Papers by Ding Liang

AMIA: Automatic Masking and Joint Intention Analysis Makes LVLMs Robust Jailbreak Defenders (2025.findings-emnlp)

Challenge:	None Large language models (LLMs) are emerging as a key tool for automated programming.
Approach:	They compare performance of None Large language models with language understanding models on functional programming and object-oriented programming benchmarks.
Outcome:	The models perform relatively well on functional programming (FP) and object-oriented programming (OOP) benchmarks, while exhibiting poor performance on OOP benchmarks.

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Yuqi Zhang, Yuchun Miao, Zuchao Li, Liang Ding

Reason-KE++: Aligning the Process, Not Just the Outcome, for Faithful LLM Knowledge Editing (2026.findings-acl)

Challenge:	AMIA is a lightweight, inference-only defense for Large Vision–Language Models . it automatically masks text-irrelevant image patches and conducts joint Intention Analysis .
Approach:	AMIA is a lightweight, inference-only defense for large vision–language models . it automatically masks a small set of text-irrelevant image patches to disrupt adversarial perturbations .
Outcome:	AMIA improves defense success rates across diverse LVLMs and jailbreak benchmarks . it preserves general utility with only 2% accuracy drop, incurs only modest inference overhead .

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Yuchen Wu, Liang Ding, Li Shen, Dacheng Tao

Reasoning

Challenge:	Current methods for modifying parameters to integrate new knowledge are not accurate enough.
Approach:	They propose an SFT+RL framework that instills process-level faithfulness by a stage-aware Reward mechanism and a Stage-assisted Reward Mechanism.
Outcome:	The proposed framework instills process-level faithfulness while boosting final accuracy.

Zero-shot Sharpness-Aware Quantization for Pre-trained Language Models (2023.emnlp-main)

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Miaoxi Zhu, Qihuang Zhong, Li Shen, Liang Ding, Juhua Liu, Bo Du, Dacheng Tao

Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis (2024.findings-emnlp)

Challenge:	Existing zero-shot quantization methods are based on overfitting problem in adversarial learning process, leading to sub-optimal performance.
Approach:	They propose a zero-shot sharpness-aware quantization framework for the quantization of various PLMs by optimizing a minimax problem.
Outcome:	The proposed framework can achieve significant performance gains on discriminative and generative PLMs.

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Xuanwen Ding, Jie Zhou, Liang Dou, Qin Chen, Yuanbin Wu, Arlene Chen, Liang He

Revisiting Knowledge Distillation for Autoregressive Language Models (2024.acl-long)

Challenge:	Existing studies focus on improving the performance of domain-specific models based on the target dataset.
Approach:	They propose a Large Language Model-based Continual Learning (LLM-CL) model for ABSA that learns the target domain’s ability while maintaining the history domains’ abilities.
Outcome:	The proposed model obtains new state-of-the-art over 19 datasets.

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Qihuang Zhong, Liang Ding, Li Shen, Juhua Liu, Bo Du, Dacheng Tao

Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models (2024.findings-acl)

Challenge:	Autoregressive language models (LMs) are expensive and memory intensive, preventing the development of industrial applications.
Approach:	They propose an adaptive teaching approach to improve the KD of autoregressive language models by distilling knowledge into a small student model.
Outcome:	The proposed method can achieve consistent and significant performance gains across all model types and sizes.

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Junfei Wu, Qiang Liu, Ding Wang, Jinghao Zhang, Shu Wu, Liang Wang, Tieniu Tan

Reducing Spurious Correlations in Aspect-based Sentiment Analysis with Explanation from Large Language Models (2023.findings-emnlp)

Challenge:	Object hallucination has been an Achilles’ heel which hinders the broader applications of large vision-language models (LVLMs).
Approach:	They propose a logical closed loop-based framework for Object Hallucination Detection and Mitigation that uses logical consistency probing to raise questions with logical correlations to determine hallucinations.
Outcome:	The proposed method can be applied to all existing LVLMs and is effective and general.

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Qianlong Wang, Keyang Ding, Bin Liang, Min Yang, Ruifeng Xu

Improving Neural Machine Translation by Bidirectional Training (2021.emnlp-main)

Challenge:	Aspect-based sentiment analysis models are susceptible to learning spurious correlations between words . a recent study shows that feature engineering is time-consuming and costly .
Approach:	They propose to use a template to prompt LLMs to generate an appropriate explanation for the sentiment polarity of each aspect to reduce spurious correlations.
Outcome:	The proposed methods improve ABSA models and their generalization ability.

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Liang Ding, Di Wu, Dacheng Tao

Self-Powered LLM Modality Expansion for Large Speech-Text Models (2024.emnlp-main)

Challenge:	Experimental results show that bidirectional training pushes the SOTA neural machine translation performance significantly higher.
Approach:	They propose a bidirectional training strategy that updates model parameters at the early stage and tunes it normally.
Outcome:	The proposed approach pushes the SOTA neural machine translation performance significantly higher on 15 translation tasks on 8 language pairs.

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Tengfei Yu, Xuebo Liu, Zhiyi Hou, Liang Ding, Dacheng Tao, Min Zhang

Progressive Multi-Granularity Training for Non-Autoregressive Translation (2021.findings-acl)

Challenge:	Large language models exhibit remarkable performance across diverse tasks . however, these methods require significant resource demands and tend to overfit specific tasks.
Approach:	They propose a self-powered LSM that leverages augmented automatic speech recognition data generated by the model itself for more effective instruction tuning.
Outcome:	The proposed model mitigates speech anchor bias and improves the fusion of speech and text modalities in large language models.

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Liang Ding, Longyue Wang, Xuebo Liu, Derek F. Wong, Dacheng Tao, Zhaopeng Tu

Interpretable Proof Generation via Iterative Backward Reasoning (2022.naacl-main)

Challenge:	Non-autoregressive translation models are weak at learning high-mode knowledge, argues a new study . despite the improved learning difficulty, there are still complicated word orders and structures in the synthetic sentences, making the NAT performance sub-optimal.
Approach:	They propose to train non-autoregressive translation models to learn fine-grained lower-mode knowledge . they break down sentence-level examples into three types and increase granularities .
Outcome:	The proposed method improves phrase translation accuracy and model reordering ability against strong NAT baselines.

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Hanhao Qu, Yu Cao, Jun Gao, Liang Ding, Ruifeng Xu

EventRAG: Enhancing LLM Generation with Event Knowledge Graphs (2025.acl-long)

Challenge:	Existing proof generation tasks require reasoning capabilities, but they usually just request for an answer without the reasoning procedure that would make it interpretable.
Approach:	They propose an iterative backward reasoning model to solve the proof generation tasks on rule-based Question Answering.
Outcome:	The proposed model improves in-domain performance and cross-domain transferability over existing models.

