Papers by Chengguo Yin
Enhancing Reinforcement Learning with Label-Sensitive Reward for Natural Language Understanding (2024.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have yielded remarkable performance, but objective mismatch issues hinder RLHF learning. |
| Approach: | They propose a Reinforcement Learning framework enhanced with Label-sensitive reward to enhance LLMs' alignment and generation capabilities. |
| Outcome: | The proposed framework improves performance on five diverse models across eight tasks. |
Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors (2024.acl-long)
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| Challenge: | Multiple-Choice Questions (MCQs) are a critical area of research in the study of Large Language models (LLMs). |
| Approach: | They propose an efficient SFT algorithm for MCQs, termed Point-wise Intelligent Feedback, which constructs negative instances by randomly combing the incorrect option contents with all candidate symbols. |
| Outcome: | The proposed algorithm significantly reduces the model’s selection bias by improving its MCSB capability. |