Papers by Wu Jing

Adaptive Policy with Wait-k Model for Simultaneous Translation (2023.emnlp-main)

Challenge:	Large Audio Language Models (LALMs) exhibit a degradation in knowledge and reasoning capabilities . empirical results show that CORD significantly bridges the audio–text performance gap .
Approach:	They propose a framework that performs online cross-modal self-distillation to bridge the acoustic-semantic gap between LALMs and text-based models.
Outcome:	The proposed framework bridges the acoustic-semantic gap between LALMs and text-based models . it employs on-policy reverse KL divergence with importance-aware weighting .

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Libo Zhao, Kai Fan, Wei Luo, Wu Jing, Shushu Wang, Ziqian Zeng, Zhongqiang Huang

Machine Translation

Challenge:	Existing approaches to simultaneous machine translation require a robust read/write policy . a standalone multi-path wait-k model performs competitively with adaptive policies .
Approach:	They propose a more flexible approach by decoupling the adaptive policy model from the translation model.
Outcome:	The proposed approach outperforms baseline approaches in translation tasks.

DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications (2021.acl-short)

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Hongxuan Tang, Hongyu Li, Jing Liu, Yu Hong, Hua Wu, Haifeng Wang

CoCA: Fusing Position Embedding with Collinear Constrained Attention in Transformers for Long Context Window Extending (2024.acl-long)

Challenge:	In order to comprehensively verify the robustness and generalization of MRC models, we construct a real-world Chinese dataset - DuReader_robust .
Approach:	They introduce a real-world Chinese dataset to evaluate the robustness and generalization of MRC models from three aspects: over-sensitivity, over-stability and generalisation.
Outcome:	The proposed model fails to perform well on the challenge test set and may provide suggestions for future model development.

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Shiyi Zhu, Jing Ye, Wei Jiang, Siqiao Xue, Qi Zhang, Yifan Wu, Jianguo Li

LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression (2020.coling-main)

Challenge:	Existing models that use self-attention and position embedding have anomalous behavior that hinder long context window extrapolation.
Approach:	They propose a collinear constraint between Q and K to integrate RoPE and self-attention.
Outcome:	The proposed model integrates self-attention and position embedding into LLMs without fine-tuning.

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Yihuan Mao, Yujing Wang, Chufan Wu, Chen Zhang, Yang Wang, Quanlu Zhang, Yaming Yang, Yunhai Tong, Jing Bai

Enhancing Self-Attention with Knowledge-Assisted Attention Maps (2022.naacl-main)

Challenge:	Existing models that use knowledge distillation are memory-intensive and latency-prohibitive . Existing solutions that use this knowledge distilling framework are expensive .
Approach:	They propose a solution that uses weight pruning, matrix factorization and knowledge distillation to learn a smaller model.
Outcome:	The proposed model reduces the training overheads by an order of magnitude on public datasets while preserving state-of-the-art accuracy.

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Jiangang Bai, Yujing Wang, Hong Sun, Ruonan Wu, Tianmeng Yang, Pengfei Tang, Defu Cao, Mingliang Zhang1, Yunhai Tong, Yaming Yang, Jing Bai, Ruofei Zhang, Hao Sun, Wei Shen

Automated Progressive Red Teaming (2025.coling-main)

Challenge:	Existing works of knowledge infusion depend on multi-task learning frameworks, which are inefficient and require large-scale retraining when new knowledge is considered.
Approach:	They propose a method which integrates knowledge-generated attention maps into the self-attention mechanism and integrates it into the model.
Outcome:	The proposed model outperforms existing methods on academic datasets and industry-scale ad relevance applications.

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Bojian Jiang, Yi Jing, Tong Wu, Tianhao Shen, Deyi Xiong, Qing Yang

Reflection on Knowledge Graph for Large Language Models Reasoning (2025.findings-acl)

Challenge:	Automated red teaming (ART) is effective but time-consuming, costly and lacks scalability.
Approach:	They propose an automated red teaming framework that generates adversarial prompts to expose LLM vulnerabilities.
Outcome:	The proposed framework explores and exploits LLM vulnerabilities through multi-round interactions.

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Yigeng Zhou, Wu Li, Yifan Lu, Jing Li, Fangming Liu, Meishan Zhang, Yequan Wang, Daojing He, Honghai Liu, Min Zhang

Reasoning in a Combinatorial and Constrained World: Benchmarking LLMs on Natural-Language Combinatorial Optimization (2026.findings-acl)

Challenge:	Existing methods for supplementing Large Language Models (LLMs) with knowledge graphs often introduce noise in the retrieval and reasoning pipeline, hindering their ability to integrate external knowledge for complex multi-hop question answering.
Approach:	They propose a framework to enhance LLMs' reasoning capabilities through reflective engagement with knowledge graphs by Query Decoupling, LLM-Driven Knowledge Graph Exploration, and Inference with Knowledge Reconstruction.
Outcome:	The proposed framework integrates external knowledge into LLMs and trains them to leverage this knowledge for answering questions.

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Xia Jiang, Jing Chen, Cong Zhang, Jie Gao, Chengpeng Hu, Chenhao Zhang, Yaoxin Wu, Yingqian Zhang

Reasoning

Challenge:	NLCO evaluates large language models for combinatorial optimization (CO) . existing evaluations emphasize relatively simple reasoning competencies .
Approach:	They propose a combinatorial optimization benchmark that evaluates large language models on CO reasoning.
Outcome:	The proposed model can handle combinatorial optimization without writing code or calling external solvers.

TOME: A Two-stage Approach for Model-based Retrieval (2023.acl-long)

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Ruiyang Ren, Wayne Xin Zhao, Jing Liu, Hua Wu, Ji-Rong Wen, Haifeng Wang

SGG: Learning to Select, Guide, and Generate for Keyphrase Generation (2021.naacl-main)

Challenge:	Recent research has focused on model-based retrieval, which discards the index in the traditional retrieval model and memorizes the candidate corpora using model parameters.
Approach:	They propose a model-based retrieval approach that discards the index in the traditional retrieval model and memorizes the candidate corpora using model parameters.
Outcome:	The proposed approach eliminates the index in the traditional retrieval model and memorizes the candidate corpora using model parameters.