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Zairun Yang, Yilin Wang, Zhengyan Shi, Yuan Yao, Lei Liang, Keyan Ding, Emine Yilmaz, Huajun Chen, Qiang Zhang

Ask-and-Verify: Span Candidate Generation and Verification for Attribute Value Extraction (2022.emnlp-industry)

Challenge:	Existing approaches to text generation often neglect event structures that shape real-world narratives.
Approach:	They propose a framework that integrates structured event semantics with iterative retrieval and inference to enhance text generation.
Outcome:	Experiments on UltraDomain and MultiHopRAG show that the proposed framework outperforms baseline RAG systems in generation effectiveness, logical consistency, and multi-hop reasoning accuracy.

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Yifan Ding, Yan Liang, Nasser Zalmout, Xian Li, Christan Grant, Tim Weninger

Information Extraction & Text Mining

Challenge:	Existing reading comprehension models can over-generate attribute values which hinders precision.
Approach:	They propose a product attribute value extraction task that captures key factual information from product descriptions and a new end-to-end pipeline framework called Ask-and-Verify.
Outcome:	The proposed framework outperforms existing models by up to 3.1% F1 absolute improvement points while scaling to thousands of attributes.

Intention Analysis Makes LLMs A Good Jailbreak Defender (2025.coling-main)

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Yuqi Zhang, Liang Ding, Lefei Zhang, Dacheng Tao

AgentInit: Initializing LLM-based Multi-Agent Systems via Diversity and Expertise Orchestration for Effective and Efficient Collaboration (2025.findings-emnlp)

Challenge:	Existing methods to align large language models with human values overlook the intrinsic nature of jailbreaks, which limits their effectiveness in complex scenarios.
Approach:	They propose a simple yet highly effective defense strategy, i.e., Intention Analysis (IA). They show that IA suppresses LLM’s tendency to follow jailbreak prompts, thereby enhancing safety.
Outcome:	The proposed strategy reduces harmfulness of LLMs and outperforms GPT-3.5 in attack success rate.

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Chunhao Tian, Yutong Wang, Xuebo Liu, Zhexuan Wang, Liang Ding, Miao Zhang, Min Zhang

Selected Languages are All You Need for Cross-lingual Truthfulness Transfer (2025.coling-main)

Challenge:	Existing MAS initialization methods do not fully account for the collaborative needs of the generated agents in subsequent stages.
Approach:	They propose to use a Natural Language to Format mechanism to optimize the structure of agent teams and incorporate a natural language to format mechanism to ensure consistency and standardization.
Outcome:	The proposed method outperforms state-of-the-art initialization methods and pre-defined strategies across various frameworks and tasks while reducing token consumption.

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Weihao Liu, Ning Wu, Wenbiao Ding, Shining Liang, Ming Gong, Dongmei Zhang

DB-LLM: Accurate Dual-Binarization for Efficient LLMs (2024.findings-acl)

Challenge:	Existing methods for truthfulness enhancement in English are limited to multilingual scenarios.
Approach:	They propose a method for cross-lingual truthfulness transfer that uses language bias and transfer contributions to select an optimal subset of all tested languages and employ translation instruction tuning for cross language truthfulness transfers.
Outcome:	The proposed method reduces multilingual representation disparity and boosts cross-lingual truthfulness transfer of LLMs.

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Hong Chen, Chengtao Lv, Liang Ding, Haotong Qin, Xiabin Zhou, Yifu Ding, Xuebo Liu, Min Zhang, Jinyang Guo, Xianglong Liu, Dacheng Tao

Reconstruct Before Summarize: An Efficient Two-Step Framework for Condensing and Summarizing Meeting Transcripts (2023.emnlp-main)

Challenge:	Existing methods for ultra-low bit quantization cause severe accuracy drops . a novel Dual-Binarization method is proposed for efficient Large Language Models .
Approach:	They propose a Dual-Binarization method that takes 2-bit-width and binarization into account . they propose DB-LLM, which uses a 2-bit binarized weighted model to represent weights efficiently .
Outcome:	The proposed method surpasses the current State-of-the-Art in ultra-low bit quantization and achieves 20% reduction in computational consumption compared to the SOTA method under the same bit-width.

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Haochen Tan, Han Wu, Wei Shao, Xinyun Zhang, Mingjie Zhan, Zhaohui Hou, Ding Liang, Linqi Song

Context-aware Watermark with Semantic Balanced Green-red Lists for Large Language Models (2024.emnlp-main)

Challenge:	Existing approaches to meeting summarization are limited due to noise, lengthy transcripts, and scattered salient information.
Approach:	They propose a two-step framework for meeting summarization that leverages a self-supervised paradigm to reconstruct transcripts and a relative positional bucketing algorithm to equip models to generate the summary.
Outcome:	The proposed method significantly reduces memory consumption and processing time on two meeting summarization datasets.

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Yuxuan Guo, Zhiliang Tian, Yiping Song, Tianlun Liu, Liang Ding, Dongsheng Li

Robustness in NLP

Challenge:	Recent research suggests that watermarking methods cause degradation of text quality due to semantic disparities between the watermarked text and the unwatermarked text.
Approach:	They propose a semantic-aware watermark method that generates a watermark key considering contexts to split a green/red list for watermark injection.
Outcome:	The proposed method reduces performance drop due to adding bias on green lists . it also allows green lists to cover almost all semantics .

MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models (2025.coling-main)

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Zihao Wei, Jingcheng Deng, Liang Pang, Hanxing Ding, Huawei Shen, Xueqi Cheng

Revisiting Token Dropping Strategy in Efficient BERT Pretraining (2023.acl-long)

Challenge:	Existing studies on knowledge editing focus on monolingual scenarios, neglecting the complexities presented by multilingual contexts and multi-hop reasoning.
Approach:	They propose a benchmark to evaluate the adaptability of multilingual knowledge editing methods.
Outcome:	The proposed benchmark evaluates the adaptability of multilingual knowledge editing methods across five languages.

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Qihuang Zhong, Liang Ding, Juhua Liu, Xuebo Liu, Min Zhang, Bo Du, Dacheng Tao

AgentDropout: Dynamic Agent Elimination for Token-Efficient and High-Performance LLM-Based Multi-Agent Collaboration (2025.acl-long)

Challenge:	Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT.
Approach:	They propose a semantic-consistent learning method to improve token dropping by skipping the computation of a subset of input tokens at several middle layers.
Outcome:	The proposed method achieves consistent and significant performance gains across all tasks and model sizes.

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Zhexuan Wang, Yutong Wang, Xuebo Liu, Liang Ding, Miao Zhang, Jie Liu, Min Zhang

Revisiting Catastrophic Forgetting in Large Language Model Tuning (2024.findings-emnlp)

Challenge:	Existing methods for MAS suffer from high token consumption and inefficiency due to frequent generation and communication among multiple agents.
Approach:	They propose a multi-agent system based on large language models that identifies redundant agents and communication across different communication rounds by optimizing the adjacency matrices of the communication graphs and eliminates them to enhance both token efficiency and task performance.
Outcome:	The proposed method reduces prompt token consumption and completion token consumption by 18.4% and improves task performance by 1.14.

