Search Arena: Analyzing Search-Augmented LLMs
Authors: name: Mihran Miroyan, name: Tsung-Han Wu, name: Logan King, name: Tianle Li, name: Jiayi Pan, name: Xinyan Hu, name: Wei-Lin Chiang, name: Anastasios N. Angelopoulos, name: Trevor Darrell, name: Narges Norouzi, name: Joseph E. Gonzalez

Search-augmented language models combine web search with Large Language
Models (LLMs) to improve response groundedness and freshness. However,
analyzing these systems remains challenging: existing datasets are limited in
scale and narrow in scope, often constrained to static, single-turn,
fact-checking questions. In this work, we introduce Search Arena, a
crowd-sourced, large-scale, human-preference dataset of over 24,000 paired
multi-turn user interactions with search-augmented LLMs. The dataset spans
diverse intents and languages, and contains full system traces with around
12,000 human preference votes. Our analysis reveals that user preferences are
influenced by the number of citations, even when the cited content does not
directly support the attributed claims, uncovering a gap between perceived and
actual credibility. Furthermore, user preferences vary across cited sources,
revealing that community-driven platforms are generally preferred and static
encyclopedic sources are not always appropriate and reliable. To assess
performance across different settings, we conduct cross-arena analyses by
testing search-augmented LLMs in a general-purpose chat environment and
conventional LLMs in search-intensive settings. We find that web search does
not degrade and may even improve performance in non-search settings; however,
the quality in search settings is significantly affected if solely relying on
the model's parametric knowledge. We open-sourced the dataset to support future
research in this direction. Our dataset and code are available at:
https://github.com/lmarena/search-arena.