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cs.CL2024
RNR: Teaching Large Language Models to Follow Roles and Rules
Kuan Wang, Alexander Bukharin, Haoming Jiang +9
Instruction fine-tuning (IFT) elicits instruction following capabilities and steers the behavior of large language models (LLMs) via supervised learning. However, existing models t…
cs.CL2024
ARL2: Aligning Retrievers for Black-box Large Language Models via Self-guided Adaptive Relevance Labeling
Lingxi Zhang, Yue Yu, Kuan Wang +1
Retrieval-augmented generation enhances large language models (LLMs) by incorporating relevant information from external knowledge sources. This enables LLMs to adapt to specific d…