Summary of Anchored Preference Optimization and Contrastive Revisions: Addressing Underspecification in Alignment, by Karel D’oosterlinck et al.
Anchored Preference Optimization and Contrastive Revisions: Addressing Underspecification in Alignment
by Karel D’Oosterlinck, Winnie Xu, Chris Develder, Thomas Demeester, Amanpreet Singh, Christopher Potts, Douwe Kiela, Shikib Mehri
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper investigates the alignment process of Large Language Models (LLMs) using contrastive alignment objectives and preference pair datasets. The authors identify that the interaction between the model, paired data, and objective can lead to subpar results. They propose two methods: Contrastive Learning from AI Revisions (CLAIR), which generates more contrastive preference pairs, and Anchored Preference Optimization (APO), a controllable and stable alignment objective. The paper demonstrates the effectiveness of these methods by aligning Llama-3-8B-Instruct using various datasets and objectives, achieving improved performance on MixEval-Hard scores that correlate with human judgments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at how Large Language Models are aligned to understand each other better. They find that the way we train models can affect their ability to work well together. The researchers develop two new methods: CLAIR, which makes better “preference pairs” for the model, and APO, a more controlled way of training the model. They test these methods by aligning different models using various datasets and objectives, finding that they improve performance on certain tests. |
Keywords
» Artificial intelligence » Alignment » Llama » Optimization