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Hongyu Li, Liang Ding, Meng Fang, Dacheng Tao

Quantifying and Improving the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding Data (2026.acl-long)

Challenge:	Catastrophic Forgetting (CF) compromises the effectiveness of large language models during fine-tuning, yet the underlying causes of CF remain largely unexplored.
Approach:	They propose a method to flatten the model loss landscape to mitigate CF by flattening the loss landscape.
Outcome:	The proposed method complements existing anti-forgetting strategies, further enhancing the resistance of LLMs to CF.

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Shiping Yang, Jie Wu, Wenbiao Ding, Ning Wu, Shining Liang, Ming Gong, Hongzhi Li, Hengyuan Zhang, Angel X. Chang, Dongmei Zhang

Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking (2023.acl-long)

Challenge:	Existing studies on robustness to explicit noise (e.g., document semantics) but overlook implicit noise (spurious features).
Approach:	They propose a framework to quantify the robustness of RAGs against spurious features by integrating a data synthesis pipeline and a taxonomy.
Outcome:	The proposed framework quantifies the robustness of RALMs against spurious features.

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Qingyue Wang, Liang Ding, Yanan Cao, Yibing Zhan, Zheng Lin, Shi Wang, Dacheng Tao, Li Guo

Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding (2024.findings-acl)

Challenge:	Existing methods to enhance the zeroshot generalization of DST fail to effectively decouple semantics of samples, limiting the zero-shot performance of the system.
Approach:	They propose a new learning schema that explicitly disentangles the semantics of seen data and leverages the performance and robustness with the mixture-of-experts mechanism.
Outcome:	The proposed model achieves state-of-the-art on multiWOZ2.1 with 10M trainable parameters and is robust to the mixture-of experts mechanism.

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Xintong Wang, Jingheng Pan, Liang Ding, Chris Biemann

Learning to Control Summaries with Score Ranking (2026.findings-acl)

Challenge:	Recent research in large vision-language models has shown promising results, but the issue of hallucination remains.
Approach:	They propose an instruction-based method to reduce hallucinations in large vision-language models . they use disturbance instructions to exacerbate hallucinosity in multimodal fusion modules .
Outcome:	The proposed method reduces hallucinations in multimodal fusion modules by reducing alignment uncertainty and subtracting hallucines from the original distribution.

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Hongye Liu, Liang Ding, Ricardo Henao

Summarization

Challenge:	Recent advances in summarization focus on improving summary quality across multiple dimensions, but they overlook the challenge of controlling summary generation with respect to individual dimensions.
Approach:	They propose a loss function that aligns model outputs with fine-grained, model-based evaluation scores to enable both improvement in summary quality and dimension-specific control.
Outcome:	The proposed method improves the overall quality of summaries while maintaining strong control over individual quality dimensions.

DUAL RM: Beyond Rule-based Preference Reward Modeling via Meta-Reward (2026.acl-long)

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Xiaobo Liang, Wanfu Wang, Qipeng Huang, Yuyang Ding, Zecheng Tang, Yixin Ji, Qianben Chen, Zhe Zhao, Kehai Chen, Juntao Li, Min Zhang

Challenge:	Existing preference-based reward modeling methods face a recursive dependency where each verifier requires a meta-verifier, leading to continuous and costly dependence on human annotation.
Approach:	They propose a dual RM that couples discriminative and generative reward models under a non-parametric meta-reward.
Outcome:	The proposed model achieves strong performance across major preference benchmarks and even when trained exclusively on language modality, it exhibits robust cross-modal transfer on Omni-RewardBench.

Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratch (2025.acl-long)

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Yuyang Ding, Xinyu Shi, Xiaobo Liang, Juntao Li, Zhaopeng Tu, Qiaoming Zhu, Min Zhang

Reasoning

Challenge:	Existing methods to improve the mathematical reasoning capabilities of Large Language Models (LLMs) are limited due to the proprietary nature of the data.
Approach:	They propose a data synthesis method that generates large-scale mathematical reasoning datasets using lightweight 7B-scale models.
Outcome:	The proposed method outperforms existing open-source datasets in both in-domain and out-of-domain evaluations and shows improvements in code reasoning tasks.

POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation (2024.acl-long)

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Shilong Pan, Zhiliang Tian, Liang Ding, Haoqi Zheng, Zhen Huang, Zhihua Wen, Dongsheng Li

Low-Resource NLP

Challenge:	Low-resource languages (LRLs) face challenges in supervised neural machine translation due to limited parallel data.
Approach:	They propose a method that uses a dynamic graph to organize auxiliary languages in prompts to improve LRL translations.
Outcome:	The proposed method improves translation accuracy in low-resource languages (LRLs) using auxiliary language pairs and synthetic pseudo-parallel data.

Token-Level Self-Evolution Training for Sequence-to-Sequence Learning (2023.acl-short)

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Keqin Peng, Liang Ding, Qihuang Zhong, Yuanxin Ouyang, Wenge Rong, Zhang Xiong, Dacheng Tao

Merging Experts into One: Improving Computational Efficiency of Mixture of Experts (2023.emnlp-main)

Challenge:	Adaptive training approaches do not consider the variation of learning difficulty in different training steps, making the learning deterministic and sub-optimal.
Approach:	They propose a dynamic token-level self-evolution training method that reweighs the training losses of different target tokens based on priors.
Outcome:	Empirically, the proposed method yields significant improvements on three translation tasks.

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Shwai He, Run-Ze Fan, Liang Ding, Li Shen, Tianyi Zhou, Dacheng Tao

Less is More: Improving LLM Reasoning with Minimal Test-Time Intervention (2026.acl-long)

Challenge:	Extensive experiments show that MEO significantly improves computational efficiency . compared to dense networks, sparsely activated networks only employ a few parameters for each input .
Approach:	They propose a method that merges multiple experts into one to reduce computation costs . they demonstrate that a sparse Mixture of Experts (MoE) can reduce the cost by activating a small subset of parameters for each input .
Outcome:	The proposed approach reduces the computational cost to that of a single expert by 83.3% compared to 82.6% in vanilla MoE.

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Zhen Yang, Mingyang Zhang, Feng Chen, Ganggui Ding, Liang Hou, Xin Tao, Ying-Cong Chen

Uncertainty Aware Learning for Language Model Alignment (2024.acl-long)

Challenge:	Recent advances in large language models (LLMs) have focused on test-time scaling to improve reasoning quality but at the cost of efficiency.
Approach:	They propose a training-free framework that enhances reasoning accuracy and stability with minimal overhead.
Outcome:	The proposed framework yields consistent gains across general, coding, and STEM tasks while remaining highly efficient.

