Papers by Qiang Zhang

OmniCharacter: Towards Immersive Role-Playing Agents with Seamless Speech-Language Personality Interaction (2025.acl-long)

Challenge:	Large language models (LLMs) have a high potential to digitize and enhance the health & public services industry.
Approach:	They propose to use a cross-lingual benchmark dataset to assess the robustness of state-of-the-art LLMs in the spatio vs temporal domain for traffic incident classification.
Outcome:	The proposed model performs well in the spatio-temporal domain and in the non-English context.

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Haonan Zhang, Run Luo, Xiong Liu, Yuchuan Wu, Ting-En Lin, Pengpeng Zeng, Qiang Qu, Feiteng Fang, Min Yang, Lianli Gao, Jingkuan Song, Fei Huang, Yongbin Li

DET: A Dual-Encoding Transformer for Relational Graph Embedding (2024.lrec-main)

Challenge:	Existing methods focus on replicating dialogues in textual form, neglecting the role’s voice traits as a crucial effect in interaction, which tends to be more immersive experiences in realistic scenarios.
Approach:	They propose a first seamless speech-language personality interaction model to achieve immersive RPAs with low latency.
Outcome:	The proposed model exhibits role-specific personality traits and vocal traits throughout the interaction, enabling a mixture of speech and language responses.

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Lingbing Guo, Zhuo Chen, Jiaoyan Chen, Qiang Zhang, Huajun Chen

A Survey on Asking Clarification Questions Datasets in Conversational Systems (2023.acl-long)

Challenge:	Existing approaches to graph representation only consider the local neighbors, sacrificing the Transformer’s ability to attend to elements at any distance.
Approach:	They propose a dual-encoding Transformer architecture that uses a structural encoder and a semantic encoder to seek for semantically relevant nodes.
Outcome:	The proposed architecture achieves superior performance compared to state-of-the-art attention-based methods on complex relational graphs like KGs and citation networks.

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Hossein A. Rahmani, Xi Wang, Yue Feng, Qiang Zhang, Emine Yilmaz, Aldo Lipani

Natural Language Interfaces

Challenge:	Existing studies on Asking Clarification Questions (ACQs) are incomparable due to inconsistent data, experimental setups and evaluation strategies.
Approach:	They analyse the current research status on Asking Clarification Questions (ACQs) and propose a set of evaluation metrics and benchmarks for multiple ACQs-related tasks.
Outcome:	The proposed techniques are compared with the available datasets and evaluated against benchmarks.

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

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

Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking (2022.acl-long)

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

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Yue Feng, Aldo Lipani, Fanghua Ye, Qiang Zhang, Emine Yilmaz

Natural Language Interfaces

Challenge:	Existing approaches to model the relations between domains and slots fail to address these issues and can be generalized to unseen domains.
Approach:	They propose a Dynamic Schema Graph Fusion Network which generates a dynamic schema graph to explicitly fuse prior slot-domain membership relations and dialogue-aware dynamic slot relations.
Outcome:	The proposed model outperforms existing methods on benchmark datasets showing that it can extract users' goals or intentions as dialogue states and keep them updated over the whole dialogue.

RepoDistill: Distilling Repository Knowledge through Compression-Aware Budget Allocation and Policy Optimization (2026.findings-acl)

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Xin Yin, Zixiang Ding, Yiang Zhang, Qiang Wang, Rui Wang, Chao Ni, Zhe Cui

TARo: Token-level Adaptive Routing for LLM Test-time Alignment (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have strong performance on code translation tasks, but they struggle with repository-level scenarios where context is extensive and interdependent.
Approach:	They propose a framework that integrates retrieval with learning budget allocation for fine-grained context compression.
Outcome:	The proposed framework outperforms baselines on SWE-QA, CoderEval, and LongCodeU.

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Arushi Rai, Qiang Zhang, Hanqing Zeng, Yunkai Zhang, Dipesh Tamboli, Xiangjun Fan, Zhuokai Zhao, Lizhu Zhang

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

Challenge:	Large language models (LLMs) exhibit strong reasoning capabilities but typically require expensive post-training to reach high performance.
Approach:	They propose to use token-level Adaptive Routing to steer frozen LLMs toward structured reasoning entirely at inference time.
Outcome:	Extensive experiments show that TARo significantly improves reasoning performance by up to +22.4% over base model and +8.4% .

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

Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims (2021.acl-long)

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

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Qiang Sheng, Juan Cao, Xueyao Zhang, Xirong Li, Lei Zhong

Probing Relative Interaction and Dynamic Calibration in Multi-modal Entity Alignment (2025.acl-long)

Challenge:	Existing methods to detect false claims ignore the characteristics of FC-articles . claims are often quoted to describe checked events, providing lexical information . sentence templates to introduce or debunk claims are common across articles, providing pattern information.
Approach:	They propose a model to rerank FC-articles using key sentences and pattern information.
Outcome:	The proposed model outperforms existing methods on two real-world datasets showing that key sentences can be used to predict if an article fact-checks the given claim.

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Chenxiao Li, Jingwei Cheng, Qiang Tong, Fu Zhang, Cairui Wang

An Auxiliary Task Boosted Multi-task Learning Method for Service Account Retrieval with Limited Human Annotation (2023.emnlp-industry)

Challenge:	Current methods for multi-modal entity alignment ignore relative interactions between modalities and the accuracy of weights.
Approach:	They propose a relative interaction and calibration framework for multi-modal entity alignment that uses attention mechanisms to perceive the uncertainty of the weight for each modality.
Outcome:	The proposed framework outperforms baselines across 5 datasets and 23 settings.

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Yuanzhou Yao, Zhao Zhang, Kaijia Yang, Huasheng Liang, Qiang Yan, Yongjun Xu

ReCreate: Reasoning and Creating Domain Agents Driven by Experience (2026.acl-long)

Challenge:	Existing approaches to service account retrieval have limited human annotation, resulting in labor-intensive and time-consuming tasks.
Approach:	They propose an Auxiliary task Boosted Multi-Task Learning method which introduces multiple auxiliary tasks and enhances the performance of the main task, service account retrieval.
Outcome:	The proposed method improves the performance of the main task, service account retrieval.

