Papers with CHARPEVAL

1 papers
CHARPEVAL: Benchmarking Large Language Models’ Contextual Reasoning in Knowledge-Grounded Dialogue (2025.findings-acl)

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Challenge: Existing benchmarks that evaluate the ability of Large Language Models (LLMs) to perform contextualized reasoning in knowledge-grounded dialogue scenarios are lacking.
Approach: They propose a benchmark to evaluate the ability of Large Language Models to perform contextualized reasoning in knowledge-grounded dialogue scenarios.
Outcome: The proposed benchmark shows that open-weight LLMs are ineffective at reasoning over discontinuous chunks of text across the input.

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