Summary of Enhancing the General Agent Capabilities Of Low-parameter Llms Through Tuning and Multi-branch Reasoning, by Qinhao Zhou et al.
Enhancing the General Agent Capabilities of Low-Parameter LLMs through Tuning and Multi-Branch Reasoning
by Qinhao Zhou, Zihan Zhang, Xiang Xiang, Ke Wang, Yuchuan Wu, Yongbin Li
First submitted to arxiv on: 29 Mar 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 This paper investigates the capabilities of open-source pre-trained Large Language Models (LLMs) when used as intelligent agents for complex problems. The study finds that while LLMs excel in various language-related tasks, their performance is significantly inferior to commercial models like ChatGPT and GPT-4 when dealing with real-world complexities. To enhance LLM agent capabilities, the authors propose methods involving constructing agent-specific data and fine-tuning the models, as well as designing prompts to activate reasoning abilities. They explore these strategies on the 7B and 13B models and demonstrate that supervised fine-tuning can reduce hallucination outputs and formatting errors in agent tasks. Additionally, techniques like multi-path reasoning and task decomposition can effectively decrease problem complexity and improve LLM performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well open-source language models do when used to help solve complex problems. These models are great at understanding and generating text, but they’re not very good at dealing with real-world complexities. The researchers want to make them better for this type of work, so they came up with some new methods. They tried out these methods on two different types of language models and found that one method, called fine-tuning, really helps reduce errors and makes the models do a better job. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Hallucination » Supervised
