Papers by Xing W
Too Long, Do Re-weighting for Efficient LLM Reasoning Compression (2026.acl-long)
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Zhong-Zhi Li, Xiao Liang, Zihao Tang, Lei Ji, Peijie Wang, Haotian Xu, Xing W, Haizhen Huang, Weiwei Deng, Yeyun Gong, Zhijiang Guo, Xiao Liu, Fei Yin, Cheng-Lin Liu
| Challenge: | Large Language Models (LLMs) have recently achieved remarkable progress on complex reasoning tasks by leveraging extended Chain-of-Thought (CoT) techniques. |
| Approach: | They propose a method that uses Extended Chain-of-Thought (EFT) to reduce the number of output tokens by nearly 40% while maintaining the accuracy of the reasoning. |
| Outcome: | The proposed method reduces the number of output tokens by nearly 40% while maintaining the accuracy of the reasoning. |
PUNR: Pre-training with User Behavior Modeling for News Recommendation (2023.findings-emnlp)
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| Challenge: | Existing news recommendation methods use pre-trained language models to produce news vectors and user vectors. |
| Approach: | They propose an unsupervised pre-training paradigm with two tasks for user behavior modeling. |
| Outcome: | The proposed model improves on the real-world news benchmark. |
Query-as-context Pre-training for Dense Passage Retrieval (2023.emnlp-main)
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| Challenge: | Existing methods to improve passage retrieval performance by using context-supervised pre-training are weakly correlated. |
| Approach: | They propose to use query-as-context pre-training to train passage-query pairs . they evaluate the pre-trained models on large-scale passage retrieval benchmarks . |
| Outcome: | The proposed technique improves performance on large-scale passage retrieval benchmarks and out-of-domain zero-shot benchmarks. |
CartesianMoE: Boosting Knowledge Sharing among Experts via Cartesian Product Routing in Mixture-of-Experts (2025.naacl-long)
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Zhenpeng Su, Xing W, Zijia Lin, Yizhe Xiong, Minxuan Lv, Guangyuan Ma, Hui Chen, Songlin Hu, Guiguang Ding
| Challenge: | Large language models (LLMs) have been attracting much attention due to their impressive performance in all kinds of downstream tasks. |
| Approach: | They propose a mix-of-experts model that allows the model size to grow without raising training costs. |
| Outcome: | The proposed model outperforms existing models in perplexity and robustness tests. |
SedarEval: Automated Evaluation using Self-Adaptive Rubrics (2024.findings-emnlp)
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| Challenge: | Existing evaluation paradigms rely on generic scoring rubrics that fail to consider the specificities of each question and its problem-solving process. |
| Approach: | They propose a new evaluation paradigm based on self-adaptive rubrics that mimic a human evaluator's analytical process. |
| Outcome: | The proposed evaluation paradigm achieves higher concordance rate with human graders than existing paradigms, including GPT-4. |
MiLe Loss: a New Loss for Mitigating the Bias of Learning Difficulties in Generative Language Models (2024.findings-naacl)
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| Challenge: | Existing generative language models neglect an inherent challenge in text corpus during training, i.e., the imbalance between frequent tokens and infrequent ones. |
| Approach: | They propose a function to mitigate the imbalance between frequent and infrequent tokens . authors propose 'MiLe Loss' function to assess learning difficulty of tokens during training . |
| Outcome: | Experiments show that models with proposed model can improve on downstream benchmarks. |
Dial-MAE: ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systems (2024.naacl-long)
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| Challenge: | Existing studies on dialogue response selection focus on post-training and fine-tuning for cross-encoders. |
| Approach: | They propose a post-training technique tailored for dense encoders in dialogue response selection . they propose 'Dialogue Contextual Masking Auto-Encoder' which compresses dialogue semantics into dense vectors . |
| Outcome: | The proposed technique achieves state-of-the-art on two commonly evaluated benchmarks. |