Summary of Offline Training Of Language Model Agents with Functions As Learnable Weights, by Shaokun Zhang et al.
Offline Training of Language Model Agents with Functions as Learnable Weights
by Shaokun Zhang, Jieyu Zhang, Jiale Liu, Linxin Song, Chi Wang, Ranjay Krishna, Qingyun Wu
First submitted to arxiv on: 17 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 In this paper, researchers introduce a novel approach to training Large Language Models (LLMs) as agents without modifying their weights. By treating specialized functions as learnable parameters, they propose AgentOptimizer, an algorithm that updates these functions using the LLM as a guide. This paradigm is useful when modifying the LLM itself is not feasible. The authors demonstrate the effectiveness of this approach by training representative LLM agents on various downstream tasks and showcase significant performance improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create better robots that can do complex jobs with the help of powerful computer models. These models are like super smart friends who can learn new skills without changing their basic nature. The researchers came up with a new way to teach these models new tricks by adding special tools that they can use to solve problems. They tested this approach on different tasks and showed that it makes the models work better. |
