Paper Citation Record · LEDGER

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes

As of 11 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 1 inbound Pith citation observation for arXiv:2501.14294.

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

pith.paper-citation-record.v1
2501.14294 v3

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:18:19.245820Z

measured 64 of 64 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-08T10:04:09.646578Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T20:11:11.041047Z

Reference resolution

63 of 63 outbound references displayed

verified exact2
verified fuzzy27
unresolved34
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 358b17c0-9256-4b3b-b5fc-59c13ba489ff · outbound

This paper cites GPT-4 Technical Report.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes GPT-4 Technical Report

Reference 1

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Observation 016e5b01-db97-4c61-89bb-cbedecdb9321 · outbound

This paper cites Llama 3 model card.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Llama 3 model card

Reference 2

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Observation a35aca98-1a46-471b-98eb-debdda1ffe42 · outbound

This paper cites Out of one, many: Using language models to simulate human samples.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Out of one, many: Using language models to simulate human samples

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:18.972276Z

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Observation 22d86686-9ae3-4b9e-914d-5729775f10b7 · outbound

This paper cites A General Language Assistant as a Laboratory for Alignment.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes A General Language Assistant as a Laboratory for Alignment

Reference 4

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Observation 3d9b5d7a-0545-4ef6-939d-60f7d846c837 · outbound

This paper cites Errors in probabilistic reasoning and judgment biases.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Errors in probabilistic reasoning and judgment biases

Reference 5

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Observation f4863c87-0d9c-4a05-93f5-0ee9de971830 · outbound

This paper cites Using cognitive psychology to understand gpt-3.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Using cognitive psychology to understand gpt-3

Reference 6

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Observation 9b946549-addb-4f0a-9e32-4b42acdd528e · outbound

This paper cites Stereotyping norwegian salmon: An inventory of pitfalls in fairness benchmark datasets.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Stereotyping norwegian salmon: An inventory of pitfalls in fairness benchmark datasets

Reference 7

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Observation 73eb5e27-176e-43a7-bda9-650c7d31659b · outbound

This paper cites Man is to computer programmer as woman is to homemaker? debiasing word embeddings.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Man is to computer programmer as woman is to homemaker? debiasing word embeddings

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:20.045254Z

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Observation 88efea68-66ed-4590-9f89-3f8a5773c34d · outbound

This paper cites Stereotypes.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Stereotypes

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:20.028691Z

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Observation a8cfdd18-2cbb-471f-b8d3-a71f394ab4fd · outbound

This paper cites Race gaps in sat scores highlight inequality and hinder upward mobility, 2017.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Race gaps in sat scores highlight inequality and hinder upward mobility, 2017

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:20.013014Z

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Observation 8c733c93-889e-448a-ba20-90716bb2de80 · outbound

This paper cites Language models are few-shot learners.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Language models are few-shot learners

Reference 11

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Observation 0be33dbf-42ed-477d-95cc-7f4dd0d16d23 · outbound

This paper cites Theory-grounded measurement of us social stereotypes in english language models.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Theory-grounded measurement of us social stereotypes in english language models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:19.988035Z

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Observation 4dd4099a-ab07-46b4-80b5-ac5d058a40e5 · outbound

This paper cites Explore the Potential of LLMs in Misinformation Detection: An Empirical Study.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Explore the Potential of LLMs in Misinformation Detection: An Empirical Study

Reference 13

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Observation eff36ca5-813c-43b0-a687-48282571ab1c · outbound

This paper cites From pretraining data to language models to downstream tasks: Tracking the trails of political biases leading to unfair NLP models.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes From pretraining data to language models to downstream tasks: Tracking the trails of political biases leading to unfair NLP models

Reference 14

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Observation adc8ecd2-af1b-4483-8bf3-ba14449125dc · outbound

This paper cites Misinfo reaction frames: Reasoning about readers ' reactions to news headlines.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Misinfo reaction frames: Reasoning about readers ' reactions to news headlines

Reference 15

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Observation 9df76b31-0406-467b-a92a-f072dc880159 · outbound

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Unresolved cited work

Reference 16

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Observation 6bfb490b-80d9-430e-b81b-1cc57021624d · outbound

This paper cites The Capacity for Moral Self-Correction in Large Language Models.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes The Capacity for Moral Self-Correction in Large Language Models

Reference 17

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Observation 3734d7b2-68a2-4567-a437-134db9f758ad · outbound

This paper cites Moral foundations theory: The pragmatic validity of moral pluralism.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Moral foundations theory: The pragmatic validity of moral pluralism

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:19.959762Z

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Observation 4e3a688e-bbf4-4c4f-856f-1cc0cb9fc465 · outbound

This paper cites Bayes rule as a descriptive model: The representativeness heuristic.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Bayes rule as a descriptive model: The representativeness heuristic

Reference 19

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Observation 8d6b0889-af48-4f3c-95cb-8df17c3cffc3 · outbound

This paper cites The political ideology of conversational ai: Converging evidence on chatgpt’s pro-environmental, left-libertarian orientation.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes The political ideology of conversational ai: Converging evidence on chatgpt’s pro-environmental, left-libertarian orientation

