Ranking Unraveled: Recipes for LLM Rankings in Head-to-Head AI Combat

• URL: http://arxiv.org/abs/2411.14483v1
• Date: Tue, 19 Nov 2024 20:16:26 GMT
• Title: Ranking Unraveled: Recipes for LLM Rankings in Head-to-Head AI Combat
• Authors: Roland Daynauth, Christopher Clarke, Krisztian Flautner, Lingjia Tang, Jason Mars,
• Abstract summary: Pairwise ranking has emerged as a new method for evaluating human preferences for large language models (LLM) We explore the effectiveness of ranking systems for head-to-head comparisons of LLMs. Our analysis uncovers key insights into the factors that affect ranking accuracy and efficiency.
• Score: 7.8905223445925055
• License:
• Abstract: Deciding which large language model (LLM) to use is a complex challenge. Pairwise ranking has emerged as a new method for evaluating human preferences for LLMs. This approach entails humans evaluating pairs of model outputs based on a predefined criterion. By collecting these comparisons, a ranking can be constructed using methods such as Elo. However, applying these algorithms as constructed in the context of LLM evaluation introduces several challenges. In this paper, we explore the effectiveness of ranking systems for head-to-head comparisons of LLMs. We formally define a set of fundamental principles for effective ranking and conduct a series of extensive evaluations on the robustness of several ranking algorithms in the context of LLMs. Our analysis uncovers key insights into the factors that affect ranking accuracy and efficiency, offering guidelines for selecting the most appropriate methods based on specific evaluation contexts and resource constraints.

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