arXiv:2606.17634v1 Announce Type: new Abstract: Evaluating large language models (LLMs) is important for understanding their capabilities, comparing competing systems, and supporting the deployment of reliable models in practice. For open-ended tasks, pairwise evaluation has become a popular paradigm, in which two responses to the same prompt are compared and the resulting judgments are aggregated into an overall ranking. A central challenge of this paradigm is intransitivity: the induced comparison outcomes may fail to support any coherent global ranking. For example, one may observe cyclic p
The proliferation of LLMs and the increasing complexity of their outputs necessitate more robust and reliable evaluation methodologies.
Reliable LLM evaluation is critical for practical deployment, competitive analysis, and transparently understanding model capabilities, preventing deployment of potentially unreliable systems.
The proposed 'prompt perturbation' method addresses intransitivity in pairwise LLM evaluation, potentially leading to more coherent and trustworthy model rankings.
- · LLM developers
- · AI ethicists
- · Enterprises deploying LLMs
- · AI evaluation platforms
- · Developers relying on flawed evaluation methods
- · Benchmarking organizations with less rigorous approaches
Improved reliability and comparability of large language models across different applications and providers.
Accelerated development of more robust LLMs as evaluation methods provide clearer signals for improvement.
Enhanced trust in AI systems in critical public and private sector applications due to better validated performance.
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Read at arXiv cs.CL