arXiv AI
PeopleSearchBench: Evaluating AI-Powered People Search Platforms with Criteria-Grounded Verification
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arXiv AI
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PeopleSearchBench: A Multi-Dimensional Benchmark for Evaluating AI-Powered People Search Platforms
arXiv:2603. 27476v2 Announce Type: replace Abstract: AI-powered people search platforms are increasingly used in recruiting, sales prospecting, and professional networking, yet no widely accepted benchmark exists for evaluating their performance.
Hugging Face Trending Papers
Sep 8
Q2D-Web: A Large-Scale Benchmark for Retrieval in Agentic RAG Systems
Q2D-Web is a new large‑scale benchmark for agentic Retrieval‑Augmented Generation (RAG) systems, featuring a 190 million‑document web corpus and 70 k machine‑reformulated search queries in ten languages. It supplies three sets of relevance judgments—agent citations, production rankings, and a combined set enriched with LLM‑based labels—to evaluate first‑stage retrievers. Experiments on 13 retrievers show consistent ranking across judgment sets but significant variation across domains, languages, and query types, and demonstrate that a carefully sampled sub‑corpus can approximate full‑corpus evaluation with minimal loss in Recall@1000.
arXiv AI
Jun 12
DailyReport: An Open-ended Benchmark for Evaluating Search Agents on Daily Search Tasks
arXiv:2606. 12871v1 Announce Type: new Abstract: Search Agents (SAs) typically leverage large language models (LLMs) to support complex information-seeking tasks by autonomously exploring web sources and synthesizing information into comprehensive responses.
arXiv AI
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Characterizing Web Search by Conversational LLM Agents: From Search Decisions and Strategies to Results and Responses
The study examines how conversational LLM agents—specifically ChatGPT, Claude, Grok, and DeepSeek—use Web search, combining real user interactions with controlled API experiments. It finds that agents differ in when they decide to search, how they craft queries, and which domains they favor, and that more frequent searching does not always improve answer quality. While most responses are grounded in search results, some claims are unsupported, raising attribution concerns.
arXiv Computation and Language
Oct 16
Beyond Relevance-Centric Retrieval: Rubric-Oriented Document Set Selection and Ranking
The paper introduces SetwiseEvalKit, a benchmark with nine dimensions and 28K rubrics for evaluating document sets, highlighting that current rerankers only achieve up to 45% coverage and struggle with cross‑document coordination. It proposes Rubric4Setwise, a training‑free method that transforms rubric criteria into selection signals, outperforming other methods in downstream generation tasks across both short‑form and long‑form scenarios. The study demonstrates the importance of evaluating and optimizing document sets as a whole rather than independently.
arXiv Machine Learning
Jun 5
Statistically Reliable LLM-Based Ranking Evaluation via Prediction-Powered Inference
arXiv:2606. 05308v1 Announce Type: new Abstract: With PRECISE, we extended Prediction-Powered Inference to produce bias-corrected estimates of ranking evaluation metrics by combining a small human-labeled set with a large LLM-judged set.