Summary of Todo: Enhancing Llm Alignment with Ternary Preferences, by Yuxiang Guo et al.
TODO: Enhancing LLM Alignment with Ternary Preferences
by Yuxiang Guo, Lu Yin, Bo Jiang, Jiaqi Zhang
First submitted to arxiv on: 2 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 Tie-rank Oriented Bradley-Terry model (TOBT) is an extension of the traditional binary Bradley-Terry model that captures human preferences more effectively, even in the presence of noisy or inconsistent labels and frequent ties. The TOBT model explicitly incorporates ties, enabling more nuanced preference representation. Building on this, the Tie-rank Oriented Direct Preference Optimization (TODO) algorithm leverages TOBT’s ternary ranking system to improve preference alignment. TODO consistently outperforms Direct Preference Optimization in modeling preferences across various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models need to be aligned with human intent to perform well. The usual way of doing this, called Direct Preference Optimization, uses a simple model that can’t handle complex preferences. Our new approach, called Tie-rank Oriented Bradley-Terry, is better because it considers ties and noise in labels. We then use this model to create an algorithm called Tie-rank Oriented Direct Preference Optimization (TODO). TODO works well on many datasets and is even good at aligning binary preferences. |
Keywords
» Artificial intelligence » Alignment » Optimization
