Summary of Good Idea or Not, Representation Of Llm Could Tell, by Yi Xu et al.
Good Idea or Not, Representation of LLM Could Tell
by Yi Xu, Bo Xue, Shuqian Sheng, Cheng Deng, Jiaxin Ding, Zanwei Shen, Luoyi Fu, Xinbing Wang, Chenghu Zhou
First submitted to arxiv on: 7 Sep 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 paper proposes an innovative approach to evaluating the merit of scientific ideas by leveraging large language models. The authors define the problem of quantitative evaluation of ideas and curate a benchmark dataset from nearly 4,000 manuscript papers with full texts. They establish a framework for quantifying the value of ideas using representations in a specific layer of large language models, showing that scores predicted by their method are relatively consistent with those of humans. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps researchers evaluate scientific ideas more efficiently. It uses big language models to assess ideas and creates a large dataset to train these models. The authors show that the models can accurately predict how valuable an idea is. This could help scientists make better decisions about which ideas to pursue. |
