Summary of Large Language Models As Evaluators For Scientific Synthesis, by Julia Evans et al.
Large Language Models as Evaluators for Scientific Synthesis
by Julia Evans, Jennifer D’Souza, Sören Auer
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT)
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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 Medium Difficulty summary: This study investigates the ability of Large Language Models (LLMs) like GPT-4 and Mistral to evaluate the quality of scientific summaries or syntheses. The researchers used a dataset of 100 research questions with their corresponding syntheses generated by GPT-4 from abstracts of five related papers, which were then compared to human quality ratings. The study examines both the closed-source GPT-4 and open-source Mistral model’s capacity to rate these summaries and provide reasons for their judgments. Preliminary results show that LLMs can offer logical explanations that somewhat align with the quality ratings, but a deeper statistical analysis reveals a weak correlation between LLM and human ratings, indicating potential and current limitations of LLMs in scientific synthesis evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This study looks at how well computer models called Large Language Models (LLMs) can judge the quality of summaries that explain complex research. The researchers tested two types of LLMs, GPT-4 and Mistral, by having them generate summaries from abstracts of scientific papers. They then compared these summaries to ratings given by human experts. The study shows that while LLMs can provide reasons for their judgments, there is not a strong connection between how well they rate something and how well humans rate it. This suggests that LLMs are not yet reliable in evaluating complex research summaries. |
Keywords
» Artificial intelligence » Gpt