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Yikun Wang, Rui Zheng, Liang Ding, Qi Zhang, Dahua Lin, Dacheng Tao

Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling (2026.findings-acl)

Challenge:	Existing alignment strategies that focus on diverse and high-quality data often overlook the intrinsic uncertainty of tasks, learning all data samples equally.
Approach:	They propose to introduce the sample uncertainty into the alignment of different task scenarios by a simple fashion by setting the label smoothing value of training according to the uncertainty of individual samples.
Outcome:	The proposed model outperforms standard supervised fine-tuning on high-entropy tasks and complex low-entropic tasks.

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Fei Wang, Li Shen, Liang Ding, Chao Xue, Ye Liu, Changxing Ding

PAD-Net: An Efficient Framework for Dynamic Networks (2023.acl-long)

Challenge:	Existing methods for fine-tuning Large Language Models are slow and lack of performance.
Approach:	They propose a Zeroth-Order optimization framework that uses forward passes to fine-tune Large Language Models.
Outcome:	The proposed framework achieves 1.7 to 3.0 wall-clock acceleration on LLaMA and OPT models.

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Shwai He, Liang Ding, Daize Dong, Boan Liu, Fuqiang Yu, Dacheng Tao

Enhancing Input-Label Mapping in In-Context Learning with Contrastive Decoding (2025.acl-short)

Challenge:	Dynamic networks can significantly improve the model’s representation power with acceptable computational cost.
Approach:	They propose a partially dynamic network to transform redundant dynamic parameters into static ones and iterative mode partition to partition dynamic and static parameters efficiently.
Outcome:	The proposed network surpasses fully dynamic networks by +0.7% top-1 acc with only 30% dynamic parameters for DY-Conv and +1.9% average score in language understanding with only 50% dynamic parameters.

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Keqin Peng, Liang Ding, Yuanxin Ouyang, Meng Fang, Yancheng Yuan, Dacheng Tao

Chain-of-History Reasoning for Temporal Knowledge Graph Forecasting (2024.findings-acl)

Challenge:	Prior research has found that large language models overlook input-label mapping information in ICL, relying more on their pre-trained knowledge.
Approach:	They propose a novel method that contrasts input-label mappings between positive and negative in-context examples to improve model performance.
Outcome:	The proposed method improves performance on 7 natural language understanding tasks without additional training.

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Yuwei Xia, Ding Wang, Qiang Liu, Liang Wang, Shu Wu, Xiao-Yu Zhang

ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large Language Models (2025.acl-long)

Challenge:	Existing graph-based models excel at capturing structural information within TKGs but lack semantic comprehension abilities.
Approach:	They propose a plug-and-play module to enhance the performance of graph-based TKG models by exploring high-order histories step-by-step.
Outcome:	Experiments on three datasets and backbones show that CoH is effective in capturing high-order historical information for LLMs.

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Hanxing Ding, Shuchang Tao, Liang Pang, Zihao Wei, Jinyang Gao, Bolin Ding, Huawei Shen, Xueqi Cheng

TamEdit: Trajectory-Aware Meta-Learning for Specificity-Preserving Continual Knowledge Editing (2026.acl-long)

Challenge:	Existing approaches to tool learning rely on hand-crafted prompts and natural language reasoning, making multi-step planning difficult and lacking precise error diagnosis and reflection mechanisms.
Approach:	They propose a framework that reformulates tool learning as a code generation task.
Outcome:	The proposed framework achieves superior performance in task completion accuracy and execution reliability compared to existing approaches.

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Shiqiang Tian, Cheng Ding, Qin Chen, Jie Zhou, Liang He

Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging (D18-1)

Challenge:	Existing methods for continual knowledge editing focus on single edits or preventing knowledge forgetting.
Approach:	They propose a meta-learning method that preserves specificity for continual knowledge editing by capturing relationships between different single edits within the trajectory.
Outcome:	Experiments show that TamEdit outperforms baselines in continual editing while preserving general capabilities.

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Tao Gui, Qi Zhang, Jingjing Gong, Minlong Peng, Di Liang, Keyu Ding, Xuanjing Huang

Syntactic Text Processing

Challenge:	Existing POS tagging methods for Twitter use labeled newswire text . however, Twitter users tend to mimic formal media expressions and develop linguistically informal styles.
Approach:	They propose to use newswire text to learn POS tagging for Twitter while twitter users are developing linguistically informal styles.
Outcome:	The proposed method achieves better performance than state-of-the-art methods on three different datasets.

TransGEC: Improving Grammatical Error Correction with Translationese (2023.findings-acl)

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Tao Fang, Xuebo Liu, Derek F. Wong, Runzhe Zhan, Liang Ding, Lidia S. Chao, Dacheng Tao, Min Zhang

KaFT: Knowledge-aware Fine-tuning for Boosting LLMs’ Domain-specific Question-Answering Performance (2025.findings-acl)

Challenge:	Experimental results show that data augmentation improves accuracy over strong baselines.
Approach:	They propose to use translationese as input for GEC data augmentation to overcome stylistic discrepancies . they propose to obtain human-translated texts with a more similar style to non-native texts .
Outcome:	The proposed method improves correction accuracy over strong baselines on four GEC benchmarks.

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Qihuang Zhong, Liang Ding, Xiantao Cai, Juhua Liu, Bo Du, Dacheng Tao

Question Answering

Challenge:	Recent literature reveals that supervised fine-tuning (SFT) is suboptimal for domain-specific question-answering tasks.
Approach:	They propose a query diversification strategy for robust conflict detection and a knowledge-aware fine-tuning approach to effectively boost LLMs’ performance.
Outcome:	The proposed approach improves the model generalization and alleviates the hallucination.

Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction (2024.lrec-main)

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Zepeng Ding, Wenhao Huang, Jiaqing Liang, Yanghua Xiao, Deqing Yang

Information Extraction & Text Mining

Challenge:	Existing large language models can extract triples from simple sentences with few-shot learning or fine-tuning, but they often miss out when extracting from complex sentences.
Approach:	They propose an evaluation-filtering framework that integrates large language models with small models for relational triple extraction tasks.
Outcome:	The proposed framework integrates large language models with small models for relational triple extraction tasks.

UIOrchestra: Generating High-Fidelity Code from UI Designs with a Multi-agent System (2025.findings-emnlp)

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Chuhuai Yue, Jiajun Chai, Yufei Zhang, Zixiang Ding, Xihao Liang, Peixin Wang, Shihai Chen, Wang Yixuan, null Wangyanping, Guojun Yin, Wei Lin

Self-Evolution Knowledge Distillation for LLM-based Machine Translation (2025.coling-main)

Challenge:	Recent advances in large language models have significantly improved automated code generation . however, the translation of complex mobile UI designs into high-fidelity front-end code remains a challenge .
Approach:	They propose a collaborative multi-agent system to reconstruct static single-page apps from mockups.
Outcome:	The proposed system outperforms existing methods in reconstructing complex app pages . the code and data will be released upon paper acceptance .

