Relevance Context Learning generates explicit relevance narratives from judged examples to guide LLM assessors, outperforming zero-shot and standard in-context learning for IR relevance judgments.
(eds.) SIGIR ’98: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Re- trieval, August 24-28 1998, Melbourne, Australia
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MIRA is a new benchmark for multi-category integrated retrieval built from real queries on a social science platform, with LLM assistance for topic descriptions and relevance labeling across four item categories.
Synthetically formalizing information needs into topics with descriptions and narratives improves LLM relevance assessor agreement with humans and reduces over-labeling of relevant documents on TREC Deep Learning and Robust04.
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Hybrid Pooling with LLMs via Relevance Context Learning
Relevance Context Learning generates explicit relevance narratives from judged examples to guide LLM assessors, outperforming zero-shot and standard in-context learning for IR relevance judgments.
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MIRA: An LLM-Assisted Benchmark for Multi-Category Integrated Retrieval
MIRA is a new benchmark for multi-category integrated retrieval built from real queries on a social science platform, with LLM assistance for topic descriptions and relevance labeling across four item categories.
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Formalized Information Needs Improve Large-Language-Model Relevance Judgments
Synthetically formalizing information needs into topics with descriptions and narratives improves LLM relevance assessor agreement with humans and reduces over-labeling of relevant documents on TREC Deep Learning and Robust04.