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New arXiv papers explore domain-specific IR and probabilistic relevance decisions

By PulseAugur Editorial · [2 sources] ·

Two new research papers submitted to arXiv explore advancements in information retrieval (IR). The first paper discusses domain-specific IR, highlighting challenges like long documents and queries, and suggests that methodological advances in this area can transfer to open-domain web search. The second paper compares dense retrieval methods with probabilistic relevance decisions, evaluating BGE-M3, TypeSafe Jev Noul, and OpenAI Decisions Predicate on the BEIR SciFact benchmark, finding that Jev Noul achieved the highest recall and nDCG scores. AI

IMPACT These papers contribute to the ongoing research in improving information retrieval systems, with potential applications in specialized domains and semantic search.

RANK_REASON Two academic papers on information retrieval submitted to arXiv.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New arXiv papers explore domain-specific IR and probabilistic relevance decisions

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Two academic papers on information retrieval submitted to arXiv.
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COVERAGE [2]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Allan Hanbury ·

    Domain-specific information retrieval

    Information Retrieval (IR) is much more than ad hoc web search: a large portion of the information seeking activities takes place in specific domains, often addressing work-related tasks. Examples are lawyers finding relevant prior cases for a legal case and academic researchers …

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Cevat Şener ·

    Probabilistic Relevance Decisions for Semantic Retrieval

    Dense similarity supplies reusable corpus representations, while probabilistic relevance decisions offer query-dependent document scoring. We compare dense-only BGE-M3 with TypeSafe Jev Noul and OpenAI Decisions Predicate scores on the official BEIR SciFact test benchmark. The st…

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