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Yuncheng Song, Liang Ding, Changtong Zan, Shujian Huang

Robust Knowledge Editing via Explicit Reasoning Chains for Distractor-Resilient Multi-Hop QA (2025.findings-emnlp)

Challenge:	Existing knowledge distillation strategies for large language models minimize output distributions between student and teacher models indiscriminately for each token.
Approach:	They propose a distillation strategy that integrates teacher and one-hot distribution of ground truth into the student distribution as prior knowledge, which promotes the distillation process.
Outcome:	The proposed method brings an average improvement of approximately 1.4 SacreBLEU points across four translation directions in the WMT22 test sets.

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Yuchen Wu, Liang Ding, Li Shen, Dacheng Tao

Robustness in NLP

Challenge:	Large language models encode vast amounts of knowledge but remain static once trained, making timely integration of emerging facts prohibitively expensive via full retraining.
Approach:	They introduce a reasoning-chain-based editing framework that steers a pretrained LLM through four structured stages to filter distractors in a single pass.
Outcome:	The proposed framework steers a pretrained LLM through four structured stages to filter distractors in a single pass.

A Contrastive Cross-Channel Data Augmentation Framework for Aspect-Based Sentiment Analysis (2022.coling-1)

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Bing Wang, Liang Ding, Qihuang Zhong, Ximing Li, Dacheng Tao

SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling (2020.emnlp-main)

Challenge:	Aspect-based sentiment analysis is sensitive to multi-aspect challenges, resulting in multiple aspects in a sentence.
Approach:	They propose a framework that leverages an in-domain generator to construct more multi-aspect samples . they then boost the robustness of ABSA models via contrastive learning on these generated samples ."
Outcome:	The proposed framework outperforms baselines without any augmentations on accuracy and Macro- F1 . the proposed framework can generate more multi-aspect samples and boost the robustness of ABSA models .

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Di Wu, Liang Ding, Fan Lu, Jian Xie

On the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine Translation (2022.coling-1)

Challenge:	Slot filling and intent detection are two main tasks in spoken language understanding systems.
Approach:	They propose a non-autoregressive slot filling model with two-pass iteration mechanism to handle uncoordinated slots problem.
Outcome:	The proposed model significantly outperforms previous models in slot filling task while speeding up decoding.

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Changtong Zan, Liang Ding, Li Shen, Yu Cao, Weifeng Liu, Dacheng Tao

Revisiting Demonstration Selection Strategies in In-Context Learning (2024.acl-long)

Challenge:	Pre-Training (PT) of text representations has been successfully applied to low-resource Neural Machine Translation (NMT) however, it often fails to achieve notable gains on resource-rich NMT on par with its Random-Initialization (RI) counterpart.
Approach:	They propose to combine pre-training and random-initialization techniques to achieve significant improvements in NMT.
Outcome:	The proposed model fusion algorithm can achieve significant improvements on two resource-rich translation benchmarks.

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Keqin Peng, Liang Ding, Yancheng Yuan, Xuebo Liu, Min Zhang, Yuanxin Ouyang, Dacheng Tao

Low-Resource NLP

Challenge:	Large language models (LLMs) have shown an impressive ability to perform a wide range of tasks using in-context learning (ICL).
Approach:	They propose a data- and model-dependent method to select models using in-context learning, TopK + ConE, and propose unified explanations for the effectiveness of previous methods.
Outcome:	The proposed method improves language understanding and generation tasks with different model scales.

Toward Human-Like Evaluation for Natural Language Generation with Error Analysis (2023.acl-long)

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Qingyu Lu, Liang Ding, Liping Xie, Kanjian Zhang, Derek F. Wong, Dacheng Tao

On the Copying Behaviors of Pre-Training for Neural Machine Translation (2021.findings-acl)

Challenge:	Pre-trained language models (PLMs) have been used to evaluate language generation tasks . pretrained error analysis can be used to refine the generated sentence toward higher confidence .
Approach:	They propose to combine pretrained language model based metrics with human-like error analysis to improve sentence confidence.
Outcome:	The proposed method outperforms top-scoring metrics in 19/25 settings.

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Xuebo Liu, Longyue Wang, Derek F. Wong, Liang Ding, Lidia S. Chao, Shuming Shi, Zhaopeng Tu

Redistributing Low-Frequency Words: Making the Most of Monolingual Data in Non-Autoregressive Translation (2022.acl-long)

Challenge:	Existing studies show that initializing NMT models with pre-trained language models (LM) can speed up the model training and boost the model performance.
Approach:	They propose a method to control copying behaviors in NMT models by initializing them with pre-trained language models (LM) they propose to use a metric called copy ratio to control the copying behavior in decoding.
Outcome:	The proposed method improves translation performance by controlling copying behaviors for pre-training based models.

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Liang Ding, Longyue Wang, Shuming Shi, Dacheng Tao, Zhaopeng Tu

VisuoThink: Empowering LVLM Reasoning with Multimodal Tree Search (2025.acl-long)

Challenge:	Knowledge distillation (KD) is the preliminary step for training non-autoregressive translation models, but it can lose important information for translating low-frequency words.
Approach:	They propose a knowledge distillation method which trains NAT student on external monolingual data with AT teacher trained on the original bilingual data.
Outcome:	Extensive experiments on eight WMT benchmarks show that monolingual KD outperforms the standard KD by improving low-frequency word translation without introducing any computational cost.

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Yikun Wang, Siyin Wang, Qinyuan Cheng, Zhaoye Fei, Liang Ding, Qipeng Guo, Dacheng Tao, Xipeng Qiu

Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks (2023.emnlp-main)

Challenge:	Existing approaches to large vision-language models fail to capture interleaved nature of human visual-verbal reasoning processes.
Approach:	They propose a framework that integrates visuospatial and linguistic domains to facilitate multimodal slow thinking by enabling progressive visual-textual reasoning.
Outcome:	Experiments show that VisuoThink significantly improves reasoning capabilities even without fine-tuning.

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Haoqi Zheng, Qihuang Zhong, Liang Ding, Zhiliang Tian, Xin Niu, Changjian Wang, Dongsheng Li, Dacheng Tao

Low-Resource NLP

Challenge:	Text classification tasks often encounter few-shot scenarios with limited labeled data, and addressing data scarcity is crucial.
Approach:	They propose a self-evolution learning (SE) based mixup approach for data augmentation in text classification which generates more adaptive and model-friendly pseudo samples for the model training.
Outcome:	The proposed approach can generate more adaptive and model-friendly pseudo samples for the model training.

ROSE Doesn’t Do That: Boosting the Safety of Instruction-Tuned Large Language Models with Reverse Prompt Contrastive Decoding (2024.findings-acl)

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Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, Dacheng Tao

REVEALER: Reinforcement-Guided Visual Reasoning for Element-Level Text-Image Alignment Evaluation (2026.acl-long)

Challenge:	Existing methods for aligning LLMs output with expected safety require substantial training efforts and expensive computational resources.
Approach:	They propose a method to directly boost the safety of existing instruction-tuned large language models without additional training.
Outcome:	The proposed method improves safety of instruction-tuned large language models without training and requires expensive computational resources.

