Summary of Advancing Tool-augmented Large Language Models: Integrating Insights From Errors in Inference Trees, by Sijia Chen et al.
Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees
by Sijia Chen, Yibo Wang, Yi-Feng Wu, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, Lijun Zhang
First submitted to arxiv on: 11 Jun 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 proposes an inference trajectory optimization framework to improve the performance of large language models (LLMs) in complex tasks. Building on recent advancements in LLMs and their ability to interact with real-world APIs, the authors introduce a novel approach that leverages decision trees to reason more effectively. The proposed method, ToolPrefer-LLaMA (TP-LLaMA), fine-tunes the model using tool-usage expert trajectories and then updates its policy based on step-wise preference pairs from the tree of thought. Experimental results demonstrate that TP-LLaMA outperforms baselines across various test scenarios, exhibiting better generalization capabilities and reasoning efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves large language models (LLMs) by using decision trees to reason better. It’s like having a super smart tool that helps you solve complex problems. The authors made a new model called TP-LLaMA that does this really well. They tested it with lots of different scenarios and found that it works way better than other approaches. |
Keywords
» Artificial intelligence » Generalization » Inference » Llama » Optimization
