Summary of Anchored Alignment For Self-explanations Enhancement, by Luis Felipe Villa-arenas et al.
Anchored Alignment for Self-Explanations Enhancement
by Luis Felipe Villa-Arenas, Ata Nizamoglu, Qianli Wang, Sebastian Möller, Vera Schmitt
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 A novel methodology for alignment is introduced to improve large language models’ ability to articulate their reasoning without annotated rationale explanations. The approach consists of three components: explanation quality assessment, self-instruction dataset generation, and model alignment. A novel technique called Alignment with Anchor Preference Pairs enhances the selection of preference pairs by categorizing model outputs into consistently correct, consistently incorrect, and variable categories. This approach significantly improves explanation quality while maintaining accuracy compared to other fine-tuning strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps large language models explain their answers better. It has three parts: checking how good explanations are, making a dataset for self-instruction, and aligning the model. The team also developed a new way to choose preference pairs that improves the Direct Preference Optimization (DPO) method. By using this approach, the model can give better explanations without sacrificing its accuracy. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Optimization