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Zhezheng Hao, Hong Wang, Jian Luo, Jianqing Zhang, Yuyan Zhou, Qiang Lin, Can Wang, Hande Dong, Jiawei Chen

RealChart2Code: Bridging the Gap in Real-World Chart-to-Code Generation via Multi-Task Evaluation (2026.acl-long)

Challenge:	Large Language Model (LLM) agents are reshaping the industrial landscape, but tasks differ widely, making them labor-intensive to build.
Approach:	They propose an experience-driven framework for the automatic creation of domain agents . they leverage agent interaction histories to provide rich concrete signals on success or failure .
Outcome:	The proposed framework outperforms human-designed agents and existing methods in experiments across diverse domains.

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Jiajun Zhang, Yuying Li, Zhixun Li, Xingyu Guo, Jingzhuo Wu, Leqi Zheng, Yiran Yang, Jianke Zhang, Qingbin Li, Shannan Yan, Changguo Jia, Junfei Wu, Zilei Wang, Qiang Liu, Liang Wang

Text Generation

Challenge:	Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains, but their ability to replicate complex, multi-panel visualizations remains largely unassessed.
Approach:	They propose a large-scale benchmark to evaluate chart generation from large- scale raw data and assess iterative code refinement in a multi-turn conversational setting.
Outcome:	The new benchmark evaluates 14 leading VLMs on real-world data and shows they struggle with complex plot structures and authentic data.

UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions (2026.acl-long)

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Chunyu Qiang, Xiaopeng Wang, Kang Yin, Yuzhe Liang, Yuxin Guo, Teng Ma, Ziyu Zhang, Tianrui Wang, Cheng Gong, Yushen Chen, Ruibo Fu, Longbiao Wang, Jianwu Dang

Early Exit with Disentangled Representation and Equiangular Tight Frame (2023.findings-acl)

Challenge:	Generative audio modeling has been fragmented into specialized tasks such as text-to-speech (TTS), text- to-music (TTM), and text-ta (TTA) specialized models require reference audio for timbre cloning and strict phoneme alignment, whereas TTA models generate unstructured textures from open-ended captions.
Approach:	They propose a unified flow-matching framework capable of synthesizing speech, music, sound effects . they propose 'token injection mechanism' that projects unstructured environmental sounds into structured temporal latent space .
Outcome:	The proposed framework achieves state-of-the-art performance in instruction-based TTS and TTM while maintaining competitive fidelity in TTA.

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Yixin Ji, Jikai Wang, Juntao Li, Qiang Chen, Wenliang Chen, Min Zhang

Representation Learning

Challenge:	Existing early exit paradigm relies on training parametrical internal classifiers to complete specific tasks.
Approach:	They propose a method to decouple two distinct types of representation and introduce a non-parametric tight frame classifier for improvement.
Outcome:	Experiments on monolingual and multilingual tasks show that the proposed method improves over existing methods.

SINCon: Mitigate LLM-Generated Malicious Message Injection Attack for Rumor Detection (2025.acl-long)

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Mingqing Zhang, Qiang Liu, Xiang Tao, Shu Wu, Liang Wang

Understanding and Improving the Robustness of Terminology Constraints in Neural Machine Translation (2023.acl-long)

Challenge:	Existing methods define important nodes as important and target them for attacks if the model treats nodes’ predictive influence more uniformly . Existing approaches target high predictive influence nodes but are vulnerable to malicious message injection attacks.
Approach:	They propose a defense mechanism that encourages the model to learn graph representations where nodes with varying importance have a more uniform influence on predictions.
Outcome:	Extensive experiments on the Twitter and Weibo datasets show that similarizing the predictive Influence of nodes with Contrastive Learning significantly enhances resistance against LLM-driven message injection attacks.

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Huaao Zhang, Qiang Wang, Bo Qin, Zelin Shi, Haibo Wang, Ming Chen

Multilinguality

Challenge:	Existing terminology constraint test sets are blind to this issue due to oversimplified settings . PH methods retain high constraint accuracy but lower translation quality .
Approach:	They propose a method that replaces terminology terms with ordered labels . placeholder methods are better at retaining high constraint accuracy but lower translation quality .
Outcome:	The proposed method achieves high accuracy and translation quality regardless of the number or length of constraints.

Personalized Text Generation with Contrastive Activation Steering (2025.acl-long)

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Jinghao Zhang, Yuting Liu, Wenjie Wang, Qiang Liu, Shu Wu, Liang Wang, Tat-Seng Chua

Information Retrieval

Challenge:	Existing approaches to personalized text generation rely on retrieval-augmented generation and parameter-efficient fine-tuning.
Approach:	They propose a training-free framework that disentangles and represents personalized writing style as a vector in LLM’s activation-space.
Outcome:	The proposed framework achieves 8% relative improvement in personalized generation while reducing storage requirements by 1700 over PEFT method.

Making Language Models Better Reasoners with Step-Aware Verifier (2023.acl-long)

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Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, Weizhu Chen

Mixture of Decoding: An Attention-Inspired Adaptive Decoding Strategy to Mitigate Hallucinations in Large Vision-Language Models (2025.findings-acl)

Challenge:	Large language models have made impressive progress in few-shot learning but still face difficulties in reasoning tasks such as GSM8K.
Approach:	They propose a new approach that uses a verifier to filter out incorrect answers based on a weighted voting scheme to improve reasoning ability of language models.
Outcome:	The proposed approach improves GSM8K reasoning rate by 17.9% to 58.1%.

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Xinlong Chen, Yuanxing Zhang, Qiang Liu, Junfei Wu, Fuzheng Zhang, Tieniu Tan

Dual Attention Model for Citation Recommendation (2020.coling-main)

Challenge:	Large Vision-Language Models (LVLMs) have impressive capabilities across visual tasks, yet they remain hindered by the persistent challenge of hallucinations.
Approach:	They propose a novel approach that dynamically adapts decoding strategies by evaluating the correctness of the model’s attention on image tokens to distinguish the correct attention.
Outcome:	Extensive experiments show that the proposed approach outperforms existing decoding methods across multiple mainstream benchmarks, effectively mitigating hallucinations in LVLMs.

