Papers by Lingxiao Zhao
T-REG: Preference Optimization with Token-Level Reward Regularization (2025.acl-long)
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| Challenge: | Reinforcement learning from human feedback (RLHF) is a dominant approach for large language models to follow instructions and produce meaningful alignment. |
| Approach: | They propose a method that leverages human feedback to optimize large language models . they propose to use sequence-level and token-level rewards to optimize preference . |
| Outcome: | The proposed method outperforms baseline methods on Alpaca Eval 2 and Arena-Hard benchmarks. |
Improving Multilingual Instruction Finetuning via Linguistically Natural and Diverse Datasets (2024.findings-emnlp)
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Sathish Reddy Indurthi, Wenxuan Zhou, Shamil Chollampatt, Ravi Agrawal, Kaiqiang Song, Lingxiao Zhao, Chenguang Zhu
| Challenge: | Advancements in Large Language Models (LLMs) have significantly enhanced instruction-following capabilities, but most IFT datasets are predominantly in English, limiting model performance in other languages. |
| Approach: | They propose a method for collecting multilingual IFT datasets that preserves linguistic naturalness and ensures prompt diversity. |
| Outcome: | Experiments show that LLMs fine-tuned using this method show significant improvements in generative and discriminative tasks. |