Summary of Lipo: Listwise Preference Optimization Through Learning-to-rank, by Tianqi Liu et al.
LiPO: Listwise Preference Optimization through Learning-to-Rank
by Tianqi Liu, Zhen Qin, Junru Wu, Jiaming Shen, Misha Khalman, Rishabh Joshi, Yao Zhao, Mohammad Saleh, Simon Baumgartner, Jialu Liu, Peter J. Liu, Xuanhui Wang
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 introduces the LiPO framework, a novel approach to aligning language models (LMs) with human feedback. The study focuses on directly fitting LMs to a ranked list of responses given a prompt, leveraging the concept of Learning-to-Rank (LTR). The authors formulate the LM alignment as a listwise ranking problem and describe the LiPO-method, which outperforms existing DPO and SLiC methods in several preference alignment tasks. By drawing an explicit connection to LTR, the researchers demonstrate that LiPO-can effectively learn from a ranked list of plausible responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us understand how we can teach language models to behave well in real-life situations by giving them feedback on their answers. The study proposes a new approach called LiPO that uses a ranking system to align the model’s behavior with human preferences. This means that instead of just judging individual answers, humans provide rankings for multiple responses given a prompt. The researchers show that this approach can improve the model’s performance compared to existing methods. |
Keywords
* Artificial intelligence * Alignment * Prompt