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Yang Zhang, Qiang Ma

Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem (2022.findings-acl)

Challenge:	Existing methods for recommending citations suffer from severe information loss . citation recommender methods do not consider the section of the paper for which the user is writing and for which they need to find a citation .
Approach:	They propose a novel embedding-based neural network to recommend citations during manuscript preparation.
Outcome:	The proposed method can recommend citations during manuscript preparation.

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Qiang Zhang, Jason Naradowsky, Yusuke Miyao

Responsible & Trustworthy NLP

Challenge:	Existing methods of offensive text detection perform poorly when asked to detect implicitly offensive statements . a dataset based on SLIGHT provides a framework for implicit offensive text identification .
Approach:	They propose a dataset to support the task of implicit offensive text detection in dialogues . they show that reasoning is crucial for understanding this broader class of offensive utterances - SLIGHT .
Outcome:	The proposed model achieves 11% accuracy in implicit offensive text detection tasks . the proposed model can be used to identify toxic speech in specific domains .

PersonaX: A Recommendation Agent-Oriented User Modeling Framework for Long Behavior Sequence (2025.findings-acl)

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Yunxiao Shi, Wujiang Xu, Zhang Zeqi, Xing Zi, Qiang Wu, Min Xu

Challenge:	Existing methods for user profile modeling extract only partial segments from full historical behavior sequence, resulting in incomplete modeling and suboptimal profiling.
Approach:	They propose an agent-agnostic LLM-UM framework to augment downstream recommendation agents . it segments complete historical behaviors into clustered groups and performs offline multi-persona profiling .
Outcome:	The proposed framework improves agent performance and inference efficiency by 31% and 10% using 30–50% of behavioral data.

KELE: Residual Knowledge Erasure for Enhanced Multi-hop Reasoning in Knowledge Editing (2025.findings-emnlp)

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Mengqi Zhang, Bowen Fang, Qiang Liu, Xiaotian Ye, Shu Wu, Pengjie Ren, Zhumin Chen, Liang Wang

VidCapBench: A Comprehensive Benchmark of Video Captioning for Controllable Text-to-Video Generation (2025.findings-acl)

Challenge:	Existing knowledge editing techniques show limitations when applied to multi-hop reasoning . residual single-hop knowledge causes edited models to revert to original answers .
Approach:	They propose a knowledge editing method that incorporates a Knowledge Erasure mechanism for Large language model Editing (KELE) they propose an erasure function for residual knowledge and an injection function for new knowledge .
Outcome:	The proposed method significantly improves multi-hop reasoning capability of edited models.

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Xinlong Chen, Yuanxing Zhang, Chongling Rao, Yushuo Guan, Jiaheng Liu, Fuzheng Zhang, Chengru Song, Qiang Liu, Di Zhang, Tieniu Tan

AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based on Meta Learning (2024.naacl-long)

Challenge:	Existing studies have not identified a link between video caption evaluation and T2V generation.
Approach:	They propose a video caption evaluation scheme specifically designed for T2V generation that integrates video annotation with caption evaluation.
Outcome:	The proposed system is agnostic to any particular caption format and can be used for training.

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Ruiyi Zhang, Rushi Qiang, Sai Ashish Somayajula, Pengtao Xie

Exploring the Impacts of Feature Fusion Strategy in Multi-modal Entity Alignment (2025.coling-main)

Challenge:	Large-scale pretraining followed by task-specific finetuning has achieved great success in various NLP tasks.
Approach:	They propose a meta learning based framework for automatically identifying the optimal rank of each LoRA layer.
Outcome:	The proposed framework is based on a meta learning based framework that can identify the optimal rank of each LoRA layer.

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Chenxiao Li, Jingwei Cheng, Qiang Tong, Fu Zhang

A Black-Box Attack on Code Models via Representation Nearest Neighbor Search (2023.findings-emnlp)

Challenge:	Existing approaches to merge multi-modal knowledge only use one fusion strategy . however, the impact of the fusion on individual entities could be ignored .
Approach:	They propose an adaptive multi-modal feature fusion strategy for entity alignment that selects the optimal entity-level feature blending strategy.
Outcome:	The proposed model achieves state-of-the-art (SOTA) performance compared to models using the same modality on a dataset with multiple inconsistent images and styles.

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Jie Zhang, Wei Ma, Qiang Hu, Shangqing Liu, Xiaofei Xie, Yves Le Traon, Yang Liu

CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs (2026.findings-acl)

Challenge:	Existing methods for generating adversarial code examples face challenges such as limted availability of substitute variables and the creation of adversarials with noticeable perturbations.
Approach:	They propose a search seed based on historical attacks to find adversarial substitutes . they employ a pre-trained variable name encoder to map the search seed to a continuous vector space .
Outcome:	The proposed approach outperforms baseline methods in terms of ASR and QT.

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Qiuyi Qi, Jinjian Zhang, Mutian Bao, Tian Liang, Guocong Li, Dongnan Liu, Wei Zhou, Jie Liu, Ming Kong, Linjian Mo, Feng Zhang, Qiang Zhu

Event-Centric Natural Language Processing (2021.acl-tutorials)

Challenge:	Existing approaches to constraint-aware planning fail to enhance the model’s intrinsic focus on constraints.
Approach:	They propose a constraint-aware reinforcement learning framework that encourages constraint focus and penalizes neglect of LLMs.
Outcome:	The proposed framework outperforms existing frameworks and state-of-the-art reasoning models in a number of real-world applications.

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Muhao Chen, Hongming Zhang, Qiang Ning, Manling Li, Heng Ji, Kathleen McKeown, Dan Roth

Challenge:	This tutorial will provide an introduction to various methods for automating the extraction, conceptualization and prediction of events and their relations.
Approach:	This tutorial will provide an introduction to various methods for automating events and their relations, and a wide range of NLU and commonsense understanding tasks.
Outcome:	This tutorial will provide an introduction to various methods for automating extraction, conceptualization and prediction of events and their relations, and a wide range of NLU and commonsense understanding tasks.

Generate First, Then Sample: Enhancing Fake News Detection with LLM-Augmented Reinforced Sampling (2025.acl-long)

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Zhao Tong, Yimeng Gu, Huidong Liu, Qiang Liu, Shu Wu, Haichao Shi, Xiao-Yu Zhang

Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce (2022.findings-emnlp)

Challenge:	Existing models have a performance gap of 20% between classifying fake news and real news, making them less suitable for practical deployment.
Approach:	They propose to adopt an LLM to generate fake news in three different styles, which are later incorporated into the training set to augment the representation of fake news.
Outcome:	The proposed model achieves state-of-the-art performance on two benchmark datasets and improves detection accuracy by 24.02% and 11.06% respectively.