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Jing Zhao, Junwei Bao, Yifan Wang, Youzheng Wu, Xiaodong He, Bowen Zhou

Text Generation

Challenge:	Existing keyphrase generation approaches synchronously generate present and absent keyphrases without explicitly distinguishing these two categories.
Approach:	They propose to deal with present and absent keyphrases separately with different mechanisms by using a hierarchical neural network with a pointing-based selector and a selection-guided generator.
Outcome:	The proposed model outperforms baselines on four keyphrase generation tasks and shows extensibility in natural language generation tasks.

pyvene: A Library for Understanding and Improving PyTorch Models via Interventions (2024.naacl-demo)

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Zhengxuan Wu, Atticus Geiger, Aryaman Arora, Jing Huang, Zheng Wang, Noah Goodman, Christopher Manning, Christopher Potts

Explainability & Interpretability in NLP

Challenge:	Existing libraries are often project-based, but pyvene provides a unified and extensible framework for performing interventions on neural models and sharing the intervened upon models with others.
Approach:	They propose an open-source Python library that supports customizable interventions on a range of different PyTorch modules.
Outcome:	The proposed framework provides a unified and extensible framework for performing interventions on neural models and sharing the intervened upon models with others.

Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge (2025.emnlp-main)

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Tianhao Wu, Weizhe Yuan, Olga Golovneva, Jing Xu, Yuandong Tian, Jiantao Jiao, Jason E Weston, Sainbayar Sukhbaatar

RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations (2024.acl-long)

Challenge:	Existing methods for improving large language models have focused on improving model responses rather than judgment capabilities, resulting in rapid saturation during iterative training.
Approach:	They propose an iterative Meta-Rewarding step where the model judges its own judgements and uses that feedback to refine its judgment skills.
Outcome:	The proposed model improves Llama-3-8B-Instruct from 22.9% to 39.4% on AlpacaEval 2 and 20.6% to 29.1% on Arena-Hard.

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Jing Huang, Zhengxuan Wu, Christopher Potts, Mor Geva, Atticus Geiger

PRAL: A Tailored Pre-Training Model for Task-Oriented Dialog Generation (2021.acl-short)

Challenge:	Existing methods to disentangle individual neurons from multiple high-level concepts are not yet benchmarked.
Approach:	They propose a method of Multi-task Distributed Alignment Search that allows to find distributed representations satisfying multiple causal criteria.
Outcome:	The proposed method achieves state-of-the-art on the target language model with Llama2-7B .

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Jing Gu, Qingyang Wu, Chongruo Wu, Weiyan Shi, Zhou Yu

Soft Knowledge Prompt: Help External Knowledge Become a Better Teacher to Instruct LLM in Knowledge-based VQA (2024.acl-long)

Challenge:	Existing approaches to building task-oriented dialog systems require a substantial amount of annotations and thus are labor-intensive.
Approach:	They propose a Pre-trainedRole Alternating Language model (PRAL) that is explicitly designed for task-oriented dialog tasks.
Outcome:	The proposed model outperforms or is on par with state-of-the-art models on task-oriented dialog tasks.

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Qunbo Wang, Ruyi Ji, Tianhao Peng, Wenjun Wu, Zechao Li, Jing Liu

FAITH: Factuality Alignment through Integrating Trustworthiness and Honestness (2026.findings-acl)

Challenge:	Recent research focuses on improving prediction performance and reliability of LLM.
Approach:	They propose a method to actively extract valuable information from the knowledge to produce a latent vector as a soft prompt, which is fused with the image embedding to form a knowledge-enhanced context to instruct LLM.
Outcome:	The proposed method improves performance on knowledge-based VQA benchmarks.

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Xiaoning Dong, Chengyan Wu, Yajie Wen, Yu Chen, Yun Xue, Zhang Jing, Wei Xu, Bolei Ma

MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG (2025.findings-naacl)

Challenge:	Existing approaches to correct factually inaccurate outputs are lacking the semantic richness needed to properly understand its internal states of trustworthiness and honesty.
Approach:	They propose a framework for factuality alignment that integrates natural-language uncertainty signals with external knowledge and computes confidence scores and semantic entropy from LLM outputs.
Outcome:	Extensive experiments on four knowledge-intensive benchmarks show that FAITH improves the factual accuracy and truthfulness of Large Language Models (LLMs).

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Pingyu Wu, Daiheng Gao, Jing Tang, Huimin Chen, Wenbo Zhou, Weiming Zhang, Nenghai Yu

Better Simultaneous Translation with Monotonic Knowledge Distillation (2023.acl-long)

Challenge:	Existing approaches to retrieve entity information are limited by document level retrieval and intermingled storage of information from different entities.
Approach:	They propose a framework that enhances entity-specific query handling . MES-RAG introduces proactive security measures that ensure system integrity .
Outcome:	Experimental results show that MES-RAG improves accuracy and recall . the framework can be integrated into existing RAG architectures .

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Shushu Wang, Jing Wu, Kai Fan, Wei Luo, Jun Xiao, Zhongqiang Huang

Enhancing the Reasoning Capabilities of Small Language Models via Solution Guidance Fine-Tuning (2025.coling-main)

Challenge:	Existing methods to train offline MT models require generating target tokens before source sentence is fully consumed.
Approach:	They propose a method that leverages traditional translation models as teachers to generate monotonic yet accurate reference translations for sequence-level knowledge distillation.
Outcome:	The proposed approach improves on strong baselines and on a monotonic version of the WMT15 De-En test set.

