Summary of Deep Bayesian Active Learning For Preference Modeling in Large Language Models, by Luckeciano C. Melo et al.
Deep Bayesian Active Learning for Preference Modeling in Large Language Models
by Luckeciano C. Melo, Panagiotis Tigas, Alessandro Abate, Yarin Gal
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Machine Learning (stat.ML)
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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 Bayesian Active Learner for Preference Modeling (BAL-PM) addresses the challenge of selecting informative points for acquiring human feedback in Large Language Models (LLMs). By leveraging principled Bayesian active learning, BAL-PM reduces the cost of preference labeling and achieves better results than previous methods. Specifically, it acquires 33% to 68% fewer labels while outperforming other stochastic Bayesian acquisition policies on two popular human preference datasets. The approach targets high epistemic uncertainty points according to the preference model and maximizes the entropy of the acquired prompt distribution in the feature space spanned by the LLM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) have made great progress, but getting them to work well requires a lot of human feedback. Researchers are trying to find ways to get this feedback more efficiently. One way is called Bayesian Active Learning. It helps us decide which points to focus on when asking humans for help. This can make the process much faster and cheaper. The problem is that some previous attempts at using this method didn’t work as well as hoped. In this new approach, scientists have found a way to fix this issue by making sure we don’t waste time collecting unnecessary information. By doing so, they were able to get the same results with 33% to 68% fewer labels. |
Keywords
» Artificial intelligence » Active learning » Prompt
