Summary of Can Large Language Model Summarizers Adapt to Diverse Scientific Communication Goals?, by Marcio Fonseca et al.
Can Large Language Model Summarizers Adapt to Diverse Scientific Communication Goals?
by Marcio Fonseca, Shay B. Cohen
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed research investigates the controllability of large language models (LLMs) in generating scientific summaries. By identifying key stylistic and content coverage factors, the study finds that non-fine-tuned LLMs outperform humans in certain summarization tasks, such as paper reviews, abstracts, and lay summaries. The work also explores the use of keyword-based classifier-free guidance (CFG) to improve controllability, achieving lexical overlap comparable to fine-tuned baselines on arXiv and PubMed datasets. However, the study reveals limitations of LLMs in generating long summaries or highly abstractive lay summaries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well large language models can create scientific summaries. The team finds that these models are really good at creating short summaries that sound like real humans wrote them. They also find a way to make the models better by giving them special instructions, called keyword-based classifier-free guidance. This helps the models match what experts have written. However, the study shows that these models can’t create very long summaries or ones that are super creative and easy to understand. |
Keywords
» Artificial intelligence » Summarization
