Papers by Zheyuan Xiao

2 papers
Do LLMs Catch Their Own Mistakes? A Comprehensive Benchmark for Reflective Tool Use LLMs (2026.findings-acl)

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Challenge: Existing benchmarks primarily evaluate planning and execution success, overlooking the self-reflective dimension of tool use.
Approach: They propose a benchmark to assess LLMs’ self-reflective reasoning in tool-augmented multi-turn dialogues.
Outcome: The proposed benchmark covers 10 domains with 88 distinct APIs and 968 annotated dialogues, systematically injecting diverse error types arising from both user and assistant behavior.
Explaining Length Bias in LLM-Based Preference Evaluations (2025.findings-emnlp)

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Challenge: a preference evaluation metric is often biased towards longer responses, revealing a reliability problem . a decomposition of the preference evaluation into two components is needed to understand this bias.
Approach: They propose to decompose the preference evaluation metric into two key components . the first component is length-dependent and related to trustworthiness .
Outcome: The proposed evaluation metric is based on two components: desirability and information mass.

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