Papers by Yongdong Zhang

ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferences (2024.acl-long)

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Yuanhe Tian, Ruyi Gan, Yan Song, Jiaxing Zhang, Yongdong Zhang

Language Models

Challenge:	Current large language models (LLMs) are ineffective in learning domain knowledge and aligning with human preference.
Approach:	They propose a benchmark LLM for Chinese medical domain that uses pre-training, supervised fine-tuning and RLHF to train LLMs.
Outcome:	The proposed LLM performs better than existing LLMs in the Chinese medical domain.

Curriculum Learning for Natural Language Understanding (2020.acl-main)

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Benfeng Xu, Licheng Zhang, Zhendong Mao, Quan Wang, Hongtao Xie, Yongdong Zhang

Semantic Text Processing

Challenge:	Pre-trained language models can be fine tuned to perform NLU tasks in a straightforward manner.
Approach:	They propose a pretrain-finetune paradigm for natural language understanding (NLU) they propose 'a cross-trainset' approach that allows users to distinguish easy from difficult examples .
Outcome:	The proposed approach achieves significant performance improvements on a wide range of NLU tasks.

S2ynRE: Two-stage Self-training with Synthetic data for Low-resource Relation Extraction (2023.acl-long)

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Benfeng Xu, Quan Wang, Yajuan Lyu, Dai Dai, Yongdong Zhang, Zhendong Mao

Information Extraction & Text Mining

Challenge:	Existing methods for relation extraction suffer from the inadequacy of large-scale annotated data.
Approach:	They propose a framework for two-stage self-training with synthetic data for relation extraction .
Outcome:	The proposed framework is based on two-stage self-training with synthetic data . it is able to synthesize large quantities of training data and iteratively and alternately learn from synthetic and golden data together.

Align Documents to Questions: Question-Oriented Document Rewriting for Retrieval-Augmented Generation (2026.findings-acl)

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Jiaang Li, Zhendong Mao, Quan Wang, Yuning Wan, Yongdong Zhang

Text Generation

Challenge:	Retrieval-Augmented Generation (RAG) enhances the factuality of Large Language Models (LLMs) however, LLMs exhibit a stylistic bias when presented with mixed contexts, revealing a bottleneck in their utility.
Approach:	They propose a style-controlled rewriter that aligns retrieved documents with a question-oriented style while preserving facts.
Outcome:	The proposed model improves RAG pipelines by 8% with negligible latency overhead.

Prompting Few-shot Multi-hop Question Generation via Comprehending Type-aware Semantics (2024.findings-naacl)

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Zefeng Lin, Weidong Chen, Yan Song, Yongdong Zhang

Low-Resource NLP

Challenge:	Existing approaches for multi-hop question generation rely on large annotated data . supervised approaches rely only on large labeled data, making it hard to perform tasks.
Approach:	They propose a type-aware semantics extraction-based chain-of-thought method for multi-hop question generation for documents . they first extract question types and essential semantic phrases from the given documents and the answer .
Outcome:	The proposed approach extracts question types and essential semantic phrases from documents and the answer.

FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents (2026.acl-long)

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Chiwei Zhu, Benfeng Xu, Mingxuan Du, Shaohan Wang, Xiaorui Wang, Zhendong Mao, Yongdong Zhang

Semantic Text Processing

Challenge:	Long trajectories in deep research often exceed model context limits, compressing token budgets for both evidence collection and report writing.
Approach:	They propose a file-system-based framework that scales deep research beyond context window . a Context Builder agent acts as a librarian and a Report Writer agent composes the final report .
Outcome:	Experiments on two open-ended benchmarks show that FS-Researcher achieves state-of-the-art report quality across different backbone models.

GASim: A Graph-Accelerated Hybrid Framework for Social Simulation (2026.acl-long)

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Xuan Zhou, Yanhui Sun, Hantao Yao, Allen He, Yongdong Zhang, Wu Liu

Multimodality

Challenge:	Large-scale social simulators require high latency due to expensive memory retrieval and sequential ABM execution.
Approach:	They propose a graph-accelerated hybrid multi-agent framework for large-scale social simulations that uses large language models and numerical agent-based models to scale up simulations.
Outcome:	The proposed framework delivers 9.94 speedup over the traditional framework and consumes less than 20% of tokens.

On the Calibration of Large Language Models and Alignment (2023.findings-emnlp)

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Chiwei Zhu, Benfeng Xu, Quan Wang, Yongdong Zhang, Zhendong Mao

Language Models

Challenge:	Large language models are becoming more popular and are proving to be reliable . however, their reliability is often understudied due to their uncertainty and complex structure .
Approach:	They conduct a systematic examination of the calibration of aligned language models throughout the entire construction process including pretraining and alignment training.
Outcome:	The results shed light on whether popular large language models are well-calibrated and how the training process influences model calibration.

Grammatical Error Correction via Mixed-Grained Weighted Training (2023.findings-emnlp)

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Jiahao Li, Quan Wang, Chiwei Zhu, Zhendong Mao, Yongdong Zhang

Syntactic Text Processing

Challenge:	Empirical evaluation shows that MainGEC achieves consistent and significant performance improvements on two benchmark datasets.
Approach:	They propose to use mixed-grained weighted training to improve the training effect for GEC by analyzing the inherent discrepancies in annotated training data.
Outcome:	Empirical results show that the proposed method achieves significant performance improvements on two benchmark datasets.

Aspect-based Sentiment Analysis with Context Denoising (2024.findings-naacl)

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Yuanhe Tian, Chang Liu, Yan Song, Fei Xia, Yongdong Zhang

Sentiment Analysis

Challenge:	Existing approaches to ABSA use text encoders to locate important context features or remove them from input.
Approach:	They propose to improve ABSA with context denoising to remove noise from text . they use diffusion networks to perform denoizing process to gradually eliminate noise . paper shows that aspect-based sentiment analysis is effective for fine-grained analysis .
Outcome:	The proposed approach improves ABSA on five widely used ABSA datasets.

Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity (2022.emnlp-main)

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Jiahao Li, Quan Wang, Zhendong Mao, Junbo Guo, Yanyan Yang, Yongdong Zhang

Syntactic Text Processing

Challenge:	Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts.
Approach:	They propose an auxiliary task of Chinese pronunciation prediction to improve CSC . they propose adaptive weighting schemes and a delicate correction strategy .
Outcome:	The proposed auxiliary task improves Chinese pronunciation prediction on three benchmarks.

Air-Decoding: Attribute Distribution Reconstruction for Decoding-Time Controllable Text Generation (2023.emnlp-main)

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Tianqi Zhong, Quan Wang, Jingxuan Han, Yongdong Zhang, Zhendong Mao

Text Generation

Challenge:	Controllable text generation (CTG) aims to generate text with desired attributes, but current methods lack high levels of controllability.
Approach:	They propose a lightweight decoding framework that reconstructs attribute distributions to balance the weights between attribute words and non-attribute words to generate more fluent text.
Outcome:	The proposed framework achieves state-of-the-art control performance on multiple CTG tasks.