Constructing Domain-Specific Evaluation Sets for LLM-as-a-judge

• URL: http://arxiv.org/abs/2408.08808v3
• Date: Tue, 20 Aug 2024 02:32:58 GMT
• Title: Constructing Domain-Specific Evaluation Sets for LLM-as-a-judge
• Authors: Ravi Raju, Swayambhoo Jain, Bo Li, Jonathan Li, Urmish Thakker,
• Abstract summary: Large Language Models (LLMs) have revolutionized the landscape of machine learning, yet current benchmarks often fall short in capturing the diverse behavior of these models in real-world applications. Existing frameworks like Alpaca-Eval 2.0 LC citedubois2024lengthcontrolledalpacaevalsimpleway and Arena-Hard v0.1 citeli2024crowdsourced are limited by their focus on general-purpose queries and lack of diversity across domains such as law, medicine, and multilingual contexts. We introduce a novel data pipeline that curates, domain-specific evaluation sets tailored for LLM-as
• Score: 15.980606104936365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have revolutionized the landscape of machine learning, yet current benchmarks often fall short in capturing the diverse behavior of these models in real-world applications. A benchmark's usefulness is determined by its ability to clearly differentiate between models of varying capabilities (separability) and closely align with human preferences. Existing frameworks like Alpaca-Eval 2.0 LC \cite{dubois2024lengthcontrolledalpacaevalsimpleway} and Arena-Hard v0.1 \cite{li2024crowdsourced} are limited by their focus on general-purpose queries and lack of diversity across domains such as law, medicine, and multilingual contexts. In this paper, we address these limitations by introducing a novel data pipeline that curates diverse, domain-specific evaluation sets tailored for LLM-as-a-Judge frameworks. Our approach leverages a combination of manual curation, semi-supervised learning to generate clusters, and stratified sampling to ensure balanced representation across a wide range of domains and languages. The resulting evaluation set, which includes 1573 samples across 14 categories, demonstrates high separability (84\%) across ten top-ranked models, and agreement (84\%) with Chatbot Arena and (0.915) Spearman correlation. The agreement values are 9\% better than Arena Hard and 20\% better than AlpacaEval 2.0 LC, while the Spearman coefficient is 0.7 more than the next best benchmark, showcasing a significant improvement in the usefulness of the benchmark. We further provide an open-source evaluation tool that enables fine-grained analysis of model performance across user-defined categories, offering valuable insights for practitioners. This work contributes to the ongoing effort to enhance the transparency, diversity, and effectiveness of LLM evaluation methodologies.

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