A graph-theoretic approach to building reliable LLM judges for retrieval
The article discusses the challenges of evaluating retrieval systems without ground-truth labels, particularly in sensitive domains like healthcare and legal. It proposes using large language models (LLMs) as judges to assess relevance based on task-specific rubrics instead of traditional labeling methods. This approach aims to overcome the limitations of existing metrics that rely on pre-existing relevance judgments.
- ▪Evaluating retrieval systems often requires ground-truth labels, which can be difficult to obtain in sensitive domains.
- ▪Embedding models may not accurately reflect task-specific relevance, leading to potential misclassifications.
- ▪Using LLMs as judges allows for qualitative assessments based on custom rubrics, reducing the need for extensive labeling efforts.
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Evaluating Retrieval Without Ground TruthA graph-theoretic approach to building reliable LLM judges for retrieval and rankingWilliam Barber and Kshitij JainMay 29, 202611ShareRecently, we have been spending a significant amount of time optimizing semantic retrieval pipelines across retrieval-augmented generation (RAG), threat detection, code search, legal search and recommendation systems. We keep hitting the same wall: a lack of ground-truth labels.In threat detection, raw data can be highly sensitive and often cannot leave the customer’s environment, making external labeling a non-starter. In healthcare and legal, labeling needs domain experts, and privacy rules narrow the pool of experts you are allowed to use.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Hacker News (AI / LLM).
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