LLM Spark: Critical Thinking Evaluation of Large Language Models

ICLR 2025 Conference Submission12877 Authors

Keywords: critical thinking, llm, problem-solving, benchmarks

TL;DR: Evaluation of Critical Thinking Ability of LLMs

Abstract:

Large language models (LLMs) excel in complex tasks but often struggle with
inconsistencies in problem framing, a critical skill for real-world scenarios. This
paper introduces SPARK, a novel evaluation framework grounded in the Hierar-
chical Three-Space Theory, to assess LLMs’ ability to identify missing informa-
tion and challenge flawed problem setups. We propose a general framework to
create benchmarks by introducing inconsistencies and misleading cues in diverse
question-answering datasets, covering mathematics, science, and reading compre-
hension. To assist with robust measuring of critical thinking, we employ two key
metrics: problem-solving capability rate and challenge rate. Our experiments with
state-of-the-art LLMs reveal their limitations in critical thinking, particularly in
recognizing inconsistencies. We also explore mitigation strategies, such as modi-
fied prompting and targeted fine-tuning. Furthermore, we conduct comprehensive
experiments to investigate how model and problem properties influence critical
thinking capabilities in LLMs.

Primary Area: datasets and benchmarks

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Submission Number: 12877