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Jing Bi, Yuting Wu, Weiwei Xing, Zhenjie Wei

Coupling Global and Local Context for Unsupervised Aspect Extraction (D19-1)

Challenge:	Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks.
Approach:	They propose a new reasoning strategy Solution Guidance (SG) and a plug-and-play training paradigm Solution-Guidance Fine-Tuning (SGFT) which focuses on problem understanding and decomposition at the semantic and logical levels, rather than specific computations.
Outcome:	The proposed reasoning strategy Solution Guidance (SG) and plug-and-play training paradigm Solution-Guidance Fine-Tuning (SGFT) improves the reasoning capabilities of small language models on various reasoning tasks.

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Ming Liao, Jing Li, Haisong Zhang, Lingzhi Wang, Xixin Wu, Kam-Fai Wong

DuReadervis: A Chinese Dataset for Open-domain Document Visual Question Answering (2022.findings-acl)

Challenge:	Existing studies on aspect extraction focus on sequence tagging models trained on human-annotated data.
Approach:	They propose a novel neural model capable of coupling global and local representations to discover aspect words by combining global and locale contexts.
Outcome:	The proposed model outperforms state-of-the-art models on laptop and restaurant reviews on two benchmarks.

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Le Qi, Shangwen Lv, Hongyu Li, Jing Liu, Yu Zhang, Qiaoqiao She, Hua Wu, Haifeng Wang, Ting Liu

Information Extraction & Text Mining

Challenge:	Open-domain question answering is a task that requires answering questions based on a collection of document images.
Approach:	They propose to use document images to answer questions using layouts and visual features instead of text.
Outcome:	The proposed approach reduces human cost and improves scalability of QA systems by incorporating layouts and visual features.

TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities (2023.acl-demo)

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Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Gou, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan

Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity (2026.acl-long)

Challenge:	Several pre-training models of different modalities are showing a rising trend of homogeneity in their model structures.
Approach:	They propose a toolkit that supports pre-training models of different modalities.
Outcome:	The proposed toolkit can match the performance of the original implementations on text, vision, and audio benchmarks.

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Feiteng Fang, Dingwei Chen, Xiang Huang, Ting-En Lin, Yuchuan Wu, Xiong Liu, Jing Ye, Ziqiang Liu, Haonan Zhang, Liang Zhu, Hamid Alinejad-Rokny, Min Yang, Yongbin Li

Challenge:	Existing approaches to reward modeling in reinforcement learning tasks are limited when dealing with ambiguous preferences.
Approach:	They propose to use AAM to dynamically calibrate preference margins using the Bradley-Terry model's internal parameter knowledge to improve reward modeling in subjective tasks.
Outcome:	The proposed approach improves reward modeling by dynamically calibrating preference margins using the model’s internal parameter knowledge.

Fine- and Coarse-Granularity Hybrid Self-Attention for Efficient BERT (2022.acl-long)

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Jing Zhao, Yifan Wang, Junwei Bao, Youzheng Wu, Xiaodong He

V-RoLoRA: RLVR-Driven MoE Routing for Steerable Pluralistic Alignment (2026.findings-acl)

Challenge:	Transformer-based pre-trained models achieve state-of-the-art results, but they can be prohibitively costly.
Approach:	They propose a fine- and coarse-granularity hybrid self-attention that shortens the computational sequence length in self- attention by progressively shortening the computational time.
Outcome:	The proposed model reduces computation cost by shortening the computational sequence length in self-attention.

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Jing Wang, Yaomin Wu, Yinglin Wang, Yitong Yang

DuReader-Retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine (2022.emnlp-main)

Challenge:	Current methods for steering large language models rely on prompt engineering or reasoning-time guidance.
Approach:	They propose a value-controllable pluralistic alignment framework enhanced with conditioned gating that dynamically directs the flow among multiple experts based on an input value or moral vector.
Outcome:	The proposed method outperforms prompt-based steering and multi-task PEFT benchmarks on two 8-billion-parameter backbones.

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Yifu Qiu, Hongyu Li, Yingqi Qu, Ying Chen, QiaoQiao She, Jing Liu, Hua Wu, Haifeng Wang

Separation and Fusion: A Novel Multiple Token Linking Model for Event Argument Extraction (2024.naacl-long)

Challenge:	Existing datasets for non-English passage retrieval are lacking in quality and accuracy.
Approach:	They present a large-scale Chinese dataset for passage retrieval . they reduce false negatives by manually annotating results pooled from multiple retrievers .
Outcome:	The proposed dataset reduces false negatives in development and testing sets and removes similar training queries.

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Jing Xu, Dandan Song, Siu Hui, Zhijing Wu, Meihuizi Jia, Hao Wang, Yanru Zhou, Changzhi Zhou, Ziyi Yang

Information Extraction & Text Mining

Challenge:	Existing methods for event argument extraction (EAE) lack cross-event information and require longer role sequences . et al. (2017): outperforms state-of-the-art methods for EE.
Approach:	They propose a separation-and-fusion paradigm to separate the acquisition of cross-event information and fuse it into the argument extraction of a target event.
Outcome:	The proposed model outperforms the state-of-the-art models on four widely used datasets.

ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents (2025.acl-long)

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Zhigen Li, Jianxiang Peng, Yanmeng Wang, Yong Cao, Tianhao Shen, Minghui Zhang, Linxi Su, Shang Wu, Yihang Wu, YuQian Wang, Ye Wang, Wei Hu, Jianfeng Li, Shaojun Wang, Jing Xiao, Deyi Xiong

MARIO: MAth Reasoning with code Interpreter Output - A Reproducible Pipeline (2024.findings-acl)

Challenge:	Existing models that use Large Language Models (LLMs) show superior performance in various tasks, but lack of controllability leads to unfocused conversations or task failure.
Approach:	They propose a standard operating procedure (SOP) framework to regulate dialogue flow by integrating Chain of Thought reasoning and supervised fine-tuning for SOP prediction.
Outcome:	The proposed method achieves a 27.95% improvement in action accuracy compared to baseline models based on GPT-3.5 and also shows notable gains for open-source models.

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Minpeng Liao, Chengxi Li, Wei Luo, Wu Jing, Kai Fan

UNComp: Can Matrix Entropy Uncover Sparsity? — A Compressor Design from an Uncertainty-Aware Perspective (2025.emnlp-main)

Challenge:	Large language models lack mathematical reasoning, a hurdle on the path to true artificial general intelligence.
Approach:	They propose a protocol for fine-tuning large language models with a Python code interpreter to enhance the text analysis of the LLMs.
Outcome:	The proposed protocol improves the performance of a 7B-parameter LLM on the GSM8K and MATH datasets while allowing for an outlier-free value model-based inference method.

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Jing Xiong, Jianghan Shen, Fanghua Ye, Chaofan Tao, Zhongwei Wan, Jianqiao Lu, Xun Wu, Chuanyang Zheng, Zhijiang Guo, Min Yang, Lingpeng Kong, Ngai Wong

Reasoning

Challenge:	Deploying large language models (LLMs) for long-context inference remains challenging due to their substantial memory and computational demands.
Approach:	They propose an uncertainty-aware framework that leverages truncated matrix entropy to identify areas of low information content.
Outcome:	The proposed framework reduces the KV cache size to 4.74% of the original and achieves a 6% speedup.

Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models (2026.acl-long)

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Linhao Zhong, Linyu Wu, Bozhen Fang, Tianjian Feng, Chenchen Jing, Wen Wang, Jiaheng Zhang, Hao Chen, Chunhua Shen

PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval (2021.findings-acl)

Challenge:	Existing Diffusion Language Models rely on hard binary masking and discrete token assignments, which hinder the revision of early decisions.
Approach:	They propose a diffusion-based language modeling approach that replaces hard binary masks with evolving soft token distributions.
Outcome:	The proposed approach outperforms existing DLMs on multiple benchmarks.

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Ruiyang Ren, Shangwen Lv, Yingqi Qu, Jing Liu, Wayne Xin Zhao, QiaoQiao She, Hua Wu, Haifeng Wang, Ji-Rong Wen

CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models (2025.findings-naacl)

Challenge:	Recent studies only consider query-centric similarity relation when learning the dual-encoder retriever.
Approach:	They propose a query-centric and PAssage-centric approach to capture more comprehensive similarity relations for dense passage retrieval.
Outcome:	The proposed approach significantly outperforms existing models on both MSMARCO and Natural Questions datasets.

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Shangda Wu, Yashan Wang, Ruibin Yuan, Guo Zhancheng, Xu Tan, Ge Zhang, Monan Zhou, Jing Chen, Xuefeng Mu, Yuejie Gao, Yuanliang Dong, Jiafeng Liu, Xiaobing Li, Feng Yu, Maosong Sun

Efficient Self-Evaluation for Diffusion Language Models via Sequence Regeneration (2026.acl-long)

Challenge:	Current music information retrieval systems struggle to meet linguistic diversity challenges . current systems struggle with text queries in non-English languages .
Approach:	They propose a music information retrieval system that supports both ABC notation and MIDI . CLaMP 2 includes a multilingual text encoder and a multiple-modal music encoder .
Outcome:	The proposed system achieves state-of-the-art results in multilingual semantic search and music classification across modalities.

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Linhao Zhong, Linyu Wu, Wen Wang, Yuling Xi, Chenchen Jing, Jiaheng Zhang, Hao Chen, Chunhua Shen

Efficient Cross-modal Prompt Learning with Semantic Enhancement for Domain-robust Fake News Detection (2025.coling-main)

Challenge:	Non-sequential and bidirectional nature of diffusion large language models makes direct likelihood-based self-evaluation challenging.
Approach:	They propose a self-evaluation confidence quantification method for diffusion large language models that quantifies confidence by computing the probability of regenerating tokens in the entire generated sequence, given the full context.
Outcome:	The proposed method is correlated with semantic coherence and answer accuracy.

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Fei Wu, Hao Jin, Changhui Hu, Yimu Ji, Xiao-Yuan Jing, Guo-Ping Jiang

Improve Speech Translation Through Text Rewrite (2025.coling-industry)

Challenge:	Existing MFND methods conduct cross-modal information interaction at later stage, resulting in weak generalization ability.
Approach:	They propose an automatic multi-modal fake news detection method that exploits cross-modal information interaction at later stage.
Outcome:	The proposed method outperforms state-of-the-art methods on three MFND benchmarks.

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Jing Wu, Shushu Wang, Kai Fan, Wei Luo, Minpeng Liao, Zhongqiang Huang

Text Generation

Challenge:	Recent advances in speech translation (ST) research have focused on the unique characteristics of spontaneous speech, including accents and presentation quality.
Approach:	They propose to transform transcribed speech into a cleaner style more in line with the expectations of translation models built from written text.
Outcome:	Experiments on public and in-house translation models show that the proposed model can be effectively distilled into a standalone translation model.

SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generation (2024.emnlp-main)

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Xiaoze Liu, Ting Sun, Tianyang Xu, Feijie Wu, Cunxiang Wang, Xiaoqian Wang, Jing Gao

Robustness in NLP

Challenge:	Large Language Models (LLMs) have transformed machine learning but have raised significant legal concerns due to their potential to produce text that infringes on copyrights.
Approach:	They propose a lightweight, real-time defense mechanism to prevent the generation of copyrighted text by evaluating methods and testing attack strategies.
Outcome:	The proposed defense significantly reduces the volume of copyrighted text generated by LLMs by effectively refusing malicious requests.

SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales (2024.emnlp-main)

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Tianyang Xu, Shujin Wu, Shizhe Diao, Xiaoze Liu, Xingyao Wang, Yangyi Chen, Jing Gao

Memformer: A Memory-Augmented Transformer for Sequence Modeling (2022.findings-aacl)

Challenge:	Existing approaches to elicit confidence from large language models are limited to binary or inaccurate group-level confidence estimates.
Approach:	They propose a training framework that teaches LLMs to express more fine-grained confidence estimates.
Outcome:	The proposed training framework reduces the confidence calibration error and maintains the performance of the model.

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Qingyang Wu, Zhenzhong Lan, Kun Qian, Jing Gu, Alborz Geramifard, Zhou Yu

Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generation (2026.acl-long)

Challenge:	Experimental results show that Memformer uses 8.1x less memory space and 3.2x faster on inference.
Approach:	They propose an efficient neural network that utilizes an external dynamic memory to encode and retrieve past information.
Outcome:	The proposed model achieves comparable performance against baselines with 8.1x less memory space and 3.2x faster on inference.

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Yuhao Wang, Ruiyang Ren, Yucheng Wang, Xin Zhao, Jing Liu, Hua Wu, Haifeng Wang

MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free (2026.findings-acl)

Challenge:	Existing reinforcement learning systems lack verifiable reward mechanisms for long-form question answering . current systems lack reliable long-term answers due to lack of factual content .
Approach:	They propose a framework for reinforced verifiable informativeness optimization . it defines informativeness as measurable and externally verifier objective for RL .
Outcome:	Experiments show that RioRAG achieves higher factual recall and faithfulness . the proposed framework is based on a framework that uses nugget-centric verification with cross-source checks .

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Yishu Lei, Shuwei He, Hu Jing, Dan Zhang, Xianlong Luo, Danxiang Zhu, Shikun Feng, Rui Liu, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang

Not All Voices Are Rewarded Equally: Probing and Repairing Reward Models across Human Diversity (2025.findings-emnlp)

Challenge:	Existing research on Large Language Models (LLMs) limited to textual input modality . acoustic information is intrinsically heterogeneous, entangling attributes such as speech, music, and environmental context.
Approach:	They propose a sparse Mixture-of-Experts architecture to decouple acoustic information by routing audio tokens to specialized experts.
Outcome:	The proposed architecture outperforms existing models on audio semantic and paralinguistic tasks while retaining shared experts for global context.

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Zihao Li, Feihao Fang, Xitong Zhang, Jiaru Zou, Zhining Liu, Wei Xiong, Ziwei Wu, Baoyu Jing, Jingrui He

CPO: Addressing Reward Ambiguity in Role-playing Dialogue via Comparative Policy Optimization (2025.findings-emnlp)

Challenge:	Using real-world datasets, we conduct the most comprehensive study to date, auditing various state-of-the-art reward models across nine sensitive attributes, including age, gender, ethnicity, etc.
Approach:	They propose a method to mitigate group disparities in reward modeling by using real-world data.
Outcome:	The proposed method is based on a population-based dataset with nine demographic attributes, including gender, ethnicity, age, gender, and ethnicity.

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Jing Ye, Rui Wang, Yuchuan Wu, Victor Ma, Feiteng Fang, Fei Huang, Yongbin Li

Augmenting Reasoning Capabilities of LLMs with Graph Structures in Knowledge Base Question Answering (2024.findings-emnlp)

Challenge:	Comparative Policy Optimization (CPO) redefines the reward evaluation paradigm by shifting from sample-wise scoring to comparative group-wise score.
Approach:	They propose a method to optimize subjective tasks by shifting from sample-wise to comparative group-wise scoring.
Outcome:	The proposed framework shifts from sample-wise scoring to comparative group-wise score . it minimizes contextual bias and enables more robust and fair performance evaluation.

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Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Hao Wang, Jun Yang, Jing Xu, Ruanmin Cao, HaoYu Wang

DMSD: Dual-Modal Semantic Disentanglement for Compositional Zero-Shot Learning (2026.findings-acl)

Challenge:	Recent work uses Large Language Models (LLMs) for semantic parsing to address Knowledge Base Question Answering tasks.
Approach:	They propose a framework that augments reasoning capabilities of LLMs with Graph Structures in Knowledge Base Question Answering to retrieve question-related graph structures.
Outcome:	The proposed framework outperforms existing methods on GrailQA and WebQSP under the few-shot setting.

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Pan Yang, Jing Yang, Ruan Xiao li, Yuling Chen, Yuankai Wu, Quan Zhou, Xu Wang

Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation (2025.coling-main)

Challenge:	Compositional Zero-Shot Learning (CZSL) is a new research paradigm that learns sub-concepts from seen compositions and recognizes unseen novel combinations.
Approach:	They propose a Dual-Modal Semantic Disentanglement framework that integrates visual and textual information to achieve effective sub-concept disentangling.
Outcome:	The proposed framework achieves state-of-the-art performance on three benchmark datasets . it integrates a class-centroid bridge module to guide class centroids toward the textual space .

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Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, Jing Liu, Hua Wu, Ji-Rong Wen, Haifeng Wang

EgoMemory: Memory-Augmented Personalized Retrieval for Long-Context Egocentric Video (2026.findings-acl)

Challenge:	Large language models (LLMs) have shown impressive prowess in solving a wide range of tasks with world knowledge, but it remains unclear how well they perceive their factual knowledge boundaries.
Approach:	They propose to use a retrieval augmentation approach to enhance LLMs' awareness of factual knowledge boundaries to analyze factual and factual information in open-domain question answering (QA)
Outcome:	The proposed method improves LLMs’ QA and judgemental capabilities by integrating supporting documents with the questions.

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Yuanmin Tang, Jue Zhang, Xiaoting Qin, Jing Yu, Meikang Qiu, Gaopeng Gou, Gang Xiong, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Wu

Inducing Character-level Structure in Subword-based Language Models with Type-level Interchange Intervention Training (2023.findings-acl)

Challenge:	Existing egocentric video datasets do not support the personalization and long-context reasoning required for episodic memory retrieval.
Approach:	They propose a benchmark framework that uses MLLMs and reflective Chain-of-Thought to ground user queries in personal memory explicitly.
Outcome:	The proposed framework outperforms state-of-the-art benchmarks on three benchmarks . it can be used to generate detailed target video descriptions in long-context contexts based on user-specific object annotations enriched with user-specified object annotation data .

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Jing Huang, Zhengxuan Wu, Kyle Mahowald, Christopher Potts

RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking (2021.emnlp-main)

Challenge:	Language models operating on subword units are challenging for character-level manipulations, authors say . authors develop a framework to learn robust character representations inside subword-based models .
Approach:	They propose a causal intervention framework to learn robust character representations inside subword-based language models.
Outcome:	The proposed model outperforms character-level models on more complex tasks . it improves robustness on unseen token sequences and leads to human-interpretable representations of characters.

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Ruiyang Ren, Yingqi Qu, Jing Liu, Wayne Xin Zhao, QiaoQiao She, Hua Wu, Haifeng Wang, Ji-Rong Wen

Projecting Out the Malice: A Global Subspace Approach to LLM Detoxification (2026.acl-long)

Challenge:	Recent studies show that passage retrieval and passage reranking are important for achieving mutual improvement.
Approach:	They propose a unified listwise training approach for passage retrieval and passage reranking that incorporates a retrieval procedure and a hybrid data augmentation strategy.
Outcome:	The proposed approach improves on both MSMARCO and Natural Questions datasets.

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Zenghao Duan, Zhiyi Yin, Zhichao Shi, Liang Pang, Shaoling Jing, Zihe Huang, Jiayi Wu, Yu Yan, Jingcheng Deng, Huawei Shen, Xueqi Cheng

Challenge:	Large language models (LLMs) exhibit exceptional performance but pose inherent risks of generating toxic content.
Approach:	They propose a method that removes toxic subspaces from FFN parameters . they propose to use a lightweight method to eliminate toxic subespaces .
Outcome:	The proposed method achieves SOTA detoxification while preserving general capabilities without large-scale retraining.

Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification (P18-1)

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Yizhong Wang, Kai Liu, Jing Liu, Wei He, Yajuan Lyu, Hua Wu, Sujian Li, Haifeng Wang

Reasoning

Challenge:	Recent years have seen rapid growth in the MRC community . MRC is believed to be a crucial step in building a general intelligent agent .
Approach:	They propose an end-to-end neural model that enables multiple passages to verify each other based on their content representations.
Outcome:	The proposed model outperforms the baseline on the English MS-MARCO dataset and the Chinese DuReader dataset, and achieves state-of-the-art performance on both datasets.

Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering (2026.findings-acl)

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Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Jun Yang, Huipeng Ma, Chenhao Li, Luan Zhang, Yading Li, Xudong Li, Shenxi Liu, Jing Jiang

Natural Language Interfaces

Challenge:	Existing retrieval-augmented approaches focus on ignoring the structural information of the Knowledge Base (KB) and the question.
Approach:	They propose a structure-aware subgraph retrieval stage that ranks candidate subgraphs by aligning them with the question’s structure, along with semantic relevance.
Outcome:	Experiments on GrailQA, WebQSP, and GraphQuestions show that the proposed framework achieves state-of-the-art performance.

D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension (D19-58)

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Hongyu Li, Xiyuan Zhang, Yibing Liu, Yiming Zhang, Quan Wang, Xiangyang Zhou, Jing Liu, Hua Wu, Haifeng Wang

In-Context Compositional Generalization for Large Vision-Language Models (2024.emnlp-main)

Challenge:	MRC requires machines to understand text and answer questions about the text.
Approach:	They propose a simple system Baidu submitted for MRQA 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models.
Outcome:	The proposed system is ranked at top 1 of all participants in terms of averaged F1 score.

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Chuanhao Li, Chenchen Jing, Zhen Li, Mingliang Zhai, Yuwei Wu, Yunde Jia

DREAM: Disentangling Risks to Enhance Safety Alignment in Multimodal Large Language Models (2025.naacl-long)

Challenge:	Recent work shows that in-context learning for large language models exhibits compositional generalization capacity.
Approach:	They propose a method to exhibit in-context compositional generalization in large vision-language models by combining visual and linguistic modalities.
Outcome:	The proposed method reduces redundancy and complexity in in-context learning with LVLMs.

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Jianyu Liu, Hangyu Guo, Ranjie Duan, Xingyuan Bu, Yancheng He, Shilong Li, Hui Huang, Jiaheng Liu, Yucheng Wang, Chenchen Jing, Xingwei Qu, Xiao Zhang, Pei Wang, Yanan Wu, Jihao Gu, Yangguang Li, Jianke Zhu

PARSE: An Efficient Search Method for Black-box Adversarial Text Attacks (2022.coling-1)

Challenge:	Multimodal Large Language Models (MLLMs) pose unique safety challenges due to their integration of visual and textual data.
Approach:	They propose a method to disentangle risks through step-by-step reasoning within multimodal inputs.
Outcome:	The proposed approach improves safety alignment in MLLMs by fine-tuning and iterative Reinforcement Learning from AI feedback.

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Pengwei Zhan, Chao Zheng, Jing Yang, Yuxiang Wang, Liming Wang, Yang Wu, Yunjian Zhang

Explicit and Implicit Data Augmentation for Social Event Detection (2025.acl-long)

Challenge:	Neural networks are vulnerable to adversarial examples, i.e., under a black-box scenario.
Approach:	They propose a word-level search algorithm that searches for subareas under dynamic search space following the subarea importance.
Outcome:	The proposed algorithm can achieve comparable success rates to complex search methods while saving numerous queries and time.

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Congbo Ma, Yuxia Wang, Jia Wu, Jian Yang, Jing Du, Zitai Qiu, Qing Li, Hu Wang, Preslav Nakov

An Interpretable and Crosslingual Method for Evaluating Second-Language Dialogues (2025.naacl-long)

Challenge:	Social event detection relies on labeled data, but annotation is costly and labor-intensive.
Approach:	They propose a plug-and-play dual augmentation framework that combines explicit text-based and implicit feature-space augmentation to enhance data diversity and model robustness.
Outcome:	The proposed framework outperforms the best baseline model by 17.67% on the Twitter2012 dataset and 15.57% on the twitter2018 dataset in terms of the average F1 score.

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Rena Wei Gao, Xuetong Wu, Carsten Roever, Jing Wu, Long Lv, Jingxuan Wu, Jey Han Lau

RoR: Read-over-Read for Long Document Machine Reading Comprehension (2021.findings-emnlp)

Challenge:	Existing studies on second language (SL) assessment of conversational fluency and interactivity have focused on written correction or pronunciation from ASR.
Approach:	They propose a framework that assesses the relationships between micro-level linguistic features and macro-level interactivity labels for Chinese-as-a-second-language dialogues.
Outcome:	The proposed framework is interpretable and can be adapted to other languages for second-language dialogue evaluation.

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Jing Zhao, Junwei Bao, Yifan Wang, Yongwei Zhou, Youzheng Wu, Xiaodong He, Bowen Zhou

Machine Reading Comprehension

Challenge:	Existing models for machine reading comprehension are limited to individual chunks due to encoding length constraint.
Approach:	They propose a read-over-read method that expands the reading field from chunk to document by predicting regional answers for each chunk.
Outcome:	Extensive experiments on QuAC and TriviaQA show that the proposed model performs well for long document reading.

OPERA: Operation-Pivoted Discrete Reasoning over Text (2022.naacl-main)

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Yongwei Zhou, Junwei Bao, Chaoqun Duan, Haipeng Sun, Jiahui Liang, Yifan Wang, Jing Zhao, Youzheng Wu, Xiaodong He, Tiejun Zhao

A Thorough Examination on Zero-shot Dense Retrieval (2023.findings-emnlp)

Challenge:	Existing methods to predict logical forms ignore the utilization of symbolic operations and lack reasoning ability and interpretability.
Approach:	They propose an operation-pivoted discrete reasoning framework that uses symbolic operations as neural modules to facilitate reasoning ability and interpretability.
Outcome:	Extensive experiments on DROP and RACENum datasets show the reasoning ability of OPERA.

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Ruiyang Ren, Yingqi Qu, Jing Liu, Xin Zhao, Qifei Wu, Yuchen Ding, Hua Wu, Haifeng Wang, Ji-Rong Wen

LMR-BENCH: Evaluating LLM Agent’s Ability on Reproducing Language Modeling Research (2025.emnlp-main)

Challenge:	Recent advances in dense retrieval (DR) models have been shown to be not as competitive as traditional sparse retrieval models in a zero-shot retrieval setting.
Approach:	They propose to examine the zero-shot capability of DR models by analyzing key factors related to source training set and potential bias from target dataset.
Outcome:	The proposed model is not as competitive as sparse retrieval models in a zero-shot retrieval setting.

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Shuo Yan, Ruochen Li, Ziming Luo, Zimu Wang, Daoyang Li, Liqiang Jing, Kaiyu He, Peilin Wu, Juntong Ni, George Michalopoulos, Yue Zhang, Ziyang Zhang, Mian Zhang, Zhiyu Chen, Xinya Du

DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models (2022.emnlp-main)

Challenge:	Large language model (LLM) agents have demonstrated remarkable potential in advancing scientific discovery, but their capability in reproducing code from research papers remains underexplored.
Approach:	They propose to evaluate LLM agents' ability to reproduce scientific research papers by analyzing code reproduction tasks from 23 research papers published in top-tier NLP venues.
Outcome:	The proposed benchmark systematically evaluates the capability of large language model (LLM) agents on code reproduction from Language Modeling Research.

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Hongyu Zhu, Yan Chen, Jing Yan, Jing Liu, Yu Hong, Ying Chen, Hua Wu, Haifeng Wang

Robustness in NLP

Challenge:	a comprehensive evaluation of QM models should be conducted on natural texts, not on artificial adversarial examples . ral models are often not robust to adversarials, which means they predict unexpected outputs .
Approach:	They use a Chinese dataset to evaluate the robustness of QM models . they show that the effect of artificial adversarial examples does not work on natural texts .
Outcome:	The proposed model is more robust than other models on natural questions with 32 linguistic perturbations.

GUI Agents: A Survey (2025.findings-acl)

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Dang Nguyen, Jian Chen, Yu Wang, Gang Wu, Namyong Park, Zhengmian Hu, Hanjia Lyu, Junda Wu, Ryan Aponte, Yu Xia, Xintong Li, Jing Shi, Hongjie Chen, Viet Dac Lai, Zhouhang Xie, Sungchul Kim, Ruiyi Zhang, Tong Yu, Mehrab Tanjim, Nesreen K. Ahmed, Puneet Mathur, Seunghyun Yoon, Lina Yao, Branislav Kveton, Jihyung Kil, Thien Huu Nguyen, Trung Bui, Tianyi Zhou, Ryan A. Rossi, Franck Dernoncourt

Challenge:	Large Foundation Models (LFMs) have transformed the landscape of AI research and day-to-day life.
Approach:	They propose a framework that delineates GUI agents' perception, reasoning, planning, and acting capabilities.
Outcome:	The proposed framework delineates their perception, reasoning, planning, and acting capabilities.

BASES: Large-scale Web Search User Simulation with Large Language Model based Agents (2024.findings-emnlp)

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Ruiyang Ren, Peng Qiu, Yingqi Qu, Jing Liu, Xin Zhao, Hua Wu, Ji-Rong Wen, Haifeng Wang

P3LM: Probabilistically Permuted Prophet Language Modeling for Generative Pre-Training (2022.findings-emnlp)

Challenge:	Existing research on web search rely on real-user experiments, which can be costly to scale up.
Approach:	They propose a user simulation framework with LLM-based agents that can generate unique user profiles at scale.
Outcome:	The proposed framework can generate unique user profiles at scale, leading to diverse search behaviors.

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Junwei Bao, Yifan Wang, Ying Jiangyong, Yeyun Gong, Jing Zhao, Youzheng Wu, Xiaodong He

LUNA: Learning Slot-Turn Alignment for Dialogue State Tracking (2022.naacl-main)

Challenge:	Existing autoregressive left-to-right (L2R) models are limited to unidirectional information and constrained on strong local dependencies.
Approach:	They propose a probabilistically permuted prophet language model which strengthens the modeling of bidirectional information and long token dependencies for sequence generation.
Outcome:	Experiments on GLGE dataset show that P3LM improves on natural language generation tasks.

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Yifan Wang, Jing Zhao, Junwei Bao, Chaoqun Duan, Youzheng Wu, Xiaodong He

Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions (2022.acl-long)

Challenge:	Existing methods exploit the utterances of all dialogue turns to assign value to slots . this can lead to suboptimal results due to information introduced from irrelevant utterrances .
Approach:	They propose a SLot-TUrN Alignment enhanced approach to assign slot value . they explicitly align each slot with its most relevant utterance and then predict the corresponding value based on this aligned utteration.
Outcome:	The proposed approach achieves state-of-the-art on three multi-domain task-oriented dialogue datasets.

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Jing Gu, Eliana Stefani, Qi Wu, Jesse Thomason, Xin Wang

Can MLLMs Reason Beyond Language? VisReason: A Comprehensive Benchmark for Vision-Centric Reasoning (2026.findings-acl)

Challenge:	Vision-and-Language Navigation (VLN) is a research topic that is gaining attention in the field of artificial intelligence.
Approach:	They propose to build an embodied agent that can communicate with humans in natural language and navigate in real 3D environments.
Outcome:	This paper reviews current studies in the emerging field of vision-and-language navigation . it highlights limitations and opportunities for future work .

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Longteng Guo, Yifan Wang, Pengkang Huo, Tailai Chen, Yuze Wu, Jing Liu, Xinxin Zhu

Equal Truth: Rumor Detection with Invariant Group Fairness (2025.findings-emnlp)

Challenge:	Recent advances in multimodal large language models demonstrate strong performance on visual reasoning benchmarks.
Approach:	They propose a benchmark for vision-centric reasoning that integrates visual and textual information for non-trivial reasoning.
Outcome:	The proposed benchmark exposes gaps between humans and current MLLMs and reveals limited benefits from test-time reasoning strategies.

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Junyi Chen, Mengjia Wu, Qian Liu, Jing Sun, Ying Ding, Yi Zhang

Chain of Thought Prompting Elicits Knowledge Augmentation (2023.findings-acl)

Challenge:	Existing rumor detection methods rarely consider fairness issues inherent in the model . this can lead to biased predictions across stakeholder groups, undermining their detection effectiveness .
Approach:	They propose a framework to address fairness issues inherent in rumor detection models . they perform unsupervised partitioning to dynamically identify potential unfair data patterns . then, they apply invariant learning to these partitions to extract fair and informative feature representations .
Outcome:	The proposed method outperforms strong baselines regarding detection and fairness performance . it also shows robust performance on out-of-distribution samples .

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Dingjun Wu, Jing Zhang, Xinmei Huang

A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair Extraction (2022.coling-1)

Challenge:	Existing knowledge augmentation methods require retrieving knowledge from external knowledge sources or developing a reasoner to leverage the logical rules within the external knowledge source.
Approach:	They propose a Chain-of-Thought-based method that augments knowledge for deep learning by removing the need for additional knowledge retrieval or knowledge reasoning models.
Outcome:	The proposed method outperforms both pure CoT-based methods and the non-augmented method across the majority of 11 publicly available benchmarks for various reasoning tasks.

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Changzhi Zhou, Dandan Song, Jing Xu, Zhijing Wu

Information Extraction & Text Mining

Challenge:	Emotion-cause pair extraction (ECPE) is an emerging task in emotion cause analysis, which extracts potential emotion-caused pairs from an emotional document.
Approach:	They propose a document-level machine reading comprehension task to model complex relations between emotions and causes while avoiding generating the pairing matrix.
Outcome:	The proposed framework outperforms existing state-of-the-art methods on the emotion cause corpus and can model complex relations between emotions and causes while avoiding pairing matrix.

Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension (P19-1)

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An Yang, Quan Wang, Jing Liu, Kai Liu, Yajuan Lyu, Hua Wu, Qiaoqiao She, Sujian Li

RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering (2021.naacl-main)

Challenge:	Recent results show pre-trained language models (LMs) can improve machine reading comprehension (MRC) Experimental results indicate that KT-NET offers significant and consistent improvements over BERT .
Approach:	They propose a method that leverages external knowledge bases to improve machine reading comprehension (MRC) KT-NET employs an attention mechanism to select desired knowledge from KBs and fuses selected knowledge with BERT to enable context- and knowledge-aware predictions.
Outcome:	The proposed model outperforms baseline models on ReCoRD and SQuAD1.1 benchmarks and ranks 1st on the ReCoDR and SQUAD1.1 leaderboards.

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Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, Haifeng Wang

An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework Based on Semantic Blocks (2022.coling-1)

Challenge:	Open-domain question answering uses dense passage retrieval to find answers . however, it is difficult to effectively train a dual-encoder due to discrepancy between training and inference .
Approach:	They propose an optimized training approach to improve dense passage retrieval using RocketQA . they propose cross-batch negatives, denoised hard negatives and data augmentation .
Outcome:	The proposed approach outperforms state-of-the-art models on both MSMARCO and Natural Questions.

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Xinnian Liang, Jing Li, Shuangzhi Wu, Jiali Zeng, Yufan Jiang, Mu Li, Zhoujun Li