Summary of How to Leverage Demonstration Data in Alignment For Large Language Model? a Self-imitation Learning Perspective, by Teng Xiao et al.
How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective
by Teng Xiao, Mingxiao Li, Yige Yuan, Huaisheng Zhu, Chao Cui, Vasant G Honavar
First submitted to arxiv on: 14 Oct 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 This novel generalized self-imitation learning (GSIL) framework effectively aligns large language models with offline demonstration data by deriving a surrogate objective of imitation learning with density ratio estimates. GSIL eliminates the need for complex adversarial training, achieving lightweight and efficient fine-tuning for large language models. It also enables a unified view for alignment with demonstration data through a family of offline losses parameterized by convex functions. The framework is tested on various benchmarks, including coding (HuamnEval), mathematical reasoning (GSM8K), and instruction-following benchmark (MT-Bench), where GSIL consistently outperforms baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make large language models learn from examples without needing to see the same data again. It’s called generalized self-imitation learning, or GSIL for short. The idea is to use examples that are already available and don’t require any extra training. This helps the model learn faster and more efficiently. The paper shows that GSIL works well on different tasks like coding, math problems, and following instructions. |
Keywords
» Artificial intelligence » Alignment » Fine tuning
