Papers by Yongxin Zhou
Scalable Data Synthesis through Human-like Cognitive Imitation and Data Recombination (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) rely on massive amounts of training data, however, the quantity of empirically observed data is limited. |
| Approach: | They propose a data synthesis framework that mimics human cognitive behaviors by recombining and interconnecting heterogeneous data from diverse sources. |
| Outcome: | The proposed framework mimics human cognitive behaviors by recombining and interconnecting heterogeneous data from diverse sources thereby enhancing advanced reasoning capabilities in large language models. |
Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)
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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu
| Challenge: | Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature. |
| Approach: | They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management. |
| Outcome: | The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench. |
PSentScore: Evaluating Sentiment Polarity in Dialogue Summarization (2024.lrec-main)
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| Challenge: | Existing studies have focused on summarizing factual information, leaving out affective content. |
| Approach: | They propose to quantify the preservation of affective content in dialogue summaries using PSentScore measures. |
| Outcome: | The proposed measures show that state-of-the-art summarization models do not preserve well affective content in their summaries. |
Effectiveness of French Language Models on Abstractive Dialogue Summarization Task (2022.lrec-1)
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| Challenge: | Pre-trained language models have established the state-of-the-art on various natural language processing tasks, including dialogue summarization. |
| Approach: | They propose to use several language specific pre-trained models to summarize spontaneous oral dialogues in French using several language-specific pre-trainers: BARThez, BelGPT-2, mBARThes, and mT5. |
| Outcome: | The proposed models outperform the existing models on the DECODA (Call Center) dialogue corpus and show that they are far superior to the current models. |
DiffS2UT: A Semantic Preserving Diffusion Model for Textless Direct Speech-to-Speech Translation (2023.emnlp-main)
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| Challenge: | Existing models for speech generation are not efficient due to low information density of speech data. |
| Approach: | They propose a method to integrate discrete diffusion models into speech generation tasks . they propose to apply diffusion forward process while employing diffusion backward process . |
| Outcome: | The proposed model achieves comparable results to the auto-regressive baselines with significantly fewer decoding steps (50 steps). |
What Matters to an LLM? Behavioral and Computational Evidences from Summarization (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) are increasingly entrusted with the management of information. |
| Approach: | They combine behavioral and computational analyses to find out what LLMs prioritize . they generate length-controlled summaries and derive empirical importance distributions . |
| Outcome: | The proposed model converges on consistent importance patterns and clusters more by family than by size. |
Jargon: A Suite of Language Models and Evaluation Tasks for French Specialized Domains (2024.lrec-main)
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Vincent Segonne, Aidan Mannion, Laura Cristina Alonzo Canul, Alexandre Daniel Audibert, Xingyu Liu, Cécile Macaire, Adrien Pupier, Yongxin Zhou, Mathilde Aguiar, Felix E. Herron, Magali Norré, Massih R Amini, Pierrette Bouillon, Iris Eshkol-Taravella, Emmanuelle Esperança-Rodier, Thomas François, Lorraine Goeuriot, Jérôme Goulian, Mathieu Lafourcade, Benjamin Lecouteux, François Portet, Fabien Ringeval, Vincent Vandeghinste, Maximin Coavoux, Marco Dinarelli, Didier Schwab
| Challenge: | Pretrained language models are the de facto backbone of most state-of-the-art NLP systems. |
| Approach: | They propose a family of domain-specific pretrained PLMs for French focusing on three important domains: transcribed speech, medicine, and law. |
| Outcome: | The proposed models perform better on transcribed speech, medicine, and law domains than state-of-the-art models on a diverse set of tasks and datasets. |
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)
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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang
| Challenge: | Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction. |
| Approach: | They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack. |
| Outcome: | The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. |
Privacy-Preserving Reasoning with Knowledge-Distilled Parametric Retrieval Augmented Generation (2026.findings-acl)
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| Challenge: | Existing RAG systems require uploading local documents to the cloud, resulting in inference latency and poor generalization on out-of-distribution (OOD) inputs. |
| Approach: | They propose a generalizable knowledge-distilled parametric RAG model aligned with standard RAG in document structure and parameter activation. |
| Outcome: | The proposed model outperforms baselines in accuracy and generalizes well on out-of-distribution (OOD) data. |