llms with psychometrics

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.867962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 409fd761-6c8f-4d48-b20c-1ff5219ee580 · outbound

This paper cites Psychcounsel-bench: Evaluating the psychology intelligence of large language models.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Psychcounsel-bench: Evaluating the psychology intelligence of large language models

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.439053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c22d2e3d-4bad-4ffb-8d97-efb883e3d3ef · outbound

This paper cites Socialbench: Sociality evaluation of role-playing conversational agents.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Socialbench: Sociality evaluation of role-playing conversational agents

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.808469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4b666605-e9f3-4d96-bb49-97eac0473990 · outbound

This paper cites The greatest good benchmark: Measuring llms’ alignment with utilitarian moral dilemmas.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench The greatest good benchmark: Measuring llms’ alignment with utilitarian moral dilemmas

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.810336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 45470683-65b6-4738-9fd1-6dc8b02d51ba · outbound

This paper cites Social-r1: Enhancing social intelligence in LLMs through human-like reinforced reasoning.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Social-r1: Enhancing social intelligence in LLMs through human-like reinforced reasoning

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.804528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3eb5105e-3f69-4b94-8008-38b16aec4baa · outbound

This paper cites MotiveBench: How Far Are We From Human-Like Motivational Reasoning in Large Language Models?.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench MotiveBench: How Far Are We From Human-Like Motivational Reasoning in Large Language Models?

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.522229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation dc512d22-16f0-4a44-a59f-6499bb90115c · outbound

This paper cites Emobench: Evaluating the emotional intelligence of large language models.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Emobench: Evaluating the emotional intelligence of large language models

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.812222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d761669d-a7f3-482b-a0e8-b8b0b6c6f9eb · outbound

This paper cites Hssbench: Benchmarking humanities and social sciences ability for multimodal large language models.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Hssbench: Benchmarking humanities and social sciences ability for multimodal large language models

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.422500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 22a60ab0-48cb-4ca0-9ae7-1bd9d6d38506 · outbound

This paper cites Aligning {ai} with shared human values.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Aligning {ai} with shared human values

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.806446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation af9418ff-9aff-4d17-9db8-39d09e9f4fad · outbound

This paper cites Character-LLM: A Trainable Agent for Role-Playing.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Character-LLM: A Trainable Agent for Role-Playing

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.431988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4269aac6-c4e4-49ef-a8a0-af6890f6b971 · outbound

This paper cites Incharacter: Evaluating personality fidelity in role-playing agents through psychological interviews.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Incharacter: Evaluating personality fidelity in role-playing agents through psychological interviews

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.822608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation dc13344c-4c29-4c4b-a339-cd6897b8db6a · outbound

This paper cites PersonaGym: Evaluating persona agents and LLMs.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench PersonaGym: Evaluating persona agents and LLMs

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.830504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6580d000-6e5f-496f-a8c0-2d41e9248082 · outbound

This paper cites Klinkert, Steph Buongiorno, and Corey Clark.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Klinkert, Steph Buongiorno, and Corey Clark

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.925881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 05ed3609-cc0c-4e4f-81a8-995dab10acfa · outbound

This paper cites Human behavior atlas: Benchmarking unified psychological and social behavior understanding.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Human behavior atlas: Benchmarking unified psychological and social behavior understanding

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.544391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2a589372-e2ef-46b4-b1c7-86aee41d0fcf · outbound

This paper cites AgentSense: Benchmarking social intelligence of language agents through interactive scenarios.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench AgentSense: Benchmarking social intelligence of language agents through interactive scenarios

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.915231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1350a6ce-e1dd-44dd-9706-f9c86aaf23f9 · outbound

This paper cites an unresolved cited work.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Unresolved cited work

Reference 80

Resolution: unresolved; raw_fallback, observed 2026-05-20T15:33:25.912582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 22b6b6b3-ed20-4360-944a-304ccf36c819 · outbound

This paper cites Moral stories: Situated reasoning about norms, intents, actions, and their consequences.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Moral stories: Situated reasoning about norms, intents, actions, and their consequences