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Yincen Qu, Ningyu Zhang, Hui Chen, Zelin Dai, Chengming Wang, Xiaoyu Wang, Qiang Chen, Huajun Chen

Commonsense Reasoning

Challenge:	Existing models rank statements solely by confidence scores, and there is no information about which ones are salient from a human perspective.
Approach:	They propose a task where a model is required to learn whether a triple is salient . they propose supervised salience evaluation using a new Benchmark dataset .
Outcome:	The proposed task is based on a new Benchmark dataset of salience evaluation in e-commerce . it shows that saliency evaluation is hard, where models perform poorly on evaluation set .

GAPO: Robust Advantage Estimation for Real-World Code LLMs (2026.findings-acl)

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Jianqing Zhang, Zhezheng Hao, Wei Xia, Hande Dong, Hong Wang, Chenxing Wei, Yuyan Zhou, Yubin Qi, Qiang Lin, Jian Cao

Extracting Temporal Event Relation with Syntax-guided Graph Transformer (2022.findings-naacl)

Challenge:	Reinforcement learning (RL) is widely used for post-training large language models (LLMs) in code editing, but in real-world code editing scenarios, reward distributions are often skewed with unpredictable noise, leading to distorted advantage computation and increased rollout outliers.
Approach:	They propose a group-relative method that finds an interval with the highest SNR and uses the median of that interval as an adaptive Q to replace the group mean in advantage calculation.
Outcome:	The proposed method improves on nine instruction-tuned LLMs while remaining plug-and-play and efficient.

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Shuaicheng Zhang, Qiang Ning, Lifu Huang

Context-Fidelity Boosting: Enhancing Faithful Generation through Watermark-Inspired Decoding (2026.findings-acl)

Challenge:	Temporal relationship extraction is crucial for understanding complex events and reasoning over them.
Approach:	They propose a Syntax-guided Graph Transformer network to extract temporal relations between events by explicitly exploiting the connection between two events based on their dependency parsing trees.
Outcome:	The proposed approach outperforms state-of-the-art methods on MATRES and TB-DENSE with up to 7.9% absolute F-score gain.

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Weixu Zhang, Fanghua Ye, Qiang Gao, Jian Li, Haolun Wu, Yuxing Tian, Sijing Duan, Nan Du, Xiaolong Li

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

Challenge:	Large language models produce content that contradicts or overlooks information provided in the input context, a phenomenon known as faithfulness hallucination.
Approach:	They propose a lightweight framework that boosts the generation probability of context-relevant tokens by boosting the generation of tokens.
Outcome:	The proposed framework improves faithfulness metrics with minimal generation overhead.

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

Open Domain Question Answering with Conflicting Contexts (2025.findings-naacl)

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

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Siyi Liu, Qiang Ning, Kishaloy Halder, Zheng Qi, Wei Xiao, Phu Mon Htut, Yi Zhang, Neha Anna John, Bonan Min, Yassine Benajiba, Dan Roth

Natural Language Interfaces

Challenge:	Open domain question answering systems often rely on information retrieved from large collections of text to answer questions.
Approach:	They evaluate and benchmark three powerful Large Language Models with a dataset . they find that 25% of unambiguous open domain questions can lead to conflicting contexts .
Outcome:	The proposed model can't be used to answer questions with conflicting contexts . it can be fine tuned to provide richer information into the model's training .

Ask an Expert: Leveraging Language Models to Improve Strategic Reasoning in Goal-Oriented Dialogue Models (2023.findings-acl)

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Qiang Zhang, Jason Naradowsky, Yusuke Miyao

Dialogue Systems & Conversational Agents

Challenge:	Existing dialogue models may encounter scenarios which are not well-represented in the training data and produce unnatural, inappropriate, or unhelpful responses.
Approach:	They propose a framework in which a model is trained with access to an "expert" they propose to optimize the model to selectively utilize (or ignore) advice given context and dialogue history.
Outcome:	The proposed framework improves quality across all expert sizes and with fewer parameters than the dialogue model itself.

SelfMix: Robust Learning against Textual Label Noise with Self-Mixup Training (2022.coling-1)

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Dan Qiao, Chenchen Dai, Yuyang Ding, Juntao Li, Qiang Chen, Wenliang Chen, Min Zhang

Information Extraction & Text Mining

Challenge:	Existing methods to handle label noise in text classification tasks are limited to visual data.
Approach:	They propose a method to handle label noise in text classification tasks using a Gaussian Mixture Model.
Outcome:	The proposed method outperforms baselines on three types of text classification tasks on visual and textual data.

Mind the Gap Between Conversations for Improved Long-Term Dialogue Generation (2023.findings-emnlp)

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Qiang Zhang, Jason Naradowsky, Yusuke Miyao

Dialogue Systems & Conversational Agents

Challenge:	a gap between conversations can be weeks, months or years, and dialogue systems which do not explicitly model time may generate unnatural responses.
Approach:	They propose to model the passage of time between conversations by exposing time information to a multi-session dialogue dataset and comparing different representations of time and event progress.
Outcome:	The proposed model is based on a real-time dataset showing that it can predict topics and information gained from conversations over a long time span.

Attention and Edge-Label Guided Graph Convolutional Networks for Named Entity Recognition (2022.emnlp-main)

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Renjie Zhou, Zhongyi Xie, Jian Wan, Jilin Zhang, Yong Liao, Qiang Liu

Syntactic Text Processing

Challenge:	Named entity recognition (NER) is the recognition of entities with specific meanings in the text, mainly including person, organization, location, etc.
Approach:	They propose an edge-aware node joint update module and introduce a node-awful edge update module to explore hidden in structured information and solve the wrong dependency label information to some extent.
Outcome:	The proposed model can exploit the structured information on the dependency tree to improve the recognition of long entities.

Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models (2025.findings-emnlp)

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Weihang Wang, Xinhao Li, Ziyue Wang, Yan Pang, Jielei Zhang, Peiyi Li, Qiang Zhang, Longwen Gao

Zoom Out and Observe: News Environment Perception for Fake News Detection (2022.acl-long)

Challenge:	Existing benchmarks focus on coarse-grained hallucination detection and fail to capture hallucinics . vision encoders exhibit unique hallucinian characteristics, but suboptimal of simple feature fusion.
Approach:	They propose a visual encoder that employs different training paradigms to instill inductive biases in visual encoded models.
Outcome:	The proposed system reduces hallucinations and improves model performance.

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Qiang Sheng, Juan Cao, Xueyao Zhang, Rundong Li, Danding Wang, Yongchun Zhu

HSGraphAgent: Knowledge-Graph-Guided Large Language Models for Harmonized System Code Classification (2026.acl-long)

Challenge:	Existing methods for fake news detection "zoom in" to verify content with knowledge sources or check readers’ replies to posts but neglect information in the external news environment where a fake news post is created and disseminated.
Approach:	They propose a framework to capture news environment signals and a module to perceive useful signals and assist final prediction.
Outcome:	The proposed framework can improve the performance of basic fake news detectors by capturing the environmental signals of news posts and analyzing the results.

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Qiang Xia, Zijian Zhang, Ao Wang, Wenhan Wang, Xiangyu Wang, Jian Li

MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning (2026.findings-acl)

Challenge:	Harmonized System (HS) code classification is a hierarchically structured and regulation-constrained task, often complicated by short and noisy product descriptions.
Approach:	They propose a knowledge-graph-guided LLM framework that formulates HS classification as a stepwise, regulation-aware reasoning process over an explicit HS knowledge graph.
Outcome:	The proposed framework constrains inference to legally valid paths while producing explicit and traceable reasoning trajectories.

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Xukai Wang, Xuanbo Liu, Mingrui Chen, Haitian Zhong, Xuanlin Yang, Bohan Zeng, Jinbo Hu, Hao Liang, Junbo Niu, Xuchen Li, Ruitao Wu, Ruichuan An, Yang Shi, Liu Liu, Qiang Liu, Zhouchen Lin, Xu-Yao Zhang, Wentao Zhang, Bin Dong

DeepRTL2: A Versatile Model for RTL-Related Tasks (2025.findings-acl)

Challenge:	Existing benchmarks designed to evaluate the reasoning capabilities of large models are limited in scope and lack flexibility to adapt difficulty according to evolving reasoning capacities of models.
Approach:	They propose a benchmark that incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.
Outcome:	The proposed benchmark incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.

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Yi Liu, Hongji Zhang, Yunhao Zhou, Zhengyuan Shi, Changran Xu, Qiang Xu

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

Challenge:	Integration of large language models into electronic design automation has been a key driver in eDA.
Approach:	They propose a family of large language models that unifies generation- and embedding-based tasks related to RTL.
Outcome:	The proposed model achieves state-of-the-art performance across all evaluated tasks.

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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.

Chinese Idiom Paraphrasing (2023.tacl-1)

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Jipeng Qiang, Yang Li, Chaowei Zhang, Yun Li, Yi Zhu, Yunhao Yuan, Xindong Wu

Text Generation

Challenge:	Chinese idioms are hard to understand by children and non-native speakers due to their non-compositionality and metaphorical meaning.
Approach:	They propose a task to rephrase idiom-containing sentences to non-idiomatic ones under the premise of preserving the original sentence’s meaning.
Outcome:	The proposed method has better performance than baselines based on the established dataset.

Toolscaler: Scalable Generative Tool Calling via Structure-Aware Semantic Tokenization (2025.findings-emnlp)

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Yunyue Su, Zhang Jinshuai, Bowen Fang, Wen Ye, Jinghao Zhang, Bowen Song, Weiqiang Wang, Qiang Liu, Liang Wang

Syntactic Text Processing

Challenge:	Extensive experiments demonstrate the effectiveness of SGTC across various tasks.
Approach:	They propose a generative tool invocation framework that introduces structure-aware semantic tokenization to encode tools as discrete code sequences.
Outcome:	The proposed framework reduces the size of the representation space and underutilizes collaborative signals among tools in downstream tasks.

Dual-Alignment Pre-training for Cross-lingual Sentence Embedding (2023.acl-long)

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Ziheng Li, Shaohan Huang, Zihan Zhang, Zhi-Hong Deng, Qiang Lou, Haizhen Huang, Jian Jiao, Furu Wei, Weiwei Deng, Qi Zhang

SAM3-I: Segment Anything with Instructions (2026.acl-long)

Challenge:	Recent studies have shown that dual encoder models trained with the sentence-level translation ranking task are effective methods for cross-lingual sentence embedding.
Approach:	They propose a dual-alignment pre-training framework that incorporates both sentence-level and token-level alignment.
Outcome:	The proposed framework improves cross-lingual sentence embedding on three cross-linguistic benchmarks.

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Jingjing Li, Yue Feng, Yuchen Guo, Jincai Huang, Wei Ji, Qi Bi, Yongri Piao, Miao Zhang, Xiaoqi Zhao, Qiang Chen, Shihao Zou, Huchuan Lu, Li Cheng

Low-Resource NLP

Challenge:	Existing methods for concept-level grounding and instruction-level reasoning use coarse representations and iterative mask filtering.
Approach:	They propose an instruction-following extension of the Segment Anything Model 3 family that unifies concept-level grounding and instruction-level reasoning within a single segmentation framework.
Outcome:	Experiments show that SAM3-I achieves appealing performance across referring and reasoning-based segmentation while maintaining its strong concept recall ability.

Knowledge Graph Enhanced Large Language Model Editing (2024.emnlp-main)

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Mengqi Zhang, Xiaotian Ye, Qiang Liu, Pengjie Ren, Shu Wu, Zhumin Chen

MapNav: A Novel Memory Representation via Annotated Semantic Maps for VLM-based Vision-and-Language Navigation (2025.acl-long)

Challenge:	Existing methods for editing large language models struggle to track and incorporate changes in knowledge associated with edits, which limits the generalization ability of post-edit LLMs in processing edited knowledge.
Approach:	They propose a model editing method that leverages knowledge graphs to enhance LLM editing by capturing changes in associated knowledge by constructing an external graph.
Outcome:	The proposed method improves the generalization ability of LLMs in processing edited knowledge.

