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arXiv Computation and Language By Rui Sun, Peizhao Mei, Xiewei Cheng, Kaizhuo Chen, Taoran Li, Zhiyuan Yu, Varun Chandrasekaran

Same Answer, Different Reasons: Trace-Level Divergence in Multilingual Large Language Models

Read the original on arXiv Computation and Language →

The paper introduces a trace-level divergence framework for evaluating multilingual large language models, breaking down agreement into structural similarity, order consistency, and holistic similarity. Experiments across three reasoning benchmarks, seven languages, and six leading LLMs show that conventional holistic similarity metrics overstate agreement, with up to 67.3% of top-scoring examples lacking structural alignment. Human judgments align more closely with structural similarity, highlighting language-specific divergence and the need for trace-level evaluation in multilingual deployments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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