Reference 20

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Observation dd92d9ea-ab5f-4a6a-bbe9-983dd36a6b02 · outbound

This paper cites Stereotypes.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Stereotypes

Reference 21

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Observation ee72749a-4b5f-426a-a77e-bbadcc65dd0a · outbound

This paper cites Examining the causal impact of first names on language models: The case of social commonsense reasoning.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Examining the causal impact of first names on language models: The case of social commonsense reasoning

Reference 22

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Observation 823b69c9-736e-45be-bb8f-dc27a748efc9 · outbound

This paper cites S tereo M ap: Quantifying the awareness of human-like stereotypes in large language models.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes S tereo M ap: Quantifying the awareness of human-like stereotypes in large language models

Reference 23

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This paper cites Communitylm: Probing partisan worldviews from language models.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Communitylm: Probing partisan worldviews from language models

Reference 24

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Observation 70673c19-97b9-42af-936d-fc5a9db9c1ac · outbound

This paper cites Definition and assessment of accuracy in social stereotypes.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Definition and assessment of accuracy in social stereotypes

Reference 25

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Observation 151b72f7-f443-4389-8474-2476f0ca1ecf · outbound

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Speech & language processing

Reference 26

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Observation f79b533d-d2d9-4472-9800-b94dc9fa69cb · outbound

This paper cites A perspective on judgment and choice: Mapping bounded rationality.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes A perspective on judgment and choice: Mapping bounded rationality

Reference 27

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This paper cites Subjective probability: A judgment of representativeness.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Subjective probability: A judgment of representativeness

Reference 28

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This paper cites On the psychology of prediction.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes On the psychology of prediction

Reference 29

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This paper cites Alignment of Language Agents.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Alignment of Language Agents

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This paper cites The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning

Reference 31

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Xml retrieval

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This paper cites Evaluating Cognitive Maps and Planning in Large Language Models with CogEval.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Evaluating Cognitive Maps and Planning in Large Language Models with CogEval

Reference 33

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Stereoset: Measuring stereotypical bias in pretrained language models

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Spontaneous discounting of availability in frequency judgment tasks

Reference 35

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Training language models to follow instructions with human feedback

Reference 36

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Asian american and pacific islander students: Equity and the achievement gap

Reference 37

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Identifying the Risks of LM Agents with an LM-Emulated Sandbox

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Whose opinions do language models reflect? 2023

Reference 39

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This paper cites The psychology of stereotyping.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes The psychology of stereotyping

Reference 40

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This paper cites Ease of retrieval as information: Another look at the availability heuristic.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Ease of retrieval as information: Another look at the availability heuristic

Reference 41

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Towards Understanding Sycophancy in Language Models

Reference 42

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes The Trickle-down Impact of Reward (In-)consistency on RLHF

Reference 43

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This paper cites Moral Mimicry: Large Language Models Produce Moral Rationalizations Tailored to Political Identity.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Moral Mimicry: Large Language Models Produce Moral Rationalizations Tailored to Political Identity

Reference 44

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF

Reference 45

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This paper cites Comparison of bayesian and regression approaches to the study of information processing in judgment.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Comparison of bayesian and regression approaches to the study of information processing in judgment

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:19.698300Z

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Observation 30cee1d2-ad68-4411-810c-89b2c9d8f46e · outbound

This paper cites ANES Time Series Cumulative Data File [dataset and documentation].

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes ANES Time Series Cumulative Data File [dataset and documentation]

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:19.683661Z

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Observation d8492034-720f-4c62-8614-cac037d82936 · outbound

This paper cites Deep alignment with country shrinks the moral gap between conservatives and liberals.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Deep alignment with country shrinks the moral gap between conservatives and liberals

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:19.670397Z

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Observation 22c3ab57-50d9-4c62-a258-892ef21ecab6 · outbound

This paper cites Evaluating and Mitigating Discrimination in Language Model Decisions.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Evaluating and Mitigating Discrimination in Language Model Decisions

Reference 49

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This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Gemini: A Family of Highly Capable Multimodal Models

Reference 50

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Observation 154cf5dd-aa4d-429e-92a2-f4f98a485489 · outbound

This paper cites Qwen2.5: A party of foundation models, September 2024.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Qwen2.5: A party of foundation models, September 2024

Reference 51

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 52

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Judgment under uncertainty: Heuristics and biases: Biases in judgments reveal some heuristics of thinking under uncertainty

Reference 53

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes liar, liar pants on fire

Reference 54

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Chain-of-thought prompting elicits reasoning in large language models

Reference 55

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Qwen2 Technical Report

Reference 56

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes From heuristic to analytic: Cognitively motivated strategies for coherent physical commonsense reasoning

Reference 57

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes LIMA: Less Is More for Alignment

Reference 58

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Position: AI Evaluation Should Learn from How We Test Humans

Reference 59

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes write newline

Reference 60

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes @esa (Ref

Reference 61

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Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Unresolved cited work

Reference 62

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Reference 63

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Pith citing papers

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Continuous Latent Diffusion Language Model cites this paper.

Continuous Latent Diffusion Language Model Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes

Reference 42

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