Summary of Auto-intent: Automated Intent Discovery and Self-exploration For Large Language Model Web Agents, by Jaekyeom Kim et al.
Auto-Intent: Automated Intent Discovery and Self-Exploration for Large Language Model Web Agents
by Jaekyeom Kim, Dong-Ki Kim, Lajanugen Logeswaran, Sungryull Sohn, Honglak Lee
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 introduces Auto-Intent, a method to adapt pre-trained large language models (LLMs) as agents for target domains without fine-tuning. It focuses on web navigation tasks and discovers underlying intents from demonstrations unsupervisedly in a compact form. The approach trains an intent predictor using the extracted intents and provides top-k probable predictions as hints to the LLM agent, improving decision-making capabilities. Auto-Intent substantially improves the performance of GPT-{3.5, 4} and Llama-3.1-{70B, 405B} agents on large-scale real-website navigation benchmarks from Mind2Web and online navigation tasks from WebArena. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper teaches a computer how to adapt its language skills for a specific task without needing extra training. It shows that by understanding the goals of the task, the computer can make better decisions. This helps with things like navigating websites or answering questions. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Llama
