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March 25, 2026Open MindOpen Access

A Suite of LMs Comprehend Puzzle Statements as Well or Better Than Humans

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Authors

SRSupantho RakshitJHJia HuKMKyle Mahowald

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Overview

Analysis shows language models perform similarly to humans on comprehension tasks, suggesting reevaluation of human performance.

Key Points

  • The aim is to reassess the comprehension abilities of large language models compared to humans, particularly on minimally complex statements.
  • Reexamination of previous claims on language comprehension
  • Analysis of log probabilities for Llama-2-70B
  • Comparison of grammaticality judgments between humans and language models
  • Evaluation of reasoning models under expert prompting
  • Language models demonstrate ceiling-level accuracy in comprehension tasks
  • Human performance was found to be overestimated
  • Lower-performing language models and humans struggle with inference-based queries
  • Grammaticality judgments from language models correlate with human judgments
  • Task design and evaluation choices may misrepresent LM capabilities

Cite This Study

Rakshit et al. (2026) studied this question.

synapsesocial.com/papers/69c37be2b34aaaeb1a67eadchttps://doi.org/10.1162/opmi.a.344
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A suite of LMs comprehend puzzle statements as well as humans2025 · 1 citations
  2. 2Can large language models be viewed as a cognitive model of human language? Not yet, regardless of reasoning capability and size2026
  3. 3Easy Problems That LLMs Get Wrong2024 · 2 citations
  4. 4Evaluating the language abilities of Large Language Models vs. humans: Three caveats2024 · 14 citations
  5. 5LLMs' Understanding of Natural Language Revealed2024 · 1 citations