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Lingfeng Zhang, Xiaoshuai Hao, Qinwen Xu, Qiang Zhang, Xinyao Zhang, Pengwei Wang, Jing Zhang, Zhongyuan Wang, Shanghang Zhang, Renjing Xu

Learning Latent Relations for Temporal Knowledge Graph Reasoning (2023.acl-long)

Challenge:	Vision-language navigation (VLN) is a key task in Embodied AI . traditional approaches rely on historical observations as spatio-temporal contexts for decision making .
Approach:	They propose a vision-language navigation model that leverages an annotation system to replace historical frames.
Outcome:	The proposed model can be used as a new memory representation method in vision-language navigation . it can be applied to simulated and real-world environments, and it is validated by experiments .

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Mengqi Zhang, Yuwei Xia, Qiang Liu, Shu Wu, Liang Wang

Structured Data in NLP

Challenge:	Existing methods for Temporal Knowledge Graph reasoning capture intra- and inter-time latent relations between entities that appear at different times.
Approach:	They propose a Latent relations Learning method for TKG reasoning that captures latent relations between entities at different times.
Outcome:	The proposed method exploits the intra- and inter-time latent relations of entities at different times.

MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning (2022.emnlp-main)

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Yuwei Xia, Mengqi Zhang, Qiang Liu, Shu Wu, Xiao-Yu Zhang

Deep Reinforcement Learning for Entity Alignment (2022.findings-acl)

Challenge:	Existing models rely on historical information to learn embeddings for entities, but ignore the evolution of facts.
Approach:	They propose a Temporal Meta-learning framework to learn evolutionary meta-knowledge from TKGs.
Outcome:	The proposed method improves on four widely-used datasets and three backbones on a wide range of scenarios on tKGs.

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Lingbing Guo, Yuqiang Han, Qiang Zhang, Huajun Chen

Representation Learning

Challenge:	Entity alignment (EA) methods identify the aligned entities based on cosine similarity, ignoring the semantics underlying the embeddings themselves.
Approach:	They propose to model entity alignment as a sequential decision-making task where an agent sequentially decides whether two entities are matched or mismatched based on representation vectors.
Outcome:	The proposed framework consistently advances the performance of several state-of-the-art methods, with a maximum improvement of 31.1% on Hits@1.

Evaluating Models’ Local Decision Boundaries via Contrast Sets (2020.findings-emnlp)

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Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, Ben Zhou

Challenge:	Standard test sets for supervised learning evaluate in-distribution generalization but are misleading when a dataset has systematic gaps.
Approach:	They propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data.
Outcome:	The proposed model performs significantly lower on contrast sets than on the original test sets—up to 25% in some cases.

OpenOmni: A Collaborative Open Source Tool for Building Future-Ready Multimodal Conversational Agents (2024.emnlp-demo)

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Qiang Sun, Yuanyi Luo, Sirui Li, Wenxiao Zhang, Wei Liu

Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition (2025.acl-long)

Challenge:	OpenOmni is an open-source, end-to-end pipeline benchmarking tool for multimodal conversational agents.
Approach:	They developed an open-source, end-to-end pipeline benchmarking tool to help solve these issues.
Outcome:	OpenOmni integrates speech-to-text, emotion detection, and large language models with the ability to integrate customized models.

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Kehua Feng, Keyan Ding, Tan Hongzhi, Kede Ma, Zhihua Wang, Shuangquan Guo, Cheng Yuzhou, Ge Sun, Guozhou Zheng, Qiang Zhang, Huajun Chen

Layer-Wise Multi-View Learning for Neural Machine Translation (2020.coling-main)

Challenge:	Existing methods for evaluation of large language models are inefficient and inefficient due to inaccuracy of standard metrics in human perception of text quality and inefficiency in sampling informative test examples.
Approach:	They propose a sample-efficient human evaluation method for large language models based on the principle of MAximum Discrepancy (MAD) competition.
Outcome:	The proposed method achieves the “golden” ranking of LLMs with a minimum set of input instructions, which in turn reveal their relative strengths and weaknesses.

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Qiang Wang, Changliang Li, Yue Zhang, Tong Xiao, Jingbo Zhu

Machine Translation

Challenge:	Existing approaches to neural machine translation are limited to the topmost encoder layer’s context representation and cannot perceive the lower encoder layers.
Approach:	They propose a layer-wise multi-view learning approach to solve this problem by incorporating an auxiliary view into the model.
Outcome:	The proposed model can achieve stable results over multiple strong baselines and is agnostic to network architectures.

Generative Giants, Retrieval Weaklings: Why do Multimodal Large Language Models Fail at Multimodal Retrieval? (2026.findings-acl)

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Hengyi Feng, Zeang Sheng, Meiyi Qiang, Yang Li, Wentao Zhang

Plug-and-Play Data Module for Code RL: Adaptive Ambiguity Replay (2026.findings-acl)

Challenge:	Rapid advances in multimodal large language models have revolutionized cross-modality understanding.
Approach:	They propose a method that uses whitening transformations to adjust MLLM representation spaces . they propose ML models that are dominated by textual semantics and visual semantics .
Outcome:	The proposed approach improves zero-shot multimodal retrieval performance without fine-tuning efforts.

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Jianqing Zhang, Wei Xia, Zhezheng Hao, Hong Wang, Hande Dong, Qiang Lin, Yang Liu, Jian Cao, Qiang Yang

RIVAL: Reinforcement Learning with Iterative and Adversarial Optimization for Machine Translation (2025.findings-emnlp)

Challenge:	Existing approaches to reinforcement learning (RL) rely on static, in-epoch metrics that overlook training dynamics, often introducing low-utility or outdated data.
Approach:	They propose a plug-and-play module that prioritizes cross-epoch ambiguous samples to neutralize the noise from stale experiences.
Outcome:	Extensive experiments on nine LLMs show that Adaptive Ambiguity Replay outperforms state-of-the-art baselines on real-world code editing tasks.

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Tianjiao Li, Mengran Yu, Chenyu Shi, Yanjun Zhao, Xiaojing Liu, Qi Zhang, Xuanjing Huang, Qiang Zhang, Jiayin Wang

Text Generation

Challenge:	Using reinforcement learning from human feedback, large language models perform poorly when applied to colloquial subtitle translation tasks.
Approach:	They propose an adversarial training framework that iteratively updates the offline reward model and the online LLM to improve training outcomes.
Outcome:	The proposed training framework significantly improves upon translation baselines.

