Summary of Scienceagentbench: Toward Rigorous Assessment Of Language Agents For Data-driven Scientific Discovery, by Ziru Chen et al.
ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery
by Ziru Chen, Shijie Chen, Yuting Ning, Qianheng Zhang, Boshi Wang, Botao Yu, Yifei Li, Zeyi Liao, Chen Wei, Zitong Lu, Vishal Dey, Mingyi Xue, Frazier N. Baker, Benjamin Burns, Daniel Adu-Ampratwum, Xuhui Huang, Xia Ning, Song Gao, Yu Su, Huan Sun
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study aims to rigorously assess the capabilities of language language models (LLMs) in automating scientific discovery. Researchers developed a benchmark of 102 tasks from peer-reviewed publications across four disciplines, validated by nine subject matter experts. The benchmark includes evaluation metrics, execution results, and costs for each task, ensuring scientific authenticity and real-world relevance. Five LLMs were evaluated using three frameworks (direct prompting, OpenHands CodeAct, and self-debug), with the best-performing agent solving only 32.4% of tasks independently. The study highlights the limitations of current language agents in generating code for data-driven discovery, underscoring the need for further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a benchmark to test language models’ ability to automate scientific discovery. They took tasks from real papers and had experts check them to make sure they were accurate and meaningful. Then, they used different methods (like giving the model hints or letting it figure things out) with five different language models. The best one could only solve 32% of the tasks on its own. This shows that even the best language models aren’t good enough yet for automating scientific discovery. |
Keywords
» Artificial intelligence » Prompting
