From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models | Paper Citation Record

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This paper cites Constitutional politics/constitutional law.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Constitutional politics/constitutional law

Reference 1

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This paper cites Llama 3: Open research and development in large-scale language models.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Llama 3: Open research and development in large-scale language models

Reference 2

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This paper cites Mistral: Lightweight and ecient open-source language model.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Mistral: Lightweight and ecient open-source language model

Reference 3

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This paper cites lii legal information institute.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models lii legal information institute

Reference 4

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This paper cites Evaluating irrationality in large language models and open research questions.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Evaluating irrationality in large language models and open research questions

Reference 5

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This paper cites Claude 2: An ai assistant with a focus on safety and reliability.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Claude 2: An ai assistant with a focus on safety and reliability

Reference 6

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This paper cites The impact of cognitive biases on professionals’ decision- making: A review.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models The impact of cognitive biases on professionals’ decision- making: A review

Reference 7

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This paper cites Barbri Simulated MBE.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Barbri Simulated MBE

Reference 8

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This paper cites Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell

Reference 9

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This paper cites Pearson correla- tion coecient.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Pearson correla- tion coecient

Reference 10

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

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Using cognitive psychology to understand gpt-3

Reference 11

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Observation f89373f3-9fe0-4bbf-8f2c-0a4b6556879b · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models On the Opportunities and Risks of Foundation Models

Reference 12

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This paper cites Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, and Dario Amodei.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, and Dario Amodei

Reference 13

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This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 14

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This paper cites Understanding the relationship between rationality and intelligence: a latent-variable approach.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Understanding the relationship between rationality and intelligence: a latent-variable approach

Reference 15

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This paper cites On the Measure of Intelligence.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models On the Measure of Intelligence

Reference 16

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This paper cites Better than rational: Evolutionary psychology and the invisible hand.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Better than rational: Evolutionary psychology and the invisible hand

Reference 17

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This paper cites Lampinen, Stephanie C.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Lampinen, Stephanie C

Reference 18

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This paper cites Gemini 1: Advancing ai capabilities through competitive benchmarks.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Gemini 1: Advancing ai capabilities through competitive benchmarks

Reference 19

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This paper cites Towards A Rigorous Science of Interpretable Machine Learning.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Towards A Rigorous Science of Interpretable Machine Learning

Reference 20

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From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Unresolved cited work

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This paper cites meaning.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models meaning

Reference 22

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This paper cites Language models do hard arithmetic tasks easily and hardly do easy arithmetic tasks, 2024.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Language models do hard arithmetic tasks easily and hardly do easy arithmetic tasks, 2024

Reference 23

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This paper cites Fast and frugal heuristics: The tools of bounded rationality.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Fast and frugal heuristics: The tools of bounded rationality

Reference 24

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This paper cites A survey on large language models: Applications, challenges, limitations, and practical usage.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models A survey on large language models: Applications, challenges, limitations, and practical usage

Reference 25

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This paper cites Machine psychology: Toward understanding ai cognitive processes through human-centric frameworks.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Machine psychology: Toward understanding ai cognitive processes through human-centric frameworks

Reference 26

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From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Unresolved cited work

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This paper cites Criminal Law & Procedure.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Criminal Law & Procedure

Reference 28

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This paper cites The weirdest people in the world? Behavioral and Brain Sciences , 33(2-3):61–83, 2010.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models The weirdest people in the world? Behavioral and Brain Sciences , 33(2-3):61–83, 2010

Reference 29

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This paper cites Real property and real property security: The well-being of the law, 1965.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Real property and real property security: The well-being of the law, 1965

Reference 30

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From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Training Compute-Optimal Large Language Models

Reference 32

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From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Some observations on the law of evidence

Reference 33

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From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Thinking, Fast and Slow

Reference 34

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This paper cites Judgment under uncertainty: Heuristics and biases.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Judgment under uncertainty: Heuristics and biases

Reference 35

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This paper cites Scaling Laws for Neural Language Models.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Scaling Laws for Neural Language Models

Reference 36

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From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Building machines that learn and think like people

Reference 37

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This paper cites Holistic Evaluation of Language Models.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Holistic Evaluation of Language Models

Reference 38

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From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Cognitive psychology tasks to assess rationality in llms

Reference 39

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This paper cites (ir) rationality and cognitive biases in large language models.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models (ir) rationality and cognitive biases in large language models

Reference 40

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From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Artificial Intelligence, Values and Alignment

Reference 41

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This paper cites Rebooting AI: Building Articial Intelligence We Can Trust.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Rebooting AI: Building Articial Intelligence We Can Trust

Reference 42

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From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Unresolved cited work

