Summary of Language Generation with Strictly Proper Scoring Rules, by Chenze Shao et al.
Language Generation with Strictly Proper Scoring Rules
by Chenze Shao, Fandong Meng, Yijin Liu, Jie Zhou
First submitted to arxiv on: 29 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The proposed strategy for adapting scoring rules to language generation allows for language modeling with any non-local scoring rules. Leveraging this approach, the authors train language generation models using two classic strictly proper scoring rules, the Brier score and the Spherical score, as alternatives to the logarithmic score. The results indicate that simply substituting the loss function, without adjusting other hyperparameters, can yield substantial improvements in model’s generation capabilities. This is achieved by training large language models (LLMs) such as LLaMA-7B and LLaMA-13B. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows a new way to improve language generation models. By using different scoring rules, the authors get better results without changing many other settings. They tested this on two big language models and found that it worked well. This is an important step forward in natural language processing research. |
Keywords
» Artificial intelligence » Llama » Loss function » Natural language processing
