Summary of Hyperdpo: Conditioned One-shot Multi-objective Fine-tuning Framework, by Yinuo Ren et al.
HyperDPO: Conditioned One-Shot Multi-Objective Fine-Tuning Framework
by Yinuo Ren, Tesi Xiao, Michael Shavlovsky, Lexing Ying, Holakou Rahmanian
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Optimization and Control (math.OC)
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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 research paper proposes a novel approach called HyperDPO, which addresses the Multi-Objective Fine-Tuning (MOFT) problem in machine learning. MOFT involves fine-tuning an existing model with datasets labeled according to different objectives simultaneously. The HyperDPO framework extends the Direct Preference Optimization (DPO) technique, originally developed for efficient language model alignment with preference data, to accommodate MOFT settings. By substituting the Bradley-Terry-Luce model in DPO with the Plackett-Luce model, HyperDPO can handle a wide range of MOFT tasks that involve listwise ranking datasets. The framework enjoys an efficient one-shot training process for profiling the Pareto front of auxiliary objectives and offers post-training control over trade-offs. Additionally, the paper proposes a novel Hyper Prompt Tuning design that conveys continuous importance weight across objectives to transformer-based models without altering their architecture. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists have developed a new way to train machines using many different goals at once. This is called Multi-Objective Fine-Tuning, or MOFT for short. They came up with an approach called HyperDPO that can handle lots of different tasks and datasets all at the same time. It’s like having multiple puzzle pieces fit together in one go! Their method is efficient and allows for control over how much to prioritize each goal. The scientists tested it on some specific tasks, like ranking things in order, and language models, and showed that it works well. |
Keywords
* Artificial intelligence * Alignment * Fine tuning * Language model * Machine learning * One shot * Optimization * Prompt * Transformer
