Summary of Nearly Optimal Algorithms For Contextual Dueling Bandits From Adversarial Feedback, by Qiwei Di et al.
Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback
by Qiwei Di, Jiafan He, Quanquan Gu
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes an algorithm called Robust Contextual Dueling Bandits (RCDB) for learning from human feedback in generative models, specifically large language models. The algorithm is designed to handle adversarial feedback, where an adversary may intentionally provide misleading preferences to manipulate the model’s output. RCDB uses uncertainty-weighted maximum likelihood estimation and achieves a regret bound of O(d/+dC/), which is nearly optimal in scenarios with and without adversarial feedback. The paper also develops a novel algorithm for estimating the link function’s derivative, eliminating the exponential dependence on the parameter radius B to a polynomial dependence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies how machines learn from human feedback, which is important for making language models better. However, this process can be tricked by bad actors who try to make the model produce unwanted results. The researchers created an algorithm called RCDB that helps protect against these attacks and makes sure the model learns in a fair way. This is useful because it means we can trust the output of language models more. |
Keywords
» Artificial intelligence » Likelihood
