In this paper, the authors address the challenge of evaluating and ranking large language models (LLMs) without access to ground truth data. They introduce a unique method that leverages triplets of models to determine rankings based on evaluations by each other. By analyzing different generative tasks like summarization, multiple-choice, and dialog, they demonstrate the effectiveness of their approach in recovering accurate rankings without reference data. The study highlights the potential for a low-resource mechanism for practical use in ranking LLMs.
The paper discusses the limitations of existing evaluation methods that rely on human responses or pre-defined metrics and benchmarks. It introduces a new perspective where LLMs evaluate each other to serve as proxies for human preferences. The proposed method aims to reduce the effort required for evaluation while providing reliable rankings for various tasks.
The experiments conducted on different tasks show promising results, with the proposed methods outperforming traditional approaches like most common answer (MCA). The study also delves into theoretical analyses regarding conditions for success and time complexity of the algorithms used.
Overall, this research offers a fresh insight into evaluating and ranking LLMs without ground truth data, paving the way for more efficient and reliable assessment methods in natural language processing.
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by Amit Dhurand... at arxiv.org 03-08-2024
https://arxiv.org/pdf/2402.14860.pdfDeeper Inquiries