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FuLin Shi, Wenyi Xiao, Leilei Gan, Liang Ding, null Binchen

On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation (2021.findings-emnlp)

Challenge:	Existing methods for text-to-image alignment evaluation rely on coarse-grained metrics or static Question Answering pipelines that lack fine-grounded interpretability and struggle to reflect human preferences.
Approach:	They propose a reinforcement-guided visual reasoning framework for element-level text-to-image alignment evaluation.
Outcome:	The proposed framework achieves state-of-the-art results on four benchmarks and surpasses the strong proprietary Gemini 3 Pro and Training-based baselines.

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Xuebo Liu, Longyue Wang, Derek F. Wong, Liang Ding, Lidia S. Chao, Shuming Shi, Zhaopeng Tu

Chinese Toxic Language Mitigation via Sentiment Polarity Consistent Rewrites (2025.emnlp-main)

Challenge:	Experimental results show that PT and BT are nicely complementary to each other.
Approach:	They introduce two probing tasks for PT and BT respectively and investigate their complementarity.
Outcome:	The proposed methods establish state-of-the-art on the WMT16 English-Romanian and English-Russian benchmarks.

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Xintong Wang, Yixiao Liu, Jingheng Pan, Liang Ding, Longyue Wang, Chris Biemann

Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction (2024.lrec-main)

Challenge:	Large language models (LLMs) can be effective at rewriting toxic content, but they often default to overly polite rewrites, distorting the emotional tone and communicative intent.
Approach:	They evaluate 17 large language models with variant architectures to evaluate their ability to rewrite toxic content while preserving the speaker's original intent.
Outcome:	The first Chinese detoxification dataset explicitly designed to preserve sentiment polarity is evaluated across five real-world scenarios.

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Ziyang Xu, Keqin Peng, Liang Ding, Dacheng Tao, Xiliang Lu

MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation Evaluators (2025.coling-main)

Challenge:	Recent research shows that pre-trained language models suffer from “prompt bias” in factual knowledge extraction.
Approach:	They propose a representation-based approach to mitigate prompt bias during inference time by querying the model and removing it from its internal representations to generate debiased representations.
Outcome:	The proposed approach corrects the overfitted performance caused by prompt bias and significantly improves prompt retrieval capability.

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Qingyu Lu, Liang Ding, Kanjian Zhang, Jinxia Zhang, Dacheng Tao

CASN:Class-Aware Score Network for Textual Adversarial Detection (2023.acl-long)

Challenge:	Large Language Models (LLMs) have shown significant potential as judges for Machine Translation (MT) quality assessment.
Approach:	They propose a framework that automatically post-edits the original translation based on each error, thereby filtering out non-impactful errors.
Outcome:	The proposed framework improves reliability and quality of error spans against GEMBA-MQM, across eight LLMs in both high- and low-resource languages.

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Rong Bao, Rui Zheng, Liang Ding, Qi Zhang, Dacheng Tao

Robustness in NLP

Challenge:	Existing approaches to counteract adversarial attacks can be divided into two directions, adversarials defense and adversarially detection.
Approach:	They propose a score-based generative method to implicitly model the data distribution using a log-density distribution and supervised contrastive learning to guide the estimation using label information.
Outcome:	The proposed method improves on three text classification tasks on four advanced attack algorithms.

Edit Once, Update Everywhere: A Simple Framework for Cross-Lingual Knowledge Synchronization in LLMs (2025.findings-acl)

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Yuchen Wu, Liang Ding, Li Shen, Dacheng Tao

SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters (2022.findings-emnlp)

Challenge:	Existing methods to update large language models focus on single-language editing or basic multilingual editing, failing to achieve true cross-linguistic knowledge synchronization.
Approach:	They propose a cross-linguistic knowledge democracy edit technique to improve cross-lingual performance.
Outcome:	The proposed method improves cross-lingual performance while maintaining high accuracy in monolingual settings.

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Shwai He, Liang Ding, Daize Dong, Jeremy Zhang, Dacheng Tao

PromptST: Abstract Prompt Learning for End-to-End Speech Translation (2023.emnlp-main)

Challenge:	Pretrain-finetuned models are increasingly complex and require more parameters to match the performance of full fine-tuning.
Approach:	They propose an efficient Adapter Tuning technique that freezes pretrained language models and fine-tunes a few extra modules.
Outcome:	The proposed setting outperforms the standard Adapter Tuning by 80% . the proposed setting is easy to use and has a high sparse ratio .

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Tengfei Yu, Liang Ding, Xuebo Liu, Kehai Chen, Meishan Zhang, Dacheng Tao, Min Zhang

WebCPM: Interactive Web Search for Chinese Long-form Question Answering (2023.acl-long)

Challenge:	Experimental results show that PromptST can improve speech-to-text translation by capturing richer linguistic knowledge.
Approach:	They propose a plug-in prompt-enhanced S2T model that captures richer linguistic knowledge . they use a 10GB linguistic probing benchmark to investigate the fusion of speech and text features .
Outcome:	The proposed model can improve on a strong baseline by capturing richer linguistic knowledge.

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Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, Jie Zhou

Information Retrieval

Challenge:	Long-form question answering requires two procedures: information retrieval and information synthesis.
Approach:	They propose a Chinese long-form question answering dataset called WebCPM . the dataset is based on a web search interface that engages with a search engine in real time .
Outcome:	The proposed dataset generates answers that are no worse than human-written ones . the dataset is the first Chinese LFQA dataset .

DocStruct: A Multimodal Method to Extract Hierarchy Structure in Document for General Form Understanding (2020.findings-emnlp)

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Zilong Wang, Mingjie Zhan, Xuebo Liu, Ding Liang

Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation (2021.acl-long)

Challenge:	Form understanding is a complex task because of the textual contents and organizational structure of forms.
Approach:	They propose to use multimodal methods to extract key-value pairs from forms . they validate their method on two benchmarks and demonstrate their effectiveness .
Outcome:	The proposed method is validated on two benchmarks, MedForm and FUNSD.

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Liang Ding, Longyue Wang, Xuebo Liu, Derek F. Wong, Dacheng Tao, Zhaopeng Tu

MacLaSa: Multi-Aspect Controllable Text Generation via Efficient Sampling from Compact Latent Space (2023.findings-emnlp)

Challenge:	Knowledge distillation (KD) is commonly used to construct synthetic data for training non-autoregressive translation models.
Approach:	They propose to use knowledge distillation to generate training data for non-autoregressive translation models by leveraging pretraining.
Outcome:	The proposed approach achieves 28.2 and 33.9 BLEU points on the WMT14 English-German and WMT16 Romanian-English datasets.

