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cs.CL2024
Disentangling Preference Representation and Text Generation for Efficient Individual Preference Alignment
Jianfei Zhang, Jun Bai, Bei Li +4
Aligning Large Language Models (LLMs) with general human preferences has been proved crucial in improving the interaction quality between LLMs and human. However, human values are…
cs.CL2024
Learning to Adapt to Low-Resource Paraphrase Generation
Zhigen Li, Yanmeng Wang, Rizhao Fan +3
Paraphrase generation is a longstanding NLP task and achieves great success with the aid of large corpora. However, transferring a paraphrasing model to another domain encounters t…