Summary of Large Language Models Can Self-improve at Web Agent Tasks, by Ajay Patel et al.
Large Language Models Can Self-Improve At Web Agent Tasks
by Ajay Patel, Markus Hofmarcher, Claudiu Leoveanu-Condrei, Marius-Constantin Dinu, Chris Callison-Burch, Sepp Hochreiter
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 explores the potential of large language models (LLMs) to navigate complex environments as agents. Specifically, it investigates whether LLMs can improve their performance through self-improvement by fine-tuning on data generated by themselves. The study focuses on the WebArena benchmark, where an agent must autonomously navigate and perform actions on web pages to achieve a specified objective. By fine-tuning on three distinct synthetic training data mixtures, the authors achieve a 31% improvement in task completion rate over the base model. To assess performance, robustness, capabilities, and quality of trajectories, novel evaluation metrics are proposed. This work demonstrates the potential for LLMs to self-improve in long-horizon tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine training machines to make decisions on their own by giving them instructions. That’s what this paper is about! Researchers want to know if these language models can get better at making decisions by teaching themselves from the data they generate. They used a special test called WebArena, where an AI has to navigate through web pages and perform tasks. By adjusting the model’s training data in different ways, they found that it got 31% better at completing tasks. To measure how well it did, they came up with new ways to evaluate its performance. |
Keywords
» Artificial intelligence » Fine tuning
