Papers by Leiyu Pan
FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data (2024.emnlp-industry)
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Haoran Sun, Renren Jin, Shaoyang Xu, Leiyu Pan, null Supryadi, Menglong Cui, Jiangcun Du, Yikun Lei, Lei Yang, Ling Shi, Juesi Xiao, Shaolin Zhu, Deyi Xiong
| Challenge: | Large language models exhibit significant performance discrepancies between high- and low-resource languages. |
| Approach: | They present an open-source multilingual LLM with 8 billion parameters and a multilingual instruction dataset. |
| Outcome: | The proposed model achieves consistent multilingual representations across languages. |
From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan (2026.acl-long)
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Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong
| Challenge: | Large language models have achieved remarkable success across a wide range of tasks, yet their performance remains heavily biased toward high-resource languages. |
| Approach: | They propose a pipeline for advancing Tibetan language modeling through multilingual continual pre-training with Tibetan, Chinese, and English. |
| Outcome: | The proposed model outperforms open-source and Tibetan-focused models on diverse tasks. |
LANDeRMT: Dectecting and Routing Language-Aware Neurons for Selectively Finetuning LLMs to Machine Translation (2024.acl-long)
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| Challenge: | Existing studies have shown promising results in multilingual translation with limited bilingual supervision. |
| Approach: | They propose a Language-Aware Neuron Detecting and Routing framework that fine tunes LLMs to Machine Translation with diverse translation training data. |
| Outcome: | The proposed framework selectively finetunes LLMs to MT tasks with diverse translation training data. |
An Empirical Study on the Robustness of Massively Multilingual Neural Machine Translation (2024.lrec-main)
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| Challenge: | Recent years have witnessed that massively multilingual neural machine translation (MMNMT) achieves a remarkable progress in both high- and low-resource language translation. |
| Approach: | They propose to use a robustness evaluation benchmark dataset to assess the translation robustness of Indonesian-Chinese translation in the face of various naturally occurring noise. |
| Outcome: | The proposed dataset is publicly available at https://github.com/ID-ZH-MTRobustEval. |
Good Reasoning Makes Good Demonstrations: Implicit Reasoning Quality Supervision via In-Context Reinforcement Learning (2026.findings-acl)
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| Challenge: | Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in large language models but treats all correct solutions equally, potentially reinforcing flawed traces that arrive at correct answers by chance. |
| Approach: | They propose a method that reweights rewards by a factor approximately proportional to Evidence Gain and assigns higher weights to high-quality traces without requiring costly computation. |
| Outcome: | Experiments on mathematical reasoning benchmarks show that Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in large language models but treats all correct solutions equally. |
Can Large Language Models Learn Translation Robustness from Noisy-Source In-context Demonstrations? (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have been used for machine translation, but their robustness remains a challenge, as they struggle to translate sentences in the presence of noise even when using similarity-based in-context learning methods. |
| Approach: | They propose a scheme for studying machine translation robustness on LLMs by using noisy-source demonstration examples. |
| Outcome: | The proposed model can learn robustness from noisy-source demonstration examples, thereby improving translation performance on noisy sentences. |
CE-GPPO: Coordinating Entropy via Gradient-Preserving Clipping Policy Optimization in Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing methods for proximal policy optimization discard valuable gradient signals from low-probability tokens due to the clipping mechanism. |
| Approach: | They propose an algorithm that reintroduces gradients from clipped tokens in native PPO in a gentle and bounded manner. |
| Outcome: | The proposed algorithm outperforms strong baselines on reasoning benchmarks on different model scales. |
Is Robustness Transferable across Languages in Multilingual Neural Machine Translation? (2023.findings-emnlp)
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| Challenge: | Existing studies have focused on bilingual machine translation with a single translation direction. |
| Approach: | They propose a robustness transfer analysis protocol to analyze the transferability of robustness across different languages in multilingual neural machine translation. |
| Outcome: | The proposed protocol shows that the robustness gained in one translation direction can transfer to other translation directions. |
DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs (2025.emnlp-main)
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Minxuan Lv, Zhenpeng Su, Leiyu Pan, Yizhe Xiong, Zijia Lin, Hui Chen, Wei Zhou, Jungong Han, Guiguang Ding, Wenwu Ou, Di Zhang, Kun Gai, Songlin Hu
| Challenge: | Existing sparsification methods like pruning can lose model knowledge through parameter removal. |
| Approach: | They propose a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks. |
| Outcome: | The proposed approach achieves superior performance across language modeling and downstream tasks under equivalent computational constraints. |
Entropy Ratio Clipping as a Soft Global Constraint for Stable Reinforcement Learning (2026.findings-acl)
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Zhenpeng Su, Leiyu Pan, Minxuan Lv, Tiehua Mei, Zijia Lin, Yuntao Li, Wenping Hu, Ruiming Tang, Kun Gai, Guorui Zhou
| Challenge: | Large language model post-training often adopts an off-policy training paradigm . however, the off-poliicy training model introduces distribution shifts that push the policy beyond the trust region. |
| Approach: | They propose to use the entropy ratio as a global metric to measure the relative change in policy exploration throughout updates. |
| Outcome: | Experiments show that the proposed metric improves performance across multiple benchmarks. |