April 2, 2024Open Access

Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey

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Large language models rely heavily on surface-level patterns and correlations in training data rather than demonstrating genuine reasoning behavior during complex tasks.
Comprehensive review of evaluation methodologies examines behavioral analyses beyond standard task accuracy to uncover the underlying reasoning processes in models.
Highlights the necessity of establishing behavioral benchmarks that differentiate genuine reasoning processes from heuristic pattern matching in artificial systems.

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Implication

Survey reveals reliance on surface-level patterns in large language models, highlighting the need to evaluate reasoning behavior beyond shallow accuracy.

Cite This Study

Mondorf et al. (2024) studied this question.

synapsesocial.com/papers/68e70b2bb6db64358768478f https://doi.org/10.48550/arxiv.2404.01869

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Implication

Survey reveals reliance on surface-level patterns in large language models, highlighting the need to evaluate reasoning behavior beyond shallow accuracy.

Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey

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Implication

Cite This Study

Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey

Key Points

Discussion

Implication

Cite This Study

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