Papers by Xiao-Hua Zhou
Prevent the Language Model from being Overconfident in Neural Machine Translation (2021.acl-long)
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| Challenge: | Neural Machine Translation models are based on partial translation and a language model that predicts the next token based only on partial. |
| Approach: | They propose a Margin-based Token-level Objective and a Sentence-level Goal to maximize the Margin . they propose to model the next token based on partial translation . |
| Outcome: | The proposed approach improves translation adequacy and fluency on English-to-German, Chinese-to English and French translation tasks. |
Phased Instruction Fine-Tuning for Large Language Models (2024.findings-acl)
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| Challenge: | Existing methods to enhance pre-trained language models' ability to follow instructions are limited due to the simultaneous handling of varying instruction complexities. |
| Approach: | They propose a phased instruction fine-tuning method that posits that the transition of a pre-trained language model from simple next-word prediction to sophisticated instruction following is a gradual learning process. |
| Outcome: | The proposed method surpasses the one-off instruction fine-tuning method in win rate and validates the hypothesis of progressive alignment. |