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Hanxing Ding, Liang Pang, Zihao Wei, Huawei Shen, Xueqi Cheng, Tat-Seng Chua

COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)

Challenge:	Existing approaches to multi-aspect controllable text generation require expensive iteration / searching within the discrete text space during the decoding stage, resulting in a degradation of text quality due to the domain discrepancies between different aspects.
Approach:	They propose a framework that estimates compact latent space for multiple aspects and performs efficient Sampling with a fast sampler to eliminate domain discrepancies.
Outcome:	The proposed framework outperforms baselines on attribute relevance and textual quality while maintaining a high inference speed.

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Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin

SHARP: Steering Hallucination in LVLMs via Representation Engineering (2025.emnlp-main)

Challenge:	Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation.
Approach:	They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets.
Outcome:	The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark.

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Junfei Wu, Yue Ding, Guofan Liu, Tianze Xia, Ziyue Huang, Dianbo Sui, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan

3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset (2024.lrec-main)

Challenge:	Large Vision-Language Models (LVLMs) generate responses that are plausible but incorrect or unsupported—commonly referred to as hallucinations.
Approach:	They propose a representation-level intervention framework that modulates hallucination-related features during inference by probing their encoded features.
Outcome:	The proposed framework reduces hallucinations while maintaining the performance and generalization capabilities of Large Vision-Language Models (LVLMs).

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Xinyu Ma, Xuebo Liu, Derek F. Wong, Jun Rao, Bei Li, Liang Ding, Lidia S. Chao, Dacheng Tao, Min Zhang

VCSUM: A Versatile Chinese Meeting Summarization Dataset (2023.findings-acl)

Challenge:	Existing studies have shown that visual information in existing MMT datasets is insufficient, causing models to disregard it and overestimate their capabilities.
Approach:	They propose to use 3AM to create an ambiguity-aware multimodal machine translation dataset.
Outcome:	The proposed dataset includes more ambiguity and a greater variety of captions and images than other MMT datasets.

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Han Wu, Mingjie Zhan, Haochen Tan, Zhaohui Hou, Ding Liang, Linqi Song

Text Generation

Challenge:	Compared to news and chat summarization, meeting summarizing is decelerated by the limited data.
Approach:	They propose a Chinese meeting summarization dataset that provides annotations for each transcript and a set of benchmark models to facilitate further research.
Outcome:	The proposed model can be used to summarize the content of meeting transcripts in Chinese.

Speech Sense Disambiguation: Tackling Homophone Ambiguity in End-to-End Speech Translation (2024.acl-long)

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Tengfei Yu, Xuebo Liu, Liang Ding, Kehai Chen, Dacheng Tao, Min Zhang

H-MAS: Hierarchical Multi-Agent Scheduling for Multi-Tenant LLM Serving (2026.findings-acl)

Challenge:	End-to-end speech translation (ST) models require simultaneous crossmodal and crosslingual transformations to be effective.
Approach:	They propose a homophone-aware contrastive learning approach that integrates a speech-text masking strategy to reduce ambiguity.
Outcome:	The proposed approach achieves SOTA results on BLEU scores on different MuST-C and CoVoST ST tasks, underlining its effectiveness in reducing speech sense ambiguity.

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Yuhan Liu, Cong Xu, Qi Jia, Yihua Wang, Feiyu Chen, Liang Jin, Lu Liu, Yaqian Zhao, Yuting Ding, Xiang Li

Challenge:	Multi-tenant Model-as-a-Service (MaaS) workloads exhibit non-stationarity across multiple time scales . existing request schedulers often rely on a fixed policy that remains unchanged at runtime .
Approach:	They propose a hierarchical multi-agent scheduler that operates in a layered closed loop . they propose to maintain 1.2–3.0 higher Goodput than SGLang and vLLM .
Outcome:	Experiments show that H-MAS achieves 1.2–3.0 higher Goodput than SGLang and vLLM . it maintains more stable QoS under diverse request lengths and heterogeneous SLO targets .

Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training (2023.findings-acl)

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Yibin Lei, Liang Ding, Yu Cao, Changtong Zan, Andrew Yates, Dacheng Tao

A Data-Centric Framework for Composable NLP Workflows (2020.emnlp-demos)

Challenge:	Dense retrievers have impressive performance, but their demand for abundant training data limits their application scenarios.
Approach:	They propose a method which uses unlabeled data to construct pseudo-positive examples from unlabelled data and then contrastively weighs the contrastive loss of different pairs according to the estimated relevance.
Outcome:	The proposed method beats the SOTA unsupervised Contriever model on BEIR and open-domain QA retrieval benchmarks and is a good few-shot learner.

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Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Xiaodan Liang, Teruko Mitamura, Eric Xing, Zhiting Hu

Challenge:	Empirical natural language processing (NLP) systems involve interoperation among multiple components . a wealth of NLP toolkits exist ( 4), such as spaCy, DKPro, CoreNLP.
Approach:	They propose a unified open-source framework that supports fast development of NLP workflows . framework includes processors for NLP tasks, visualization, and annotation .
Outcome:	The framework offers processors for NLP tasks, visualization, and annotation, and is extensible . it is delivered through two modularized yet integratable open-source projects, Forte and Stave .

In-Context Example Retrieval from Multi-Perspectives for Few-Shot Aspect-Based Sentiment Analysis (2024.lrec-main)

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Qianlong Wang, Hongling Xu, Keyang Ding, Bin Liang, Ruifeng Xu

InfoGain-RAG: Boosting Retrieval-Augmented Generation through Document Information Gain-based Reranking and Filtering (2025.emnlp-main)

Challenge:	Existing approaches to solve few-shot aspect-based sentiment analysis (ABSA) are suboptimal for this task because of in-context examples .
Approach:	They propose to retrieve in-context examples for few-shot aspect-based sentiment analysis . they construct positive and negative pairs from three perspectives and train the retriever .
Outcome:	The proposed retrieval framework outperforms baselines on four ABSA datasets.

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Zihan Wang, Zihan Liang, Zhou Shao, Yufei Ma, Huangyu Dai, Ben Chen, Lingtao Mao, Chenyi Lei, Yuqing Ding, Han Li

Information Retrieval

Challenge:	Retrieval-Augmented Generation (RAG) frameworks struggle with identifying whether retrieved documents meaningfully contribute to answer generation.
Approach:	They propose a document-related metric to quantify the contribution of retrieved documents to correct answer generation.
Outcome:	The proposed framework outperforms existing approaches on both single and multiple retrieval paradigms.

Learning from Imperfect Data: Towards Efficient Knowledge Distillation of Autoregressive Language Models for Text-to-SQL (2024.findings-emnlp)

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Qihuang Zhong, Kunfeng Chen, Liang Ding, Juhua Liu, Bo Du, Dacheng Tao

Programming Languages in NLP

Challenge:	Existing text-to-SQL LLMs are computationally expensive and difficult to deploy in real-world applications.
Approach:	They propose to distill a larger teacher model into a smaller student model by using imperfect data to improve the KD.
Outcome:	The proposed method achieves the best tradeoff between performance and efficiency on 5 text-to-SQL benchmarks.

