Summary of Investigating on Rlhf Methodology, by Alexey Kutalev and Sergei Markoff
Investigating on RLHF methodology
by Alexey Kutalev, Sergei Markoff
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper investigates how to align Large Language Models (LLMs) with human preferences. To achieve this, researchers train a Preference Model that simulates human preferences and fine-tune LLMs using Reinforcement Learning. The study discusses the methods and details essential for achieving the best results and presents its experience with the Direct Preference Optimization method. As a contribution, the authors introduce an approach for collecting a preference dataset through perplexity filtering, making it easier and more cost-effective to create such datasets for specific LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make Large Language Models match what humans like. To do this, scientists train a model that acts like humans when choosing between things. They also try different ways to make large language models better using a technique called Reinforcement Learning. The study talks about the methods and steps they took to get good results and shows how another method works. As a result, the authors have found an easy way to collect data on what people prefer. |
Keywords
» Artificial intelligence » Optimization » Perplexity » Reinforcement learning
