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cs.CL2023
ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation
Chenglong Wang, Hang Zhou, Yimin Hu +5
Applying Reinforcement Learning (RL) to sequence generation models enables the direct optimization of long-term rewards (\textit{e.g.,} BLEU and human feedback), but typically requ…
cs.CL2023★ 1 cited
Learning Evaluation Models from Large Language Models for Sequence Generation
Chenglong Wang, Hang Zhou, Kaiyan Chang +6
Automatic evaluation of sequence generation, traditionally reliant on metrics like BLEU and ROUGE, often fails to capture the semantic accuracy of generated text sequences due to t…