Summary of Inference-time Language Model Alignment Via Integrated Value Guidance, by Zhixuan Liu et al.
Inference-Time Language Model Alignment via Integrated Value Guidance
by Zhixuan Liu, Zhanhui Zhou, Yuanfu Wang, Chao Yang, Yu Qiao
First submitted to arxiv on: 26 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 This paper introduces Integrated Value Guidance (IVG), a method to efficiently align large language models during inference time, without the need for direct fine-tuning. IVG uses implicit and explicit value functions to guide decoding at token and chunk-levels respectively, achieving better performance compared to traditional methods. The approach is demonstrated across various tasks, including sentiment generation, summarization, and instruction-following. For instance, in AlpacaEval 2.0, the model “Mistral-7B-Instruct-v0.2” shows a significant improvement in length-controlled win rates from 19.51% to 26.51%, while “Mixtral-8x7B-Instruct-v0.1” improves from 25.58% to 33.75% with Tulu guidance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps large language models work better with humans by making them more accurate and aligned. The authors developed a new way to make these models understand what we want, without needing to train them again. This approach is called Integrated Value Guidance (IVG). IVG uses special instructions to help the model make good choices when generating text. It works well for tasks like writing sentences that sound natural or summarizing long texts into shorter ones. Even a very big and complex language model can use IVG to do its job better. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Language model » Summarization » Token
