WeiminXiong/IPR
Watch Every Step! LLM Agent Learning via Iterative Step-level Process Refinement (EMNLP 2024 Main Conference)
This project helps researchers and developers train Large Language Model (LLM) agents to perform complex, multi-step tasks more effectively. It takes expert demonstrations of how to solve a problem and uses an iterative refinement process to teach the LLM agent to follow each step accurately. The output is a more capable LLM agent ready for deployment in environments like online shopping or text-based games.
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Use this if you are developing or fine-tuning LLM agents for tasks that require precise, sequential decision-making, such as automated online shopping or interactive problem-solving in text-based environments.
Not ideal if you are looking for an off-the-shelf LLM agent for direct end-user application without any training or development work.
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Python
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Last pushed
Oct 18, 2024
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