Boosting LLM’s Molecular Structure Elucidation with Knowledge Enhanced Tree Search Reasoning (2025.acl-long)

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Xiang Zhuang, Bin Wu, Jiyu Cui, Kehua Feng, Xiaotong Li, Huabin Xing, Keyan Ding, Qiang Zhang, Huajun Chen

GenKIE: Robust Generative Multimodal Document Key Information Extraction (2023.findings-emnlp)

Challenge:	Molecular structure elucidation involves deducing a molecule’s structure from various types of spectral data, which is crucial in chemical experimental analysis.
Approach:	They propose a Knowledge-enhanced reasoning framework for Molecular Structure Elucidation that leverages Monte Carlo Tree Search for test-time scaling as a plugin to extend the LLMs’ coverage of the chemical structure space.
Outcome:	The proposed framework significantly improves on both GPT-4o-mini and GPT4o, and a specialized molecule-spectrum scorer improves performance.

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Panfeng Cao, Ye Wang, Qiang Zhang, Zaiqiao Meng

LEPO: Latent Reasoning Policy Optimization for Large Language Models (2026.findings-acl)

Challenge:	Key information extraction (KIE) is a key application for information retrieval and text mining.
Approach:	They propose a novel generative end-to-end model, named GenKIE, to address the KIE task.
Outcome:	The proposed model generalizes over different types of documents and achieves state-of-the-art results.

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Yuyan Zhou, Jiarui Yu, Hande Dong, Zhezheng Hao, Hong Wang, Jianqing Zhang, Qiang Lin

Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning (D19-1)

Challenge:	Existing latent reasoning methods that use chain of thought (CoT) are limited to selecting one discrete token at each reasoning step, which potentially induces information loss.
Approach:	They propose a framework that injects controllable stochasticity into latent reasoning via Gumbel-Softmax, restoring LLMs' exploratory capacity and enhancing their compatibility with Reinforcement Learning (RL).
Outcome:	The proposed framework preserves richer information for more comprehensive reasoning and is compatible with Reinforcement Learning (RL).

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Zheng Li, Xin Li, Ying Wei, Lidong Bing, Yu Zhang, Qiang Yang

Sentiment Analysis

Challenge:	Existing methods to extract aspects and sentiments are limited due to lack of annotated sequence data.
Approach:	They propose a Selective Adversarial Learning method to align latent correlation vectors . they propose tagging a set of aspect boundary tags and sentiment tags to create a joint label space .
Outcome:	The proposed method can learn weights for words to achieve fine-grained adaptation.

Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs (D19-1)

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Mingyang Chen, Wen Zhang, Wei Zhang, Qiang Chen, Huajun Chen

InstructProtein: Aligning Human and Protein Language via Knowledge Instruction (2024.acl-long)

Challenge:	Empirically, our model achieves state-of-the-art results on few-shot link prediction KG benchmarks.
Approach:	They propose a Meta Relational Learning framework to do few-shot link prediction in KGs by observing only a few associative triples.
Outcome:	The proposed model achieves state-of-the-art results on few-shot link prediction KG benchmarks.

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Zeyuan Wang, Qiang Zhang, Keyan Ding, Ming Qin, Xiang Zhuang, Xiaotong Li, Huajun Chen

Stealthy Attack on Large Language Model based Recommendation (2024.acl-long)

Challenge:	Large Language Models (LLMs) are a promising new approach to understanding biological sequences such as proteins.
Approach:	They propose an LLM that can generate protein sequences in human and protein languages by pre-training an Lm on protein and natural language corpora and supervised instruction tuning to facilitate alignment.
Outcome:	The proposed model outperforms state-of-the-art LLMs on protein-text generation tasks by a large margin.

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Jinghao Zhang, Yuting Liu, Qiang Liu, Shu Wu, Guibing Guo, Liang Wang

Mitigating Shortcut Learning via Smart Data Augmentation based on Large Language Model (2025.coling-main)

Challenge:	Recent advances in recommender systems have been overlooked due to their emphasis on textual content.
Approach:	They propose to introduce large language models into recommendation models to exploit the semantic understanding and strong transferability of LLMs.
Outcome:	The proposed approach significantly boosts an item’s exposure by altering its textual content during the testing phase, without requiring direct interference with the model’s training process.

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Xinyi Sun, Hongye Tan, Yaxin Guo, Pengpeng Qiang, Ru Li, Hu Zhang

Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers (2026.findings-acl)

Challenge:	Existing methods to improve shortcut learning performance are limited by manual definition of shortcuts and inherent confirmation bias during model training.
Approach:	They propose a method of Smart Data Augmentation based on Large Language Models to identify shortcuts and generate their anti-shortcut counterparts.
Outcome:	The proposed method shows an improvement of 5.61% across various natural language processing tasks.

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Zhiyang Zhang, Junda She, Kuo Cai, Bo Chen, Shiyao Wang, Xinchen Luo, Qiang Luo, Ruiming Tang, Han Li, Kun Gai, Guorui Zhou

UrbanGeoEval: A City-Scale Benchmark for Evaluating Large Language Models in Geospatial Reasoning (2026.acl-long)

Challenge:	Existing methods for constructing item identifiers face bottlenecks due to their large output space and expensive vocabulary expansion and alignment training.
Approach:	They propose to use Large Language Models to develop general-purpose, semantically-aware recommender systems that can be generalized and reusable.
Outcome:	Experiments on real-world datasets show that GRAM outperforms baselines and significantly outperformed baselines.

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Mutian Bao, Qiuyi Qi, Tian Liang, Jinjian Zhang, Wei Zhou, Ming Kong, Linjian Mo, Qiang Zhu

Foreseeing the Benefits of Incidental Supervision (2021.emnlp-main)

Challenge:	Extensive experiments on 18 widely used LLMs uncover critical insights: (1) models exhibit severe geographic biases and resolution gaps; (2) failures in complex multi-hop tasks stem from brittle foundational spatial skills rather than high-level logic deficits.
Approach:	They propose a dual-module framework that disentangles factual recall and spatial logic from the model's real capabilities in urban environments.
Outcome:	Extensive tests on 18 widely used LLMs reveal that models exhibit severe geographic biases and resolution gaps, and failures in complex multi-hop tasks often stem from brittle foundational spatial skills rather than high-level logic deficits.

