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cs.CL2024
Improving Multilingual Instruction Finetuning via Linguistically Natural and Diverse Datasets
Sathish Reddy Indurthi, Wenxuan Zhou, Shamil Chollampatt +4
Advancements in Large Language Models (LLMs) have significantly enhanced instruction-following capabilities. However, most Instruction Fine-Tuning (IFT) datasets are predominantly…
cs.CL2024
WPO: Enhancing RLHF with Weighted Preference Optimization
Wenxuan Zhou, Ravi Agrawal, Shujian Zhang +5
Reinforcement learning from human feedback (RLHF) is a promising solution to align large language models (LLMs) more closely with human values. Off-policy preference optimization,…