Summary of Predicting Vs. Acting: a Trade-off Between World Modeling & Agent Modeling, by Margaret Li et al.
Predicting vs. Acting: A Trade-off Between World Modeling & Agent Modeling
by Margaret Li, Weijia Shi, Artidoro Pagnoni, Peter West, Ari Holtzman
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 research paper investigates the limitations of Reinforcement Learning-based HuggingFace (RLHF) aligned Language Models (LMs) on next-token prediction tasks. Despite their success in benchmarks and long-form text generation, these models struggle with this fundamental task. The study highlights that RLHF LMs, designed to interact with humans, appear to lose their ability to predict what comes next in arbitrary documents, a core training objective of Base LMs. The authors analyze the challenges faced by RLHF LMs in this area and explore ways to improve their performance on next-token prediction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how well Language Models (LMs) can predict what comes next in a text. These models are really good at generating long pieces of text and doing well on tests, but they struggle with this simple task. As these models become more like conversation partners for humans, they seem to lose their ability to make educated guesses about what comes next in an arbitrary document. The researchers want to understand why this is happening and find ways to improve the models’ performance. |
Keywords
» Artificial intelligence » Reinforcement learning » Rlhf » Text generation » Token
