# Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?

arXiv: https://arxiv.org/abs/2401.18070 · HTML: https://mlsanity.com/paper/2401.18070/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 69 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Builds on (most cited first, 27 of 27)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Are NLP Models really able to Solve Simple Math Word Problems?](https://mlsanity.com/paper/2103.07191/) (2021)
- [Progress measures for grokking via mechanistic interpretability](https://mlsanity.com/paper/2301.05217/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [Faith and Fate: Limits of Transformers on Compositionality](https://mlsanity.com/paper/2305.18654/) (2023)
- [Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus](https://mlsanity.com/paper/2104.08758/) (2021)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought](https://mlsanity.com/paper/2210.01240/) (2022)
- [A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers](https://mlsanity.com/paper/2106.15772/) (2021)
- [On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning](https://mlsanity.com/paper/2212.08061/) (2022)
- [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://mlsanity.com/paper/2109.00725/) (2021)
- [Can Large Language Models Infer Causation from Correlation?](https://mlsanity.com/paper/2306.05836/) (2023)
- [MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems](https://mlsanity.com/paper/2305.14536/) (2023)
- [What's In My Big Data?](https://mlsanity.com/paper/2310.20707/) (2023)
- [What Artificial Neural Networks Can Tell Us About Human Language Acquisition](https://mlsanity.com/paper/2208.07998/) (2022)
- [Capturing Failures of Large Language Models via Human Cognitive Biases](https://mlsanity.com/paper/2202.12299/) (2022)
- [A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models](https://mlsanity.com/paper/2210.12023/) (2022)
- [Positional Description Matters for Transformers Arithmetic](https://mlsanity.com/paper/2311.14737/) (2023)
- [Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods](https://mlsanity.com/paper/2305.18156/) (2023)
- [A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models](https://mlsanity.com/paper/2311.00445/) (2023)
- [Evaluating Large Language Models with NeuBAROCO: Syllogistic Reasoning Ability and Human-like Biases](https://mlsanity.com/paper/2306.12567/) (2023)
- [Beyond English: Evaluating LLMs for Arabic Grammatical Error Correction](https://mlsanity.com/paper/2312.08400/) (2023)
- [World Models for Math Story Problems](https://mlsanity.com/paper/2306.04347/) (2023)
- [A Theme-Rewriting Approach for Generating Algebra Word Problems](https://mlsanity.com/paper/1610.06210/) (2016)
- [Large Language Models for In-Context Student Modeling: Synthesizing Student's Behavior in Visual Programming](https://mlsanity.com/paper/2310.10690/) (2023)

## Built on by (most cited first, 19 of 19)

- [Interpreting and Improving Large Language Models in Arithmetic Calculation](https://mlsanity.com/paper/2409.01659/) (2024)
- [Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors](https://mlsanity.com/paper/2407.09136/) (2024)
- [Can LLMs Understand Time Series Anomalies?](https://mlsanity.com/paper/2410.05440/) (2024)
- [A Comprehensive Evaluation of Cognitive Biases in LLMs](https://mlsanity.com/paper/2410.15413/) (2024)
- [Interpreting Arithmetic Mechanism in Large Language Models through Comparative Neuron Analysis](https://mlsanity.com/paper/2409.14144/) (2024)
- [Cognitive Biases in Large Language Models: A Survey and Mitigation Experiments](https://mlsanity.com/paper/2412.00323/) (2024)
- [Probing for Arithmetic Errors in Language Models](https://mlsanity.com/paper/2507.12379/) (2025)
- [Evaluating and Optimizing Educational Content with Large Language Model Judgments](https://mlsanity.com/paper/2403.02795/) (2024)
- [MathGAP: Out-of-Distribution Evaluation on Problems with Arbitrarily Complex Proofs](https://mlsanity.com/paper/2410.13502/) (2024)
- [CBEval: A framework for evaluating and interpreting cognitive biases in LLMs](https://mlsanity.com/paper/2412.03605/) (2024)
- [Bias Beware: The Impact of Cognitive Biases on LLM-Driven Product Recommendations](https://mlsanity.com/paper/2502.01349/) (2025)
- [Can LLMs Reason Abstractly Over Math Word Problems Without CoT? Disentangling Abstract Formulation From Arithmetic Computation](https://mlsanity.com/paper/2505.23701/) (2025)
- [Self-Adaptive Cognitive Debiasing for Large Language Models in Decision-Making](https://mlsanity.com/paper/2504.04141/) (2025)
- [Models Can and Should Embrace the Communicative Nature of Human-Generated Math](https://mlsanity.com/paper/2409.17005/) (2024)
- [Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize?](https://mlsanity.com/paper/2507.11423/) (2025)
- [Do Large Language Models Show Biases in Causal Learning? Insights from Contingency Judgment](https://mlsanity.com/paper/2510.13985/) (2025)
- [People will agree what I think: Investigating LLM's False Consensus Effect](https://mlsanity.com/paper/2407.12007/) (2024)
- [Do LLM Agents Mirror Socio-Cognitive Effects in Power-Asymmetric Conversations?](https://mlsanity.com/paper/2605.17694/) (2026)
- [Learning to Reason Efficiently with A* Post-Training](https://mlsanity.com/paper/2605.24597/) (2026)

## Abstract (arXiv)

There is increasing interest in employing large language models (LLMs) as cognitive models. For such purposes, it is central to understand which properties of human cognition are well-modeled by LLMs, and which are not. In this work, we study the biases of LLMs in relation to those known in children when solving arithmetic word problems. Surveying the learning science literature, we posit that the problem-solving process can be split into three distinct steps: text comprehension, solution planning and solution execution. We construct tests for each one in order to understand whether current LLMs display the same cognitive biases as children in these steps. We generate a novel set of word problems for each of these tests, using a neuro-symbolic approach that enables fine-grained control over the problem features. We find evidence that LLMs, with and without instruction-tuning, exhibit human-like biases in both the text-comprehension and the solution-planning steps of the solving process, but not in the final step, in which the arithmetic expressions are executed to obtain the answer.