DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis

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Paper Summary

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AI Can't Write Related Work Yet: New Benchmark Shows Where Research Synthesis Systems Fall Short

This paper introduces DeepScholar-bench, a new benchmark designed to test AI systems on their ability to synthesize research, similar to writing the 'Related Work' section of a scientific paper. Results show current AI systems struggle with this task, especially when it comes to finding the most important information and verifying what they say. A proposed system called DeepScholar-base outperforms others, but still has lots of room to improve.

Possible Conflicts of Interest

The authors acknowledge support from several companies involved in AI research, including Google, Meta, and VMware.

Identified Weaknesses

Knowledge synthesis and verifiability are still significant challenges for current AI research systems

Current systems have a hard time picking out the most important info and also sometimes have trouble verifying what they write using citations.

Current systems aren't always great at determining what's actually relevant and important to cite.

This can lead to less-relevant work being included and truly important research getting missed.

Rating Explanation

This paper introduces a valuable benchmark for a challenging area of AI research. While the proposed DeepScholar-base model establishes a good baseline, the results highlight how much work remains to be done, making this a significant contribution.

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