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.917522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 33500b69-f0f0-4fe2-95c6-b12a24663a43 · outbound

This paper cites Emotionqueen: A benchmark for evaluating empathy of large language models.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Emotionqueen: A benchmark for evaluating empathy of large language models

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.921825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e3140a78-ee74-4a64-bb1f-e221deeb8238 · outbound

This paper cites Interintent: Investigating social intelligence of llms via intention understanding in an interactive game context.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Interintent: Investigating social intelligence of llms via intention understanding in an interactive game context

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.910704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0346deb8-9dbc-4fc1-8a4e-abf40f9649ce · outbound

This paper cites Understanding social reasoning in language models with language models.Advances in Neural Information Processing Systems, 36:13518–13529.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Understanding social reasoning in language models with language models.Advances in Neural Information Processing Systems, 36:13518–13529

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.934652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6af01a38-915b-48bb-a45c-46613bb4b9b7 · outbound

This paper cites Cognitive bias in decision-making with llms.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Cognitive bias in decision-making with llms

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.892757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 52d1b9c7-355e-4209-ba9c-caaf7f51b1a7 · outbound

This paper cites Decision-making behavior evaluation framework for llms under uncertain context.Advances in neural information processing systems, 37:113360–113382.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Decision-making behavior evaluation framework for llms under uncertain context.Advances in neural information processing systems, 37:113360–113382

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.894872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0b9ffb7c-f634-4fab-a9eb-a9e4bb9b179c · outbound

This paper cites Tomato: Verbalizing the mental states of role-playing llms for benchmarking theory of mind.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Tomato: Verbalizing the mental states of role-playing llms for benchmarking theory of mind

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.903737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f7471e29-68d1-4cf3-9b1f-9d4fe575e12f · outbound

This paper cites Testing theory of mind in large language models and humans.Nature Human Behaviour, 8(7):1285–1295.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Testing theory of mind in large language models and humans.Nature Human Behaviour, 8(7):1285–1295

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.890745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 32371c9c-af36-45c3-8ccc-5ad09d3f2b52 · outbound

This paper cites Hi-tom: A benchmark for evaluating higher-order theory of mind reasoning in large language models.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Hi-tom: A benchmark for evaluating higher-order theory of mind reasoning in large language models

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.901770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8d9e0426-ad20-4220-b6a8-898dea4db83a · outbound

This paper cites Muma-tom: Multi-modal multi-agent theory of mind.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Muma-tom: Multi-modal multi-agent theory of mind

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.929948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7ea9f07a-4d0e-4f5b-84ba-b79bd2f3bbdd · outbound

This paper cites The mind in the machine: A survey of incorporating psychological theories in llms.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench The mind in the machine: A survey of incorporating psychological theories in llms

Reference 91

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.500975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 58d3cfdf-9476-45df-b0db-cd326e60d0cd · outbound

This paper cites Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective

Reference 92

Resolution: verified exact; arxiv_id, observed 2026-05-20T15:33:25.484748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b4c08b27-a6b2-4f9a-bebd-9d8f1d6633c9 · outbound

This paper cites Argyle, Ethan C.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Argyle, Ethan C

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.865087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ed0a8d77-0004-4ad2-acf2-23a60057c91b · outbound

This paper cites Doubleday.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Doubleday

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.870067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0a4d88f6-4581-49ff-a611-811a01528f84 · outbound

This paper cites Harvard University Press.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Harvard University Press

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T15:33:25.875915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a94b41e0-6830-4956-b661-9af1fe6b2d54 · outbound

This paper cites predict every pair as positive.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench predict every pair as positive

Reference 96

Resolution: malformed identifier; raw_fallback, observed 2026-05-20T15:33:25.851792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5e7a63e0-8295-45fb-884d-7fea5663720d · outbound

This paper cites Apple released a phone.

Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench Apple released a phone

Reference 97

Resolution: malformed identifier; raw_fallback, observed 2026-05-20T15:33:25.856228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers