LLM should think and action as a human
Haun Leung, ZiNan Wang

TL;DR
This paper proposes a new thinking method for large language models that enhances reasoning and planning by simulating human-like thinking processes, improving multi-turn conversation quality and tool usage.
Contribution
It introduces a built-in chain of thought approach, combining supervised fine-tuning and reinforcement learning to improve LLM reasoning, planning, and multi-turn conversation handling.
Findings
Enhanced reasoning and planning abilities in LLMs.
Reduced errors in multi-turn conversations.
Improved tool call efficiency and response quality.
Abstract
It is popular lately to train large language models to be used as chat assistants, but in the conversation between the user and the chat assistant, there are prompts, require multi-turns between the chat assistant and the user. However, there are a number of issues with the multi-turns conversation: The response of the chat assistant is prone to errors and can't help users achieve their goals, and as the number of conversation turns increases, the probability of errors will also increase; It is difficult for chat assistant to generate responses with different processes based on actual needs for the same prompt; Chat assistant require the use of tools, but the current approach is not elegant and efficient, and the number of tool calls is limited. The main reason for these issues is that large language models don't have the thinking ability as a human, lack the reasoning ability and…
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Taxonomy
TopicsLegal Education and Practice Innovations
