Evaluating long AI research reports is hard because they are long, dense, and technical. Existing automated judges struggle with these reports. This paper introduces a new to test how well different AI models can rank such reports. It finds that general purpose AI models judge reports better than specialized , but even the best models still make mistakes when dealing with highly specialized or very recent information.
AI reward models struggle to judge research reports; language model judges outperform
The first benchmark for evaluating reward models on deep research reports shows that generative LLMs used in comparative evaluation outperform traditional reward models, though all methods still struggle with specialized knowledge and appropriate evaluation dimensions.
Top University
Shudong Liu · Yuting Zhong · Guanhua Chen · Xinyi Yang · Junnan Liu · Lidia S. Chao · +2 more
University of Macau · Monash University · Nanjing University
Research Digest··2 min read
Thread:Learned Reward Goodhart
Before you read this
Three ideas this paper builds onbenchmarkA standard set of tasks or data used to evaluate and compare the performance of AI systems.338 papers →deep researchA multi-step AI system that autonomously gathers, evaluates, and synthesizes evidence from diverse sources to produce comprehensive reports.24 papers →reward modelA learned scoring function that rates outputs to align generation, assess evidence, or optimize inference.7 papers →
Read it as
The authors introduce DeepResearch RewardBench, a benchmark to evaluate reward models on the task of assessing AI-generated research reports.
The authors collected research report candidates from multiple AI agents and tool-augmented LLMs on topics from existing datasets.
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