Reference 43

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This paper cites Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Reference 44

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From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Articial Intelligence: A Guide to Human-Level Thinking

Reference 45

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This paper cites Measuring machine intelligence: A human-centric per- spective.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Measuring machine intelligence: A human-centric per- spective

Reference 46

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This paper cites Dual process theory of reasoning: Contemporary issues and developmental applications.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Dual process theory of reasoning: Contemporary issues and developmental applications

Reference 47

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This paper cites Conrmation bias: A ubiquitous phenomenon in many guises.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Conrmation bias: A ubiquitous phenomenon in many guises

Reference 48

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This paper cites Culture and systems of thought: holistic versus analytic cognition.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Culture and systems of thought: holistic versus analytic cognition

Reference 49

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This paper cites Bayesian Rationality: The Probabilistic Approach to Human Reasoning.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Bayesian Rationality: The Probabilistic Approach to Human Reasoning

Reference 50

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Observation 04f0e0fa-956e-4efc-babd-78e310c932c8 · outbound

This paper cites Gpt-3.5: A compact and ecient language model for diverse applications.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Gpt-3.5: A compact and ecient language model for diverse applications

Reference 51

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This paper cites Gpt-4 technical report.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Gpt-4 technical report

Reference 52

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From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Unresolved cited work

Reference 53

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This paper cites Training language models to follow instructions with human feedback.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Training language models to follow instructions with human feedback

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:53.699258Z

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Observation 910f6aa9-7632-4019-8e33-40f4898aebc9 · outbound

This paper cites Rational choice and the structure of the environment.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Rational choice and the structure of the environment

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:53.546970Z

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Observation 1a3bcb61-99c8-4e63-bd4b-e6d6413a4ba4 · outbound

This paper cites Models of bounded rationality: Empirically grounded economic reason, volume 3.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Models of bounded rationality: Empirically grounded economic reason, volume 3

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:53.435384Z

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Observation 9eaf7e23-f76c-4942-ad8e-594bd3975b2d · outbound

This paper cites Rationality and the Reective Mind.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Rationality and the Reective Mind

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:53.312868Z

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Observation 0d3756ff-38f3-4c04-a80b-1af1f55127ca · outbound

This paper cites Stanovich and Richard F.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Stanovich and Richard F

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:53.142962Z

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Observation f0fd36e5-2e5f-4ec5-8507-05840816cd00 · outbound

This paper cites The great rationality debate.Psychological Science, 13(1):94–99, 2002.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models The great rationality debate.Psychological Science, 13(1):94–99, 2002

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:53.006655Z

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Observation 72c040f2-e0a9-4a05-95a5-a8988b587cfc · outbound

This paper cites Ecological Ratio- nality: Intelligence in the World.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Ecological Ratio- nality: Intelligence in the World

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:52.861792Z

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Observation ab93ad0d-6860-4936-86df-ae9b082daa20 · outbound

This paper cites The psychological foundations of culture.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models The psychological foundations of culture

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:52.756112Z

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Observation c136ec9b-7798-4ad1-b1af-85d4b1d8c922 · outbound

This paper cites Judgment under uncertainty: Heuristics and biases.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Judgment under uncertainty: Heuristics and biases

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:52.588887Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 882994d3-1343-46c1-beab-62b81cbcf618 · outbound

This paper cites Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:52.469863Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 51da76ff-cd10-4542-8fd3-1b12cac3d4e5 · outbound

This paper cites an unresolved cited work.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Unresolved cited work

Reference 65

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Observation d4ff70e1-824f-4ea9-984a-5821bad161dc · outbound

This paper cites Emergent Abilities of Large Language Models.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Emergent Abilities of Large Language Models

Reference 66

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Observation fe1fae8e-414d-4fb4-a455-f7bbb589490e · outbound

This paper cites Latest version of chatgpt aces bar exam with score nearing 90th percentile.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Latest version of chatgpt aces bar exam with score nearing 90th percentile

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:52.246836Z

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Observation 9dfac648-fc75-4de5-9e0e-325755a60720 · outbound

This paper cites Ai in context: Task-specic reasoning and role adaptation.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Ai in context: Task-specic reasoning and role adaptation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:52.102702Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ffbafe6b-7d02-4dcf-a8d2-d9efd32b092b · outbound

This paper cites Explanation by progressive disclosure: An ai-assisted decision- making framework.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Explanation by progressive disclosure: An ai-assisted decision- making framework

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:51.964261Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6d811753-e940-44dc-ac31-015173e41e44 · outbound

This paper cites Language models exhibit human-like biases: A study on an- choring and framing eects.

From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Language models exhibit human-like biases: A study on an- choring and framing eects

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:51.795364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers