Papers by Feng Qin

Adaptive Multi-pass Decoder for Neural Machine Translation (D18-1)

Challenge:	Recent studies show that pre-trained language models can produce informative and fluent text with the help of large-scale datasets, but they suffer insufficient learning problem with limited training data.
Approach:	They propose to use table transformation module with template to rewrite structured table in natural language as input for GPT-2 and exploit multi-task learning with two auxiliary tasks to preserve table’s structural information.
Outcome:	The proposed model outperforms existing systems on most few-shot settings.

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Xinwei Geng, Xiaocheng Feng, Bing Qin, Ting Liu

Gradient Consistency-based Parameter Allocation for Multilingual Neural Machine Translation (2024.lrec-main)

Challenge:	End-to-end neural machine translation (NMT) has attracted increasing attention in recent years.
Approach:	They propose an adaptive multi-pass decoder which introduces a flexible multi- pass polishing mechanism to extend the capacity of NMT via reinforcement learning.
Outcome:	The proposed architecture improves Chinese-English translation with 1.55 BLEU . the proposed architecture adopts a flexible multi-pass polishing mechanism .

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Wenshuai Huo, Xiaocheng Feng, Yichong Huang, Chengpeng Fu, Hui Wang, Bing Qin

SciPedia: Unlocking the Value of Scientific Data for Pre-training (2026.acl-long)

Challenge:	Multilingual neural machine translation models are often prone to parameter interference . a common problem is that the model compromises with the language diversity to find a solution .
Approach:	They propose a method that allocates parameters based on consistency between the gradients of the individual language and the average gradient.
Outcome:	The proposed method reduces parameter interference and improves translation quality.

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Yiwei Qin, Zhen Huang, Tiantian Mi, Weiye Si, Qipeng Guo, Siyuan Feng, Pengfei Liu

GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News Summarization (2024.emnlp-main)

Challenge:	High-quality scientific data is critical for advancing LLMs, yet academic literature remains underutilized.
Approach:	They construct a large-scale raw scientific corpus but identify a critical Learnability Gap . they develop a multi-stage pipeline featuring content cleaning and pedagogical augmentation .
Outcome:	The proposed approach boosts average performance by +2.12 (3B) and +2.95 (7B) on in-domain tasks.

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Yangfan Ye, Xiachong Feng, Xiaocheng Feng, Weitao Ma, Libo Qin, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin

Improving Controllable Text Generation with Position-Aware Weighted Decoding (2022.findings-acl)

Challenge:	Current studies focus on single-language or single-document tasks for news summarization . lack of a benchmark inhibits researchers from adequately studying this invaluable problem.
Approach:	They propose a novel task that unifies Multi-lingual, Cross-lingual and Multi-document Summarization into one task.
Outcome:	The proposed task encapsulates the real-world requirements all-in-one and is validated by extensive analysis.

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Yuxuan Gu, Xiaocheng Feng, Sicheng Ma, Jiaming Wu, Heng Gong, Bing Qin

Learning to Rewrite for Non-Autoregressive Neural Machine Translation (2021.emnlp-main)

Challenge:	Controllable text generation is a challenging task in natural language generation, which aims to generate diverse text related to specified attributes.
Approach:	They propose a framework that uses a lightweight controller to adjust bias signals from the controller at different decoding positions.
Outcome:	Experiments on positive sentiment control, topic control, and language detoxification show the proposed framework works on 4 SOTA models.

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Xinwei Geng, Xiaocheng Feng, Bing Qin

Adaptive Backtracking for Privacy Protection in Large Language Models (2026.findings-acl)

Challenge:	Existing non-autoregressive neural machine translations have poor inference speed but weak recognition of erroneous translation pieces.
Approach:	They propose an architecture to explicitly learn to rewrite the erroneous translation pieces.
Outcome:	The proposed architecture can achieve better performance while significantly reducing decoding time.

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Zhihao Yao, Yuxuan Gu, Xiachong Feng, Weitao Ma, Bo Li, Xiaocheng Feng, Bing Qin

Hierarchical Catalogue Generation for Literature Review: A Benchmark (2023.findings-emnlp)

Challenge:	Existing privacy protection methods are prone to privacy leakage, but they are not effective in ensuring the privacy of users.
Approach:	They propose to capture latent leakage tendency of large language models during generation process and to construct a new benchmark for personal information.
Outcome:	The proposed method improves privacy by up to 14% over strong baselines against adversarial attacks, avoiding the degradation of response utility.

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Kun Zhu, Xiaocheng Feng, Xiachong Feng, Yingsheng Wu, Bing Qin

Challenge:	Scientific literature review generation aims to extract and organize important information from an abundant collection of reference papers and produces corresponding reviews while lacking a clear and logical hierarchy.
Approach:	They propose a task to generate a hierarchical catalogue of a review paper given various references by using a database of 7.6k literature review catalogues and 389k reference papers.
Outcome:	The proposed method produces a hierarchical catalogue of a review paper given various references.

Contrastive Learning with Generated Representations for Inductive Knowledge Graph Embedding (2023.findings-acl)

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Qian Li, Shafiq Joty, Daling Wang, Shi Feng, Yifei Zhang, Chengwei Qin

A Two-Stage Framework with Self-Supervised Distillation for Cross-Domain Text Classification (2024.lrec-main)

Challenge:	Existing methods for inductive knowledge Graphs are limited by sparsity and implicit transfer.
Approach:	They propose a Contrastive Learning framework with graph guided Variational autoencoder on Meta-KGs to capture and transfer entities.
Outcome:	The proposed framework outperforms state-of-the-art methods with extensive experiments.

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Yunlong Feng, Bohan Li, Libo Qin, Xiao Xu, Wanxiang Che

Unifying the Convergences in Multilingual Neural Machine Translation (2022.emnlp-main)

Challenge:	Existing work on cross-domain text classification relies on domain-invariant features or task-agnostic features.
Approach:	They propose a two-stage framework for cross-domain text classification that leverages or reuses rich labeled data from the source domain and unlabeled data in the target domain.
Outcome:	The proposed framework achieves state-of-the-art on a public cross-domain text classification benchmark.

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Yichong Huang, Xiaocheng Feng, Xinwei Geng, Bing Qin

Large Language Model-based Human-Agent Collaboration for Complex Task Solving (2024.findings-emnlp)

Challenge:	Existing approaches to multilingual neural machine translation are overfitting and inconsistency is ignored .
Approach:	They propose a training strategy that picks up language-specific best checkpoints for each language pair to teach the current model on the fly.
Outcome:	The proposed training strategy alleviates convergence inconsistency and achieves state-of-the-art on language pairs.

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Xueyang Feng, Zhi-Yuan Chen, Yujia Qin, Yankai Lin, Xu Chen, Zhiyuan Liu, Ji-Rong Wen

Unlocking Multilingual Reasoning Capability of LLMs and LVLMs through Representation Engineering (2026.acl-long)

Challenge:	Recent advances in large language models have led to the development of LLM-based autonomous agents.
Approach:	They propose a Reinforcement Learning-based Human-Agent Collaboration method which trains a policy model to determine the most opportune stages for human intervention within the task-solving process.
Outcome:	The proposed method improves human-agent collaboration significantly through well-planned, limited human intervention.

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Qiming Li, Xiaocheng Feng, Yixuan Ma, Ruihan Chen, Zihe Tong, Zekai Ye, Xiachong Feng, Libo Qin, Haoyu Ren, Kun Chen, Yunfei Lu, Dandan Tu, Bing Qin

From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)

Challenge:	Existing approaches to enhance multilingual reasoning capabilities rely on costly multilingual training or employ prompting with external translation tools.
Approach:	They propose a training-free inference-time method to enhance multilingual reasoning capabilities via Representation Engineering without additional training data or tools.
Outcome:	The proposed method outperforms existing methods on four reasoning benchmarks in English and Thai and Swahili.

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Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin

Alleviating Hallucinations from Knowledge Misalignment in Large Language Models via Selective Abstention Learning (2025.acl-long)

Challenge:	rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research.
Approach:	They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication.
Outcome:	The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication.

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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yuxuan Gu, Yangfan Ye, Liang Zhao, Weihong Zhong, Baoxin Wang, Dayong Wu, Guoping Hu, Lingpeng Kong, Tong Xiao, Ting Liu, Bing Qin

CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention (2025.acl-long)

Challenge:	Large language models (LLMs) suffer from severe hallucination issues due to the knowledge misalignment between the pre-training stage and the supervised fine-tuning stage.
Approach:	They propose a training objective with an abstention mechanism that selectively rejects tokens that misalign with the desired knowledge distribution via a special [REJ] token.
Outcome:	The proposed model selectively rejects tokens that misalign with the desired knowledge distribution via a special [REJ] token.

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Zekai Ye, Qiming Li, Xiaocheng Feng, Libo Qin, Yichong Huang, Baohang Li, Kui Jiang, Yang Xiang, Zhirui Zhang, Yunfei Lu, Duyu Tang, Dandan Tu, Bing Qin

OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding (2023.acl-demo)

Challenge:	Large Vision-Language Models (LVLMs) have impressive multimodal abilities but remain prone to multilingual object hallucination.
Approach:	They propose a cross-lingual attention intervention method to mitigate multilingual object hallucination in LVLMs by aligning attention patterns.
Outcome:	The proposed method improves 13.56% (up to 30%) on the POPE and 21.75% on the hallucination subsets across languages.

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Libo Qin, Qiguang Chen, Xiao Xu, Yunlong Feng, Wanxiang Che

Aligning Translation-Specific Understanding to General Understanding in Large Language Models (2024.emnlp-main)

Challenge:	Spoken Language Understanding (SLU) is a task-oriented dialogue system . open-source toolkit provides a unified, modularized, and extensible toolkit for SLU .
Approach:	They introduce an open-source toolkit to provide a unified toolkit for spoken language understanding.
Outcome:	The proposed toolkit unifies 10 models for both single-intent and multi-intention scenarios.

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Yichong Huang, Baohang Li, Xiaocheng Feng, Wenshuai Huo, Chengpeng Fu, Ting Liu, Bing Qin

Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring (2025.naacl-long)

Challenge:	Large Language models (LLMs) have remarkable abilities in understanding complex texts . however, understanding misalignment leads to LLMs mistakenly translating complex concepts .
Approach:	They propose a translation process that aligns the translation-specific understanding with the general understanding to improve translation quality and reduce translation literalness.
Outcome:	The proposed translation process improves translation quality and reduces translation literalness by -25% -51%.

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Honglin Mu, Han He, Yuxin Zhou, Yunlong Feng, Yang Xu, Libo Qin, Xiaoming Shi, Zeming Liu, Xudong Han, Qi Shi, Qingfu Zhu, Wanxiang Che

MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios (2025.findings-acl)

Challenge:	Existing black-box jailbreak methods often rely on model feedback . existing methods may be intercepted by content moderators during the search process .
Approach:	They propose a method that guides malicious prompt construction by local training a mirror model of the target black-box model through benign data distillation.
Outcome:	The proposed method achieves a 92% attack success rate and 80% stealth rate on a subset of AdvBench.

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JinYang Huang, Xiachong Feng, Qiguang Chen, Hanjie Zhao, Zihui Cheng, Jiesong Bai, Jingxuan Zhou, Min Li, Libo Qin

Programming Languages in NLP

Challenge:	MLDebugging is a benchmark designed to assess debugging challenges within multi-library Python code.
Approach:	They propose to introduce a benchmark to assess debugging challenges within multi-library Python code using 126 Python libraries.
Outcome:	The proposed benchmark covers 126 Python libraries and a wide range of multi-library code issues.

Context-Aware Hierarchical Taxonomy Generation for Scientific Papers via LLM-Guided Multi-Aspect Clustering (2025.emnlp-main)

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Kun Zhu, Lizi Liao, Yuxuan Gu, Lei Huang, Xiaocheng Feng, Bing Qin

Text Clustering

Challenge:	Existing taxonomy construction methods lack coherence and granularity . Existing approaches rely on manual or narrowly defined schemes .
Approach:	They propose a context-aware hierarchical taxonomy generation framework that integrates LLMs with dynamic clustering.
Outcome:	The proposed method outperforms existing methods in taxonomy coherence, granularity, and interpretability.

Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models (2024.acl-long)

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Weihong Zhong, Xiaocheng Feng, Liang Zhao, Qiming Li, Lei Huang, Yuxuan Gu, Weitao Ma, Yuan Xu, Bing Qin

Align Attention Heads Before Merging Them: An Effective Way for Converting MHA to GQA (2025.findings-emnlp)

Challenge:	Large Vision-Language Models (LVLMs) suffer from multimodal hallucinations . however, the generated hallucines could influence the models’ subsequent generation .
Approach:	They propose a framework to evaluate LVLMs' behaviors when encountering generated hallucinations and a method to revise the output distribution of LVLs with the one derived from the residual visual input.
Outcome:	The proposed framework reduces the performance of open-source LVLMs by 31%, indicating that they are prone to accept the generated hallucinations and make false claims that they would not have supported without distractions.

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Qingyun Jin, Xiaohui Song, Feng Zhou, Zengchang Qin

Challenge:	Large language models (LLMs) have demonstrated exceptional performance across diverse natural language processing tasks.
Approach:	They propose a method for converting multi-head attention into grouped-query attention with any compression ratio of KV heads.
Outcome:	The proposed method can compress up to 87.5% KV heads of LLaMA2-7B model and 75% Kv heads of Sheared-LLa MA-1.3B with acceptable performance degradation.

Breaking Language Preference in Multilingual RAG via Language-Controllable Retrieval and Language-Agnostic Reasoning (2026.findings-acl)

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Wenshuai Huo, Xiaocheng Feng, Baohang Li, Chengpeng Fu, Yichong Huang, Hui Wang, Bing Qin

OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Models (2026.acl-long)

Challenge:	Existing approaches to improve retrieval accuracy and generation quality of large language models suffer from language preference.
Approach:	They propose a framework that explicitly disentangles multilingual RAG into language-controllable retrieval and language-agnostic reasoning.
Outcome:	Experimental results show that the proposed approach outperforms baselines across multilingual benchmarks.

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Qiguang Chen, Chengyu Luan, Jiajun Wu, Qiming Yu, Yi Yang, Yizhuo Li, Jingqi Tong, Xiachong Feng, Libo Qin, Wanxiang Che

N-LTP: An Open-source Neural Language Technology Platform for Chinese (2021.emnlp-demo)

Challenge:	Existing multimodal reasoning benchmarks for large vision-language models emphasize single-image analysis and fail to exploit contextual information across multiple images.
Approach:	They propose a benchmark to evaluate Olympiad-level reasoning when evidence is distributed over multiple images.
Outcome:	The proposed model outperforms existing models on bi-image Olympiads and Gemini-3-Pro on multimodal Olympiad-level reasoning tasks.

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Wanxiang Che, Yunlong Feng, Libo Qin, Ting Liu

Event Representation Learning with Multi-Grained Contrastive Learning and Triple-Mixture of Experts (2024.lrec-main)

Challenge:	Existing tools that teach an independent model for each task are not supported in Chinese.
Approach:	They propose an open-source neural language platform supporting six Chinese NLP tasks . source code, documentation, and pre-trained models are available at https://github.com/hit-SCIR/ltp .
Outcome:	The proposed platform supports six Chinese NLP tasks.

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Tianqi Hu, Lishuang Li, Xueyang Qin, Yubo Feng

Probing and Boosting Large Language Models Capabilities via Attention Heads (2025.emnlp-main)

Challenge:	Existing methods for learning event representations fail to capture hidden feature information between events.
Approach:	They propose a method that extends the random masked language model by incorporating a specialized MLM to capture different grammatical structures within events.
Outcome:	The proposed method outperforms baselines in hard similarity and transitive sentence similarity tasks, highlighting the superiority of the proposed method.

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Dezhi Zhao, Xin Liu, Xiaocheng Feng, Hui Wang, Bing Qin

Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits (2025.coling-main)

Challenge:	Existing approaches to identifying capabilities rely on external signals with limited structural grounding . emergence of specific capabilities remains poorly understood .
Approach:	They propose a lightweight approach that links LLM capabilities to internal components by identifying correspondences at the level of attention heads.
Outcome:	The proposed approach improves accuracy on MMLU and BBH by 1 to 1.5 points over gradient-based method and 5 to 6 points over other intermediate-state baselines.

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Bohan Li, Jiannan Guan, Longxu Dou, Yunlong Feng, Dingzirui Wang, Yang Xu, Enbo Wang, Qiguang Chen, Bichen Wang, Xiao Xu, Yimeng Zhang, Libo Qin, Yanyan Zhao, Qingfu Zhu, Wanxiang Che

Fine-Mem: Fine-Grained Feedback Alignment for Long-Horizon Memory Management (2026.acl-long)

Challenge:	Existing data on MBTI personality detection are based on self-reported labels and fail to capture the full range of population personality traits.
Approach:	They construct a manually annotated MBTI personality detection dataset with soft labels under the guidance of psychologists and use them to identify the task.
Outcome:	The MBTIBench is the first manually annotated MBti personality detection dataset with soft labels under the guidance of psychologists.

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Weitao Ma, Xiaocheng Feng, Lei Huang, Xiachong Feng, Zhanyu Ma, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He, Bing Qin

One for All: Update Parameterized Knowledge Across Multiple Models with Once Edit (2025.acl-long)

Challenge:	Existing approaches to memory management rely on final task performance as the primary reward, resulting in severe reward sparsity and ineffective credit assignment.
Approach:	They propose a framework for fine-grained feedback alignment using a Chunk-level step reward and Evidence-Anchored Reward Attribution to redistribute global rewards based on memory items utilized as evidence in reasoning.
Outcome:	The proposed framework outperforms baselines and supports generalization across different model configurations and backbones.

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Weitao Ma, Xiyuan Du, Xiaocheng Feng, Lei Huang, Yichong Huang, Huiyi Zhang, Xiaoliang Yang, Baohang Li, Xiachong Feng, Ting Liu, Bing Qin

Green & Sustainable NLP

Challenge:	Existing methods for modifying large language models focus on individual models, resulting in errors and hallucinations.
Approach:	They propose an ensemble-based approach that employs a plug-in model as the editing module and a dynamic weight mechanism to enhance its effectiveness.
Outcome:	The proposed approach outperforms existing methods while achieving superior editing efficiency.

Question Tells You Where the Answer Is: Intention-aware Long-Context KV Cache Compression (2026.acl-long)

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Liang Zhao, Xiaocheng Feng, Weihong Zhong, Lei Huang, Kun Zhu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin

Challenge:	Recent methods to reduce the KV cache size fail to identify crucial KVs for generation while excluding others accurately, resulting in severe information loss.
Approach:	They propose an intention-aware KV cache eviction method that identifies and retains crucial KVs according to the attention distribution of intention, which semantically reflects the user’s goal and determines which part of the context is relevant.
Outcome:	The proposed method can maintain the model performance while reducing the KV cache size from 128K to 2K, leading to a 6.3x increase in decoding speed and 7.8x enhancement in memory efficiency compared to the default setting.

AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)

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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song

Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues (2022.acl-long)

Challenge:	Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage.
Approach:	They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas.
Outcome:	The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality.

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Qingxiu Dong, Ziwei Qin, Heming Xia, Tian Feng, Shoujie Tong, Haoran Meng, Lin Xu, Zhongyu Wei, Weidong Zhan, Baobao Chang, Sujian Li, Tianyu Liu, Zhifang Sui

Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement (2024.findings-emnlp)

Challenge:	Existing work in vision language cross-modal reasoning uses binary or multi-choice classification based on source image and textual query.
Approach:	They propose a task where a textual premise is the background presumption on each source image.
Outcome:	The proposed task is based on a dataset of 15,360 movie screenshots and human-curated premise templates from 6 pre-defined categories.

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Yunlong Feng, Dechuan Teng, Yang Xu, Honglin Mu, Xiao Xu, Libo Qin, Qingfu Zhu, Wanxiang Che

Enhancing Safe and Controllable Protein Generation via Knowledge Preference Optimization (2025.acl-long)

Challenge:	Decompilation is the process of converting compiled code back into a high-level programming language for analysis when source code is unavailable.
Approach:	They propose two methods to improve decompilation performance without fine-tuning and fine-grained alignment enhancement to achieve further improvements.
Outcome:	The proposed methods achieved a Re-Executability performance improvement of approximately 3.90% on the Decompile-Eval benchmark, establishing a new state-of-the-art performance of 52.41%.

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Yuhao Wang, Keyan Ding, Kehua Feng, Zeyuan Wang, Ming Qin, Xiaotong Li, Qiang Zhang, Huajun Chen

Structured Data in NLP

Challenge:	Protein language models pose significant risks of generating harmful sequences, e.g., viral transmissibility, drug resistance, environmental imbalances, public health crises, etc.
Approach:	They propose a protein-based model that integrates prior knowledge via a Protein Safety Knowledge Graph to minimize the risk of generating harmful sequences.
Outcome:	The proposed framework reduces the likelihood of producing hazardous sequences while maintaining high functionality.

Towards Higher Pareto Frontier in Multilingual Machine Translation (2023.acl-long)

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Yichong Huang, Xiaocheng Feng, Xinwei Geng, Baohang Li, Bing Qin

Enabling Unsupervised Neural Machine Translation with Word-level Visual Representations (2023.findings-emnlp)

Challenge:	Existing Pareto optimization approaches are limited by the long-tailed distribution of multilingual corpora.
Approach:	They propose a Pareto mutual distillation framework that pushes the Paret frontier outwards rather than making trade-offs.
Outcome:	The proposed framework pushes the Pareto frontier outwards rather than making trade-offs, the authors show.

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Chengpeng Fu, Xiaocheng Feng, Yichong Huang, Wenshuai Huo, Hui Wang, Bing Qin, Ting Liu

Improving Language Model Reasoning with Self-motivated Learning (2024.lrec-main)

Challenge:	Unsupervised neural machine translation methods have been observed to make particular errors in comparison to supervised machine translation, such as confusing nouns that pertain to the same semantic category.
Approach:	They propose a method that incorporates images at the word level to augment lexical mappings.
Outcome:	Experiments on a multi-lingual dataset show that the proposed method generates more accurate translations with only monolingual data.

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Yunlong Feng, Yang Xu, Libo Qin, Yasheng Wang, Wanxiang Che

Improved Visual Story Generation with Adaptive Context Modeling (2023.findings-acl)

Challenge:	Large-scale high-quality training data is important for improving the performance of models.
Approach:	They propose a framework that motivates the model to automatically generate rationales on existing datasets and improves the performance of reasoning through reinforcement learning.
Outcome:	The proposed model outperforms InstructGPT on multiple reasoning datasets and outperformed InstructGPT on other datasets.

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Zhangyin Feng, Yuchen Ren, Xinmiao Yu, Xiaocheng Feng, Duyu Tang, Shuming Shi, Bing Qin

Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation (2026.acl-long)

Challenge:	a recent work shows that diffusion models generate images of high resolution and semantic consistency to text prompts.
Approach:	They propose a method that uses adaptive context modeling to improve leading system . they evaluate their method on pororoSV and FlintstonesSV datasets .
Outcome:	The proposed method achieves state-of-the-art FID scores on pororo and Flintstones datasets.

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Zekun Yuan, Yangfan Ye, Xiaocheng Feng, Baohang Li, Qichen Hong, Yunfei Lu, Dandan Tu, Bing Qin

Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time) (D19-1)

Challenge:	Existing studies on large language models focus on literal-level translation quality, such as adequacy and fluency.
Approach:	They propose a Culture-Aware Novel-Driven Parallel Dataset for Machine Translation and a multi-dimensional evaluation framework for assessing cultural translation quality.
Outcome:	The proposed model improves evaluation reliability in LLM-as-a-judge scenarios under culture-aware constraints.

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Heng Gong, Xiaocheng Feng, Bing Qin, Ting Liu

PLaD: Preference-based Large Language Model Distillation with Pseudo-Preference Pairs (2024.findings-acl)

Challenge:	Seq2Seq models for table-to-text generation have achieved remarkable progress, but modeling table representation in one dimension is inadequate.
Approach:	They propose to model each table cell considering other records in the same row and to enrich table’s representation by modeling each cell in context of other cells in the similar column or with historical data respectively.
Outcome:	The proposed model outperforms baseline and state-of-the-art models on ROTOWIRE, a benchmark dataset of NBA basketball games.

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Rongzhi Zhang, Jiaming Shen, Tianqi Liu, Haorui Wang, Zhen Qin, Feng Han, Jialu Liu, Simon Baumgartner, Michael Bendersky, Chao Zhang

CodeBERT: A Pre-Trained Model for Programming and Natural Languages (2020.findings-emnlp)

Challenge:	Knowledge distillation (KD) is a technique for transferring expertise from large teacher models to compact student models with reduced memory footprints and inference costs.
Approach:	They propose to transfer knowledge from large teacher models to compact student models by exploiting teacher-student capacity discrepancies to generate pseudo-preference pairs where teacher outputs are preferred over student outputs.
Outcome:	The proposed framework exploits teacher-student capacity discrepancy to generate pseudo-preference pairs where teacher outputs are preferred over student outputs.

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Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou

Semantic Parsing with Syntax- and Table-Aware SQL Generation (P18-1)

Challenge:	Large pre-trained models have improved performance on a variety of natural language processing tasks.
Approach:	They develop a bimodal pre-trained model for programming language (PL) and natural language (NL) it incorporates a hybrid objective function that detects replaced tokens from generators.
Outcome:	The proposed model performs better on two NL-PL applications by fine-tuning model parameters.

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Yibo Sun, Duyu Tang, Nan Duan, Jianshu Ji, Guihong Cao, Xiaocheng Feng, Bing Qin, Ting Liu, Ming Zhou

Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding (2024.findings-emnlp)

Challenge:	Existing approaches generate a SQL query word-by-word but results are incorrect or not executable due to mismatch between question words and table contents.
Approach:	They propose a generative model to map natural language questions into SQL queries.
Outcome:	The proposed model significantly improves state-of-the-art execution accuracy from 69.0% to 74.4% on a large question- SQL dataset.

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Liang Zhao, Xiachong Feng, Xiaocheng Feng, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin, Ting Liu

Length Controlled Generation for Black-box LLMs (2025.acl-long)

Challenge:	Existing methods to enhance length extrapolation of large language models have been developed, but a systematic survey is lacking.
Approach:	They propose to examine the effects of positional encoding on length extrapolation.
Outcome:	The proposed methods improve the extrapolation of large language models, but they are still lacking a systematic survey.

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Yuxuan Gu, Wenjie Wang, Xiaocheng Feng, Weihong Zhong, Kun Zhu, Lei Huang, Ting Liu, Bing Qin, Tat-Seng Chua

The NLP Task Effectiveness of Long-Range Transformers (2023.eacl-main)

Challenge:	Existing length control methods involve fine-tuning the parameters of LLMs, which is inefficient and suboptimal for practical use.
Approach:	They propose an iterative sampling framework that regulates LLMs to generate length-constrained text without modifying the underlying parameters.
Outcome:	The proposed method achieves 100% success rates on Llama3.1 tasks with minimal additional computational overhead.

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Guanghui Qin, Yukun Feng, Benjamin Van Durme

x1: Learning to Think Adaptively Across Languages and Cultures (2026.findings-acl)

Challenge:	Existing benchmarks on long-range attention models have not been sufficient to develop efficient Transformers and their practical application on complex NLP tasks.
Approach:	They propose to benchmark 7 Transformer variants on 5 difficult NLP tasks and 7 datasets to examine their capacity for long-range attention.
Outcome:	The proposed models have advantages on content selection and query-guided decoding, but they come with previously unrecognized drawbacks such as insufficient attention to distant tokens and accumulated approximation error.

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Yangfan Ye, Xiaocheng Feng, Xiachong Feng, Yichong Huang, Zekun Yuan, Lei Huang, Weitao Ma, Qichen Hong, Yunfei Lu, Dandan Tu, Bing Qin

Controllable Text Generation via Probability Density Estimation in the Latent Space (2023.acl-long)

Challenge:	Existing large language models (LLMs) ignore this diversity by reasoning in a single dominant language.
Approach:	They propose a family of reasoning models that can adaptively reason in an advantageous language on a per-instance basis.
Outcome:	The proposed model can reason in a single dominant language on a per-instance basis.

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Yuxuan Gu, Xiaocheng Feng, Sicheng Ma, Lingyuan Zhang, Heng Gong, Weihong Zhong, Bing Qin

Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization (2021.acl-long)

Challenge:	Existing control approaches cannot effectively model complex space with diverse attributes, high dimensionality, and asymmetric structure, leaving subsequent controls unsatisfactory.
Approach:	They propose a control framework using probability density estimation in the latent space and an invertible transformation function that maps the complex distributions to simple Gaussian distributions in the prior space.
Outcome:	The proposed method outperforms baselines on attribute relevance and text quality, achieving a new SOTA.

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Xiachong Feng, Xiaocheng Feng, Libo Qin, Bing Qin, Ting Liu

Dialogue Systems & Conversational Agents

Challenge:	Existing dialogue summarization systems encode text with a number of general semantic features, but these are often not available in open-domain tools.
Approach:	They propose to use DialoGPT to label three types of features on two datasets . they propose to employ pre-trained and non-pre-tried models as dialogue annotators .
Outcome:	The proposed method improves on two dialogue summarization datasets and achieves state-of-the-art performance.

WebAnchor: Anchoring Agent Planning to Stabilize Long-Horizon Web Reasoning (2026.findings-acl)

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Xinmiao Yu, Liwen Zhang, Xiaocheng Feng, Pengjun Xie, Jingren Zhou, Bing Qin, Yong Jiang

Xinference: Making Large Model Serving Easy (2024.emnlp-demo)

Challenge:	Existing methods for reinforcement learning (RL)-based agents struggle with long-horizon planning and strategy coherence.
Approach:	They propose a reinforcement learning framework that decouples planning and execution.
Outcome:	The proposed framework outperforms baseline and first-step RL frameworks on four benchmarks.

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Weizheng Lu, Lingfeng Xiong, Feng Zhang, Xuye Qin, Yueguo Chen

Information Extraction & Text Mining

Challenge:	Open-source large models are rapidly catching up with the closed-source models . however, many current inference tools are not as simple and convenient to use.
Approach:	They develop an open-source library to simplify the deployment and management of large models.
Outcome:	The proposed library outperforms open-source models and offers high throughput and low latency.

ImplicitMemBench: Measuring Unconscious Behavioral Adaptation in Large Language Models (2026.acl-long)

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Chonghan Qin, Xiachong Feng, Weitao Ma, Xiaocheng Feng, Lingpeng Kong

Two-Stage Parameter Alignment for Multi-LoRA Merging in Large Language Models (2026.findings-acl)

Challenge:	Existing memory benchmarks for LLMs evaluate explicit recall of facts, yet overlook implicit memory where experience becomes automated behavior without conscious retrieval.
Approach:	They propose a benchmark that evaluates implicit memory using three constructs from non-declarative memory.
Outcome:	The new benchmark reframes evaluation from "what agents recall" to "what they automatically enact" no model exceeds 66% overall, with top performers far below human baselines .

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Zijian Li, Xiachong Feng, Weitao Ma, Yichong Huang, Xiaocheng Feng, Bing Qin

Robustness in NLP

Challenge:	Current general model merging methods are prone to parameter interference problems . a novel two-stage parameter alignment framework is proposed to address this problem .
Approach:	They propose a two-stage parameter alignment framework that integrates low-rank LoRAs . they propose to reduce the computational complexity of existing methods by preserving fine-grained functions .
Outcome:	The proposed framework exhibits greater robustness than other methods in high-rank and high-interference scenarios while preserving fine-grained functions.

A Distributional Lens for Multi-Aspect Controllable Text Generation (2022.emnlp-main)

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Yuxuan Gu, Xiaocheng Feng, Sicheng Ma, Lingyuan Zhang, Heng Gong, Bing Qin

Stratagem: Learning Transferable Reasoning via Trajectory-Modulated Game Self-Play (2026.acl-long)

Challenge:	Existing methods for multi-aspect control suffer from attribute degeneration due to mutual interference of these controllers.
Approach:	They propose to use attribute fusion to find the intersections of multiple attributes as their combination for generation.
Outcome:	The proposed method outperforms baselines on attribute relevance and text quality and achieves the SOTA.

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Xiachong Feng, Deyi Yin, Xiaocheng Feng, Yi Jiang, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Qiming Li, Yuxuan Gu, Bing Qin, Lingpeng Kong

Temporal Cognitive Tree: A Hierarchical Modeling Approach for Event Temporal Relation Extraction (2024.findings-emnlp)

Challenge:	Existing self-play approaches to developing general reasoning in language models rely on terminal game outcomes.
Approach:	They propose a game-based reasoning transfer model that addresses two barriers to reasoning transfer.
Outcome:	The proposed model improves mathematical reasoning, general reasoning, and code generation benchmarks.

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Wanting Ning, Lishuang Li, Xueyang Qin, Yubo Feng, Jingyao Tang

Information Extraction & Text Mining

Challenge:	Recent studies focus on locating relative position of event pairs on timeline . hierarchical modeling approach neglects multidimensional information in temporal relation and hierarchy of reasoning.
Approach:	They propose a novel hierarchical modeling approach that mimics human logical reasoning by introducing a Temporal Cognitive Tree.
Outcome:	The proposed model outperforms existing methods on TB-Dense and MATRES datasets.

CC-Tuning: A Cross-Lingual Connection Mechanism for Improving Joint Multilingual Supervised Fine-Tuning (2025.acl-long)

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Yangfan Ye, Xiaocheng Feng, Zekun Yuan, Xiachong Feng, Libo Qin, Lei Huang, Weitao Ma, Yichong Huang, Zhirui Zhang, Yunfei Lu, Xiaohui Yan, Duyu Tang, Dandan Tu, Bing Qin

Advancing Large Language Model Attribution through Self-Improving (2024.emnlp-main)

Challenge:	Existing fine-tuning approaches that focus on English-centric training corpora often introduce implicit cross-lingual alignment, overlooking the potential for more profound, latent-level cross-linguistic interactions.
Approach:	They propose a multilingual fine-tuning paradigm that explicitly establishes a cross-lingual connection mechanism at the latent level.
Outcome:	The proposed model outperforms vanilla SFT and offers a strong latent-level alternative to data-level augmentation methods.

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Lei Huang, Xiaocheng Feng, Weitao Ma, Liang Zhao, Yuchun Fan, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin

Extending Context Window of Large Language Models from a Distributional Perspective (2024.emnlp-main)

Challenge:	Teaching large language models to generate text with citations to evidence sources requires high-quality attribution data, which is costly and labor-intensive.
Approach:	They propose a framework for iteratively improving the attribution capability of large language models (LLMs) by attributing output to verifiable sources.
Outcome:	Experiments on three open-domain question-answering datasets show that START improves in aggregating information across multiple sources.

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Yingsheng Wu, Yuxuan Gu, Xiaocheng Feng, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin

Unveiling Entity-Level Unlearning for Large Language Models: A Comprehensive Analysis (2025.coling-main)

Challenge:	Existing scaling methods for extending context window rely on empirical approaches and lack understanding of the internal distribution within RoPE resulting in suboptimal performance.
Approach:	They propose to optimize the context window extending task from the view of rotary angle distribution by minimizing disturbance between rotary angles to maintain consistency with the pre-training phase.
Outcome:	The proposed approach reduces by up to 72% of the distributional disturbance when extending LLaMA2’s context window to 8k, and reduces it by up 32% when extending to 16k.

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Weitao Ma, Xiaocheng Feng, Weihong Zhong, Lei Huang, Yangfan Ye, Xiachong Feng, Bing Qin

Learning Fine-Grained Grounded Citations for Attributed Large Language Models (2024.findings-acl)

Challenge:	Existing studies have focused on instance-level unlearning, specifically removing predefined instances containing sensitive content.
Approach:	They propose a task to erase entity-related knowledge from the target model completely by analyzing the forget set and its size.
Outcome:	The proposed task systematically evaluates popular unlearning algorithms and reveals that the knowledge coverage of the forget set and its size play pivotal roles.

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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuxuan Gu, Weihong Zhong, Xiachong Feng, Weijiang Yu, Weihua Peng, Duyu Tang, Dandan Tu, Bing Qin

SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution (2026.findings-acl)

Challenge:	despite impressive performance, large language models still struggle with hallucinations . current approaches suffer from suboptimal citation quality due to reliance on in-context learning .
Approach:	They propose a framework that teaches large language models to generate fine-grained citations.
Outcome:	The proposed framework outperforms all baselines on the ALCE benchmark and achieves an average improvement of 14.21% in citation quality.

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Xiachong Feng, Yi Jiang, Xiaocheng Feng, Deyi Yin, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Yuxuan Gu, Chonghan Qin, Bing Qin, Lingpeng Kong

Enhancing Content Planning for Table-to-Text Generation with Data Understanding and Verification (2020.findings-emnlp)

Challenge:	Existing approaches to improve social intelligence of AI systems employ retrospective attributions and lack theoretical grounding.
Approach:	They propose a framework that uses Shapley values to ensure fair credit distribution with axiomatic guarantees of efficiency, symmetry, and marginality.
Outcome:	The proposed framework matches or exceeds proprietary models including GPT-4o and Claude-3.5-Sonnet.

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Heng Gong, Wei Bi, Xiaocheng Feng, Bing Qin, Xiaojiang Liu, Ting Liu

FroM: Frobenius Norm-Based Data-Free Adaptive Model Merging (2025.findings-emnlp)

Challenge:	Table-to-text models that select and order salient data and verbalize them fluently are lacking in content planning stage.
Approach:	They propose to enhance neural content planning by understanding data values with contextual numerical value representations that bring the sense of value comparison into content planning.
Outcome:	The proposed model outperforms existing systems with respect to content planning metrics on ROTOWIRE and MLB datasets.

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Zijian Li, Xiaocheng Feng, Huixin Liu, Yichong Huang, Ting Liu, Bing Qin

Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization (2025.acl-long)

Challenge:	a new adaptive merging method is proposed to improve fine-tuning performance . traditional methods often encounter task interference when merging full fine-uning models .
Approach:	They propose an adaptive merging method that directly measures model parameters using the Frobenius norm .
Outcome:	The proposed method outperforms baseline methods in various fine-tuning scenarios.

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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yangfan Ye, Weihong Zhong, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Bing Qin

MPR-GUI: Benchmarking and Enhancing Multilingual Perception and Reasoning in GUI Agents (2026.acl-long)

Challenge:	Existing frameworks for retrieval-augmented large language models (LLMs) are lacking in LFQA faithfulness testing.
Approach:	They propose a framework to teach retrieval-augmented large language models to explicitly discriminate between faithful and unfaithful generations.
Outcome:	The proposed framework outperforms GPT-4o in LFQA scenarios and outperformed existing benchmarks.

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Ruihan Chen, Qiming Li, Xiaocheng Feng, Weihong Zhong, Xiaoliang Yang, Yuxuan Gu, Zekun Zhou, Yunfei Lu, Haoyu Ren, Kun Chen, Dandan Tu, Bing Qin