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Hangfeng He, Mingyuan Zhang, Qiang Ning, Dan Roth

Challenge:	Real-world applications often require improved models by leveraging a range of cheap incidental supervision signals.
Approach:	They propose a unified PAC-Bayesian motivated informativeness measure that characterizes the uncertainty reduction provided by incidental supervision signals.
Outcome:	The proposed measure quantifies the value added by incidental supervision signals to sequence tagging tasks.

Overcoming Catastrophic Forgetting by Exemplar Selection in Task-oriented Dialogue System (2024.findings-acl)

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Chen Chen, Ruizhe Li, Yuchen Hu, Yuanyuan Chen, Chengwei Qin, Qiang Zhang

Dialogue Systems & Conversational Agents

Challenge:	Experimental results show that HESIT effectively alleviates catastrophic forgetting by exemplar selection, and achieves state-of-the-art performance on the largest CL benchmark of ToDs in terms of all metrics.
Approach:	They propose a method to overcome catastrophic forgetting in task-oriented dialogue systems by tracing their hyper-gradients and a retraining strategy that uses influential exemplars for periodic retrains.
Outcome:	The proposed method achieves state-of-the-art on the largest CL benchmark of ToDs in terms of all metrics.

From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning (2026.acl-long)

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Jiajun Zhang, Zeyu Cui, Jiaxi Yang, Lei Zhang, Yuheng Jing, Zeyao Ma, Tianyi Bai, Zilei Wang, Qiang Liu, Liang Wang, Binyuan Hui, Junyang Lin

Programming Languages in NLP

Challenge:	Fill-in-the-Middle (FIM) models suffer from performance degradation and prohibitive latency.
Approach:	They propose a search-and-replace infilling framework that integrates agentic verification and editing into a single-pass inference process.
Outcome:	The proposed framework harmonizes completion tasks with the instruction-following priors of Chat LLMs, extending the paradigm from static infilling to dynamic context-aware editing.

Deeper Insights Without Updates: The Power of In-Context Learning Over Fine-Tuning (2024.findings-emnlp)

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Qingyu Yin, Xuzheng He, Chak Tou Leong, Fan Wang, Yanzhao Yan, Xiaoyu Shen, Qiang Zhang

How Do In-Context Examples Affect Compositional Generalization? (2023.acl-long)

Challenge:	Fine-tuning and in-context learning are two prevalent methods in imbuing large language models with task-specific knowledge.
Approach:	They propose to use a circuit shift theory to explain why in-context learning is superior to fine-tuning for tasks with implicit patterns.
Outcome:	The proposed method can grasp deep patterns and significantly improve accuracy on implicit patterns, compared with fine-tuning and in-context learning.

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Shengnan An, Zeqi Lin, Qiang Fu, Bei Chen, Nanning Zheng, Jian-Guang Lou, Dongmei Zhang

Low-Resource NLP

Challenge:	In-context learning paradigms that focus on large corpus are limiting compositional generalization performance.
Approach:	They propose a test suite to investigate in-context compositional generalization . they propose to use examples that are structurally similar to the test case .
Outcome:	The proposed test suite investigates in-context compositional generalization performance . it finds that the performance can be affected by the selection of in-const examples .

Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning (2026.acl-long)

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Zelin Tan, Hejia Geng, Xiaohang Yu, Mulei Zhang, Guancheng Wan, Yifan Zhou, Qiang He, Xiangyuan Xue, Heng Zhou, Yutao Fan, Zhong-Zhi Li, Zaibin Zhang, Guibin Zhang, Chen Zhang, Zhenfei Yin, Philip Torr, Lei Bai

STAPO: Selective Trajectory-Aware Policy Optimization for LLM Agent Training (2026.acl-long)

Challenge:	elucidating scaling laws for large language models (LLMs) during pre-training remains unexplored.
Approach:	They characterize how model scale, data, and compute interact during pre-training . they find that large models consistently demonstrate superior compute and data efficiency .
Outcome:	The proposed scaling laws offer practical guidance for scaling reasoning capabilities through reinforcement learning post-training.

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Qiuyi Qi, Tian Liang, Mutian Bao, Jinjian Zhang, Dongnan Liu, Wei Zhou, Linjian Mo, Ming Kong, Jie Liu, Feng Zhang, Qiang Zhu

AI4Reading: Chinese Audiobook Interpretation System Based on Multi-Agent Collaboration (2025.acl-demo)

Challenge:	Prior work has explored step-level supervision using Shannon-entropy-based uncertainty signals, which conflate inherent state complexity with agent confidence.
Approach:	They propose a hierarchical group-based RL framework that leverages normalized entropy to locate outlier steps associated with trajectory neglect and optimizes them via a mechanism of trajectory-aware reward and trajectory-independent penalty.
Outcome:	Experiments on ALFWorld, WebShop, and Search-Augmented QA show that STAPO achieves state-of-the-art performance while substantially alleviating trajectory neglect.

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Minjiang Huang, Jipeng Qiang, Yi Zhu, Chaowei Zhang, Xiangyu Zhao, Kui Yu

Unified Hallucination Detection for Multimodal Large Language Models (2024.acl-long)

Challenge:	Interpretative audiobooks are becoming more popular, but their manual creation process remains time-consuming and resource-intensive.
Approach:	They propose a multi-agent collaboration system that leverages large language models and speech synthesis technology to generate podcast-like audiobook interpretations.
Outcome:	The proposed system is open source and open to the public.

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Xiang Chen, Chenxi Wang, Yida Xue, Ningyu Zhang, Xiaoyan Yang, Qiang Li, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen

Chinese Live-Streaming E-Commerce Morph Resolution: Datasets and Methods (2026.findings-acl)

Challenge:	despite significant strides in multimodal tasks, MLLMs are plagued by the critical issue of hallucination.
Approach:	They propose a meta-evaluation benchmark to facilitate evaluation of advancements in hallucination detection methods.
Outcome:	The proposed framework validates hallucinations robustly and