AgentTuning: Adjusting Language Models Through Multi-Agent Tasks
Researchers have open-sourced a project called AgentTuning on GitHub, which provides a new method for fine-tuning language models. AgentTuning trains and adjusts language models through interaction trajectories from multiple agent tasks to adapt to different tasks and scenarios. This method can enhance the performance and generalization ability of language models while reducing the workload of manual tuning. AgentTuning has been validated in various natural language processing tasks, including dialogue generation, question answering systems, and summarization.
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