Runaway is Ashamed, But Helpful: On the Early-Exit Behavior of Large Language Model-based Agents in Embodied Environments (2025.findings-emnlp)

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Qingyu Lu, Liang Ding, Siyi Cao, Xuebo Liu, Kanjian Zhang, Jinxia Zhang, Dacheng Tao

DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMs (2025.findings-emnlp)

Challenge:	Experiments with 4 different LLMs across 5 embodied environments show significant efficiency improvements, with only minor drops in agent performance.
Approach:	They propose an intrinsic method that injects exit instructions during generation and an extransic system that verifies task completion to determine when to halt an agent’s trial.
Outcome:	The proposed method injects exit instructions during generation and an exit method verifies task completion to determine when to halt an agent’s trial.

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Xiabin Zhou, Wenbin Wang, Minyan Zeng, Jiaxian Guo, Xuebo Liu, Li Shen, Min Zhang, Liang Ding

Challenge:	Existing KV cache compression methods enforce a fixed pattern, neglecting task-specific characteristics, which hampers the effective retention of essential information while discarding less important tokens.
Approach:	They propose a Task-Aware KV cache mechanism that dynamically adjusts the KV caching size across different layers based on the characteristics of the tasks.
Outcome:	The proposed method surpasses state-of-the-art methods by 11% on the LongBench dataset even under extreme compression (0.9%)

Self-supervised Product Title Rewrite for Product Listing Ads (2022.naacl-industry)

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Xue Zhao, Dayiheng Liu, Junwei Ding, Liang Yao, Mahone Yan, Huibo Wang, Wenqing Yao

Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models (2024.findings-acl)

Challenge:	Existing work has investigated the title optimization for Product Listing Ads (PLAs) however, little work has examined the effectiveness of this method.
Approach:	They propose a method to rewrite product listing ads titles without considering the fluency and information priority.
Outcome:	The proposed solution reduces the cost and improves CTR in the offline test and real-world online test by a large amount.

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Qingyu Lu, Baopu Qiu, Liang Ding, Kanjian Zhang, Tom Kocmi, Dacheng Tao

Self-Evolution Learning for Discriminative Language Model Pretraining (2023.findings-acl)

Challenge:	Recent research shows that large language models (LLMs) perform poorly at segment level.
Approach:	They propose a new prompting method that emulates the commonly accepted human evaluation framework . they will release their code and scripts to facilitate the community .
Outcome:	The proposed method is based on the human evaluation framework MQM and produces explainable and reliable MT evaluations at both the system and segment level.

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Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, Dacheng Tao

Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models (2022.findings-emnlp)

Challenge:	Random masking does not consider the importance of the different words in the sentence meaning, e.g., entity-level masking requires expensive prior knowledge and generally does not use existing model weights.
Approach:	They propose a token masking and learning method that uses a random masking strategy to learn the under-explored tokens.
Outcome:	The proposed method improves linguistic knowledge learning and generalization on 10 tasks.

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Qihuang Zhong, Liang Ding, Li Shen, Peng Mi, Juhua Liu, Bo Du, Dacheng Tao

Towards Making the Most of ChatGPT for Machine Translation (2023.findings-emnlp)

Challenge:	Existing methods for fine-tuning pretrained language models suffer from poor generalization . however, they add a perturbation to each model parameter equally, which is sub-optimal .
Approach:	They propose a sharpness-aware minimization optimization procedure that introduces a Fisher mask to improve the efficiency of SAM.
Outcome:	The proposed method outperforms the vanilla sharpness-aware minimization method on GLUE and SuperGLUE benchmarks.

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Keqin Peng, Liang Ding, Qihuang Zhong, Li Shen, Xuebo Liu, Min Zhang, Yuanxin Ouyang, Dacheng Tao

Context-Aware Cross-Attention for Non-Autoregressive Translation (2020.coling-main)

Challenge:	Prior studies have shown that ChatGPT achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g., low-resourced and distant-language-pairs translation.
Approach:	They propose task-specific prompts and domain-specific prompts which are based on task information and domain information and a task-specific prompt.
Outcome:	The proposed prompts improve the performance of ChatGPT in complex tasks and generate hallucinations for non-English-centric tasks.

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Liang Ding, Longyue Wang, Di Wu, Dacheng Tao, Zhaopeng Tu

The Bitter Lesson of Diffusion Language Models for Agentic Workflows: A Comprehensive Reality Check (2026.acl-long)

Challenge:	Existing studies have shown that non-autoregressive translation models can predict all tokens independently and simultaneously.
Approach:	They propose to enhance signals of neighbour source tokens into conventional cross-attention to address a locality perception problem in NAT cross- attention.
Outcome:	The proposed approach improves translation quality over strong NAT baselines on representative datasets.

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Qingyu Lu, Liang Ding, Kanjian Zhang, Jinxia Zhang, Dacheng Tao

Escaping the Echo Trap: On Credit Assignment Failure in Multi-turn LLM Self-Reflection (2026.acl-long)

Challenge:	Embodied and Tool-Calling agents are effective in planning and complex reasoning, but require causal, precise, and logically grounded reasoning mechanisms to be viable for agentic tasks.
Approach:	They propose a framework that integrates dLLMs as plug-and-play cognitive cores.
Outcome:	The proposed model breaks the sequential latency bottleneck in agentic interactions.

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Linxuan Du, Guangquan Xue, Xiaobo Liang, Qipeng Huang, Yuyang Ding, Xinyu Shi, Zhang Yijun, Ji Qi, Wenpeng Zhu, Juntao Li, Min Zhang

Challenge:	Existing methods for multi-turn self-reflection are limited by the Echo Trap problem . the model is limited by its inherent capabilities and repeats earlier reflections to preserve reward signals .
Approach:	They propose a tree-structured extension of GRPO for multi-turn self-reflection which enables more accurate advantage estimation.
Outcome:	The proposed method mitigates behavior collapse and improves performance across benchmarks.

CogSteer: Cognition-Inspired Selective Layer Intervention for Efficiently Steering Large Language Models (2025.findings-acl)

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Xintong Wang, Jingheng Pan, Liang Ding, Longyue Wang, Longqin Jiang, Xingshan Li, Chris Biemann

Self-Attention with Cross-Lingual Position Representation (2020.acl-main)

Challenge:	Large Language Models (LLMs) achieve excellent performance through pretraining on extensive data.
Approach:	They propose an efficient selective layer intervention based on parameter-efficient fine-tuning methods to select the optimal steering layer to modulate LLM semantics.
Outcome:	The proposed approach is based on a model-agnostic framework and is safe to deploy.

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Liang Ding, Longyue Wang, Dacheng Tao