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Changle Qu

2 accepted papers

2025

From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions

ICLR 2025oral

Tool learning enables Large Language Models (LLMs) to interact with external environments by invoking tools, serving as an effective strategy to mitigate the limitations inherent in their pre-training data. In this process, tool documentation plays a crucial role by providing usage instructions for…

2025

Uplift-RAG: Uplift-Driven Knowledge Preference Alignment for Retrieval-Augmented Generation

EMNLP 2025

Retrieval-augmented generation (RAG) has proven effective in enhancing the knowledge coverage of large language models (LLMs) and mitigating hallucinations by incorporating external retrieved documents. However, documents deemed relevant by the retriever are not necessarily helpful for answer genera