Paper Citation Record · LEDGER

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs

As of 10 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2506.12338.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.12338 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:56:28.736826Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

verified exact0
verified fuzzy21
unresolved12
parse uncertain0
malformed identifier3
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 21ba9eb5-2233-45d3-8e91-c93cf4d0b39a · outbound

This paper cites Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:29.143010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 05120fa0-2655-4e17-a68e-05a8e9a61ecf · outbound

This paper cites Let’s think step-by-step,.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Let’s think step-by-step,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:29.082680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation af13429f-059d-49d8-a3f9-fa7fe80e6f3d · outbound

This paper cites an unresolved cited work.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Unresolved cited work

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f612e256-e463-4b1e-a206-e2e4dea8d85b · outbound

This paper cites A” and “B.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs A” and “B

Reference 5

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Source-reported events for the cited work

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Observation 32437478-4e51-47a0-b678-6ea86376b1c4 · outbound

This paper cites an unresolved cited work.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Unresolved cited work

Reference 6

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Source-reported events for the cited work

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Observation 275b9153-de81-4398-af03-a92cc390d9fb · outbound

This paper cites In our study, we observed notable differences in how biases affect the outputs of different LLMs.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs In our study, we observed notable differences in how biases affect the outputs of different LLMs

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:29.012460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a9dd30c1-75e4-4136-85c1-805c4959bfff · outbound

This paper cites an unresolved cited work.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Unresolved cited work

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d2359240-7f7b-4755-8742-73a026870753 · outbound

This paper cites Let’s think step by step.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Let’s think step by step

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:29.062789Z

Source-reported events for the cited work

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Observation fc221f65-9e3a-4d1c-a125-227ce8fd52fc · outbound

This paper cites Wrong Answer in Bold; Attitude Change underlined.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Wrong Answer in Bold; Attitude Change underlined

Reference 10

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Source-reported events for the cited work

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Observation c1bf722c-02a3-4895-90b2-a0fa2098e37c · outbound

This paper cites Here, the presence of many wrong answers results in a marked accuracy reduction for Mistral by 19.13% and a less pronounced but still significant decline for Vicuna by 4.35%.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Here, the presence of many wrong answers results in a marked accuracy reduction for Mistral by 19.13% and a less pronounced but still significant decline for Vicuna by 4.35%

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 433772f5-21f7-47ac-8e04-acf3f39a2e10 · outbound

This paper cites unboxing.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs unboxing

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:29.002266Z

Source-reported events for the cited work

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Observation a0ef4ae1-6704-43e0-ac9f-22ef68bf7574 · outbound

This paper cites Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios,.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:28.991888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8b073b87-60f6-4416-be72-30e06662eba3 · outbound

This paper cites It has been accepted for inclusion in ICIS 2024 Proceedings by an authorized administrator of AIS Electronic Library (AISeL).

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs It has been accepted for inclusion in ICIS 2024 Proceedings by an authorized administrator of AIS Electronic Library (AISeL)

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:29.132800Z

Source-reported events for the cited work

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Observation 8114523c-7320-4206-b893-c8a73fa1b22c · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Trans- formers for Language Understanding,.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs BERT: Pre-training of Deep Bidirectional Trans- formers for Language Understanding,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:28.960528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2b27f864-86ec-4a66-aebb-6611a0b6dba2 · outbound

This paper cites Mistral 7B.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Mistral 7B

Reference 22

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Source-reported events for the cited work

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Observation 23d2646a-217e-416e-ae61-bd547cfa140c · outbound

This paper cites The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning

Reference 23

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Source-reported events for the cited work

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Observation 8f80db83-0f8c-4217-a0db-558bc75d6d0d · outbound

This paper cites Large Language Models are Zero-Shot Reasoners.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Large Language Models are Zero-Shot Reasoners

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T00:56:28.696572Z

Source-reported events for the cited work

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Observation c0f14032-2582-4710-866a-258ec89e9881 · outbound

This paper cites Revealing the Dark Secrets of BERT,.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Revealing the Dark Secrets of BERT,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:28.939481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ab7bca10-d190-43b3-898c-666e51b03614 · outbound

This paper cites Measuring Faithfulness in Chain-of-Thought Reasoning.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Measuring Faithfulness in Chain-of-Thought Reasoning

