Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels
By Honglei Zhuang, Zhen Qin, Kai Hui, Junru Wu, Le Yan, Xuanhui Wang, Michael Bendersky. 2023
Subfields: Information Retrieval & Search, Large Language Models
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Citations in the corpus
- CoRNStack: High-Quality Contrastive Data for Better Code Retrieval and Reranking (2024, unknown): “Recently, listwise reranking approaches (Pradeep et al., 2023; Reddy et al., 2024) have gained popularity for their ability to score multiple passages simultaneously, as opposed to pointwise Zhuang et al. (2023a;c) or pairwise Qin et al. (2023) reranking, where scoring is performed in isolation.”
- Recent advances in text embedding: A Comprehensive Review of Top-Performing Methods on the MTEB Benchmark (2024, background): “• Relevance Classification (RC) [110]: RC ( q, p ) = LLM ( label | p, P RC ) , where P RC is a prompt with few-shot examples for grading the relevance of each query-passage pair.”