English static mirror for SEO/GEO · AI-assisted translation · Read Chinese original
智柴网 中文原文

LLM-Driven Usefulness Judgment for Web Search Evaluation (arXiv 2504.14401)

Forum topic · 小凯 · 2026-07-05

Summary

This forum post indexes an April 2025 arXiv paper, 'LLM-Driven Usefulness Judgment for Web Search Evaluation' (arXiv:2504.14401), by Mouly Dewan, Jiqun Liu, Aditya Gautam, and Chirag Shah. The paper explores using large language models to judge the usefulness of web search results, moving beyond traditional relevance-based evaluation toward user-centered usefulness assessment. The forum entry provides metadata (title, authors, source link), a research background on information retrieval challenges such as scalability and user intent understanding, and situates the work within the search engine evaluation chapter of an awesome list. It also includes generic discussion of LLM-era search architectures (retrieval, reranking, generation), evaluation practices like LLM-as-judge and cross-validation with human assessment, an engineering checklist covering latency, cost, safety, and data quality, a glossary of IR terms (RAG, LTR, nDCG, agentic search), and cross-references to related resources on RAG evaluation, citation quality in AI search, and evaluation frameworks such as ARES. Readers are advised to consult the original PDF for exact experimental figures, since the post is largely a structured overview based on the abstract and public metadata rather than a detailed reproduction of the paper's results.

LLM-Driven Usefulness Judgment for Web Search Evaluation (arXiv 2504.14401)

Overview

This entry indexes an April 2025 arXiv paper on using large language models (LLMs) to perform usefulness judgments for web search evaluation.

| Field | Content | |-------|---------| | Title | LLM-Driven Usefulness Judgment for Web Search Evaluation | | Authors | Mouly Dewan, Jiqun Liu, Aditya Gautam, Chirag Shah | | Published | April 2025 (arXiv) | | Source | https://arxiv.org/abs/2504.14401 | | Type | Academic paper | | Section | Evaluation of Search Engines |

Context

The paper addresses long-standing challenges in information retrieval: efficiency, scalability, and understanding user intent. Traditional pipelines often treat retrieval, ranking, and generation as separate stages, which is increasingly mismatched with LLM-era expectations of natural language interaction, multi-hop reasoning, and up-to-date knowledge. The work examines whether LLMs can reliably judge the *usefulness* of search results — a user-centered criterion that goes beyond static topical relevance.

Key points from the forum discussion

  • Unified framing: The entry situates the paper within a broader framework covering retrievers, rerankers, planners, generators, and feedback mechanisms.
  • Evaluation practices: Offline metrics (nDCG, MRR, Recall@k) increasingly diverge from online user satisfaction; LLM-as-judge approaches should be cross-validated with human assessment.
  • Common benchmarks and baselines mentioned for this research area: MS MARCO, BEIR, Natural Questions; BM25, dense retrieval, cross-encoder rerankers, retrieval-free LLMs.
  • Open problems: evaluation trustworthiness, latency and cost, hallucination and safety, cross-lingual and multimodal extension.

    • Engineering checklist (from the post)

    | Check | Question | Suggestion | |-------|----------|------------| | Data | PII in training/index? Versioning? | Partitioned indexes, anonymization, rollback-capable embeddings | | Latency | p99 budget? Retrieval steps? | Cascades with early stopping, query caching, async reranking | | Quality | Do offline gains translate online? | Interleaving experiments, human audits, citation checks | | Safety | Poisoning/bias via open retrieval? | Source allowlists, adversarial detection, output filtering | | Cost | Token/GPU cost per query? | Small-model routing, distillation, hybrid sparse+dense |

    Glossary

  • IR: Information Retrieval
  • RAG: Retrieval-Augmented Generation
  • LTR: Learning to Rank
  • nDCG: Normalized Discounted Cumulative Gain
  • Agentic Search: modeling search as sequential decision-making with tool calls
  • Gen-IR: Generative Information Retrieval

    • Related entries

  • AI Search Has A Citation Problem (CJR, Mar 2025)
  • Evaluation of Retrieval-Augmented Generation: A Survey (arXiv 2405.07437)
  • A Dataset of Information-Seeking Questions and Answers Anchored in Research Articles (arXiv 2105.03011)
  • ARES: An Automated Evaluation Framework for RAG (arXiv 2311.09476)
  • Agent-X: Evaluating Deep Multimodal Reasoning in Vision-Centric Agentic Tasks (arXiv 2505.24876)
  • AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents (arXiv 2401.13178)

Notes

The forum post is largely a structured overview based on the abstract and public metadata. Quantitative results should be verified against the original PDF at https://arxiv.org/abs/2504.14401 before citation.

Tags

#information-retrieval#llm-evaluation#search-engines#usefulness-judgment#llm-as-judge#rag#arxiv#search-evaluation

This page is an English static mirror generated for search and AI citation. It may be a full translation or structured summary of the Chinese original. Canonical interactive discussion lives on the Chinese page: https://zhichai.net/topic/178208702