Reference 26

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Source-reported events for the cited work

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Observation b13ac098-5869-415e-a144-e174da339eaa · outbound

This paper cites Design guidelines for prompt engineering text-to-image generative models,.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Design guidelines for prompt engineering text-to-image generative models,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:28.928630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7ec2132b-9bcd-423d-a842-d3e89fa2d5b4 · outbound

This paper cites Faithful Chain-of-Thought Reasoning.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Faithful Chain-of-Thought Reasoning

Reference 28

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Source-reported events for the cited work

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Observation f1d06080-19e7-473c-9ae0-b0471b3a4a15 · outbound

This paper cites Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T00:56:28.714258Z

Source-reported events for the cited work

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Observation d98a5331-3eef-4cc2-acf5-7888602feb83 · outbound

This paper cites Automating Customer Service using LangChain: Building custom open-source GPT Chatbot for organizations.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Automating Customer Service using LangChain: Building custom open-source GPT Chatbot for organizations

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T00:56:28.717609Z

Source-reported events for the cited work

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Observation b8d0f0e8-6685-4570-86d7-e2a653973133 · outbound

This paper cites Addressing cognitive bias in medical language models.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Addressing cognitive bias in medical language models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T00:56:28.724113Z

Source-reported events for the cited work

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Observation b402e86b-db83-4ad0-9c13-800acd0c20be · outbound

This paper cites Neural Machine Translation of Rare Words with Subword Units,.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Neural Machine Translation of Rare Words with Subword Units,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:28.905580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 756f26ed-d064-4ff0-accf-adc3ec43bcf5 · outbound

This paper cites Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning,.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:28.894748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9b1519a8-56a3-4020-b959-71b8eab118d6 · outbound

This paper cites Welcome to the Era of ChatGPT et Al.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Welcome to the Era of ChatGPT et Al

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:28.883620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8b12ca14-5758-4178-adfd-81aac554c1fe · outbound

This paper cites Why Johnny can’t prompt: how non-AI experts try (and fail) to design LLM prompts,.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Why Johnny can’t prompt: how non-AI experts try (and fail) to design LLM prompts,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:28.872461Z

Source-reported events for the cited work

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Observation 21ae3321-b174-4e5d-a0fd-e0e70c6354df · outbound

This paper cites Attitudes as object–evaluation associations of varying strength,.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Attitudes as object–evaluation associations of varying strength,

Reference 2007

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:28.950143Z

Source-reported events for the cited work

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Observation 64a2a118-466c-40e9-a3de-60b57b7ea283 · outbound

This paper cites A” or “B.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs A” or “B

Reference 2016

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0bd19880-72a6-4be7-8e5c-df0ba7c917d3 · outbound

This paper cites What Does BERT Look at? An Analysis of BERT’s Attention,.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs What Does BERT Look at? An Analysis of BERT’s Attention,

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:28.970880Z

Source-reported events for the cited work

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Observation 7948ff5f-8ec6-41b1-bc65-42e1e268118c · outbound

This paper cites Language Models are Few-Shot Learners.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Language Models are Few-Shot Learners

Reference 2020

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Source-reported events for the cited work

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Observation ea547e70-a390-4709-8482-87ad6278cab4 · outbound

This paper cites FinQA: A Dataset of Numerical Reasoning over Financial Data,.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs FinQA: A Dataset of Numerical Reasoning over Financial Data,

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:28.981538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d0650620-459c-4fc6-8e53-b3d36fbbf652 · outbound

This paper cites 2021)) covering different domains, and conduct experiments on both closed-source LLMs (GPT-3.5 and GPT-4 (OpenAI et al.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs 2021)) covering different domains, and conduct experiments on both closed-source LLMs (GPT-3.5 and GPT-4 (OpenAI et al

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:29.102836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 48068ed1-ddf2-413c-9961-8b81ce9f0688 · outbound

This paper cites Santi Cazorla called for the screen.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs Santi Cazorla called for the screen

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:29.122862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 95fed698-3446-40da-aa5d-352e3e3a4c47 · outbound

This paper cites On measuring faithfulness or self-consistency of natural language explanations,.

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs On measuring faithfulness or self-consistency of natural language explanations,

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:56:28.916589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.