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cs.IR2024★ 1 cited
Intermediate Distillation: Data-Efficient Distillation from Black-Box LLMs for Information Retrieval
Zizhong Li, Haopeng Zhang, Jiawei Zhang
Recent research has explored distilling knowledge from large language models (LLMs) to optimize retriever models, especially within the retrieval-augmented generation (RAG) framewo…
cs.CL2024
GPTA: Generative Prompt Tuning Assistant for Synergistic Downstream Neural Network Enhancement with LLMs
Xiao Liu, Jiawei Zhang
This study introduces GPTA, a Large Language Model assistance training framework, that enhances the training of downstream task models via prefix prompt. By minimizing data exposur…
cs.CL2024
Unveiling the Magic: Investigating Attention Distillation in Retrieval-augmented Generation
Zizhong Li, Haopeng Zhang, Jiawei Zhang
Retrieval-augmented generation framework can address the limitations of large language models by enabling real-time knowledge updates for more accurate answers. An efficient way in…