![[2602.13543] LiveNewsBench: Evaluating LLM Web Search Capabilities with Freshly Curated News](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.13543] LiveNewsBench: Evaluating LLM Web Search Capabilities with Freshly Curated News
Summary
The paper introduces LiveNewsBench, a benchmark for evaluating the web search capabilities of Large Language Models (LLMs) using freshly curated news data, focusing on real-time information access and complex fact retrieval.
Why It Matters
As LLMs increasingly integrate web search capabilities, establishing a reliable evaluation framework is crucial for assessing their performance in real-world applications. LiveNewsBench addresses the challenge of evaluating LLMs by providing a regularly updated benchmark that tests their ability to retrieve current information, which is vital for applications in news and information retrieval.
Key Takeaways
- LiveNewsBench is designed to evaluate LLMs' web search capabilities with fresh news data.
- It generates challenging question-answer pairs that require multi-hop reasoning and external information.
- The benchmark includes human-verified samples to ensure reliable evaluation.
- It supports the creation of a large-scale training dataset for agentic web search models.
- The leaderboard and datasets are publicly available, promoting transparency and collaboration in research.
Computer Science > Information Retrieval arXiv:2602.13543 (cs) [Submitted on 14 Feb 2026] Title:LiveNewsBench: Evaluating LLM Web Search Capabilities with Freshly Curated News Authors:Yunfan Zhang, Kathleen McKeown, Smaranda Muresan View a PDF of the paper titled LiveNewsBench: Evaluating LLM Web Search Capabilities with Freshly Curated News, by Yunfan Zhang and 2 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) with agentic web search capabilities show strong potential for tasks requiring real-time information access and complex fact retrieval, yet evaluating such systems remains challenging. We introduce \bench, a rigorous and regularly updated benchmark designed to assess the agentic web search abilities of LLMs. \bench automatically generates fresh question-answer pairs from recent news articles, ensuring that questions require information beyond an LLM's training data and enabling clear separation between internal knowledge and search capability. The benchmark features intentionally difficult questions requiring multi-hop search queries, page visits, and reasoning, making it well-suited for evaluating agentic search behavior. Our automated data curation and question generation pipeline enables frequent benchmark updates and supports construction of a large-scale training dataset for agentic web search models, addressing the scarcity of such data in the research community. To ensure reliable evaluation, we include a subset of human-verif...
