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

• URL: http://arxiv.org/abs/2401.18070v2
• Date: Mon, 17 Jun 2024 15:08:05 GMT
• Title: Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?
• Authors: Andreas Opedal, Alessandro Stolfo, Haruki Shirakami, Ying Jiao, Ryan Cotterell, Bernhard Schölkopf, Abulhair Saparov, Mrinmaya Sachan,
• Abstract summary: We study the biases of large language models (LLMs) in relation to those known in children when solving arithmetic word problems. 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.
• Score: 140.9751389452011
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Reasoning with Large Language Models, a Survey [2.831296564800826]
  This paper reviews the rapidly expanding field of prompt-based reasoning with LLMs. Our taxonomy identifies different ways to generate, evaluate, and control multi-step reasoning. We find that self-improvement, self-reflection, and some meta abilities of the reasoning processes are possible through the judicious use of prompts.
  arXiv  Detail & Related papers  (2024-07-16T08:49:35Z)
• Eliciting Problem Specifications via Large Language Models [4.055489363682198]
  Large language models (LLMs) can be utilized to map a problem class into a semi-formal specification. A cognitive system can then use the problem-space specification to solve multiple instances of problems from the problem class.
  arXiv  Detail & Related papers  (2024-05-20T16:19:02Z)
• Exploring the Compositional Deficiency of Large Language Models in Mathematical Reasoning [30.40415945003794]
  We investigate the compositionality of large language models (LLMs) in mathematical reasoning. Since problems with logical flaws are quite rare in the real world, these represent unseen'' cases to LLMs. Our experiments show that while LLMs possess both components of requisite knowledge, they do not textbfspontaneously combine them to handle these novel cases.
  arXiv  Detail & Related papers  (2024-05-05T16:35:30Z)
• Distilling Algorithmic Reasoning from LLMs via Explaining Solution Programs [2.3020018305241337]
  Distilling explicit chain-of-thought reasoning paths has emerged as an effective method for improving the reasoning abilities of large language models. We propose a novel approach to distill reasoning abilities from LLMs by leveraging their capacity to explain solutions. Our experiments demonstrate that learning from explanations enables the Reasoner to more effectively guide program implementation by a Coder.
  arXiv  Detail & Related papers  (2024-04-11T22:19:50Z)
• Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word Problem [58.3723958800254]
  Large language models (LLMs) are highly effective in various natural language processing (NLP) tasks. They are susceptible to producing unreliable conjectures in ambiguous contexts called hallucination. This paper presents a new method for evaluating LLM hallucination in Question Answering (QA) based on the unanswerable math word problem (MWP)
  arXiv  Detail & Related papers  (2024-03-06T09:06:34Z)
• FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition [57.747888532651]
  Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
  arXiv  Detail & Related papers  (2024-02-29T21:05:37Z)
• Language Models can be Logical Solvers [99.40649402395725]
  We introduce LoGiPT, a novel language model that directly emulates the reasoning processes of logical solvers. LoGiPT is fine-tuned on a newly constructed instruction-tuning dataset derived from revealing and refining the invisible reasoning process of deductive solvers.
  arXiv  Detail & Related papers  (2023-11-10T16:23:50Z)
• Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning [4.376598435975689]
  We discuss the challenges associated with employing large language models to enhance students' mathematical problem-solving skills. LLMs can generate the wrong reasoning processes, and also exhibit difficulty in understanding the given questions' rationales when attempting to correct students' answers.
  arXiv  Detail & Related papers  (2023-10-20T16:05:35Z)
• Evaluating Language Models for Mathematics through Interactions [116.67206980096513]
  We introduce CheckMate, a prototype platform for humans to interact with and evaluate large language models (LLMs) We conduct a study with CheckMate to evaluate three language models (InstructGPT, ChatGPT, and GPT-4) as assistants in proving undergraduate-level mathematics. We derive a taxonomy of human behaviours and uncover that despite a generally positive correlation, there are notable instances of divergence between correctness and perceived helpfulness.
  arXiv  Detail & Related papers  (2023-06-02T17:12:25Z)
• PAL: Program-aided Language Models [112.94785609781503]
  We present Program-Aided Language models (PaL) to understand natural language problems. PaL offloads the solution step to a programmatic runtime such as a Python interpreter. We set new state-of-the-art results in all 12 benchmarks.
  arXiv  Detail & Related papers  (2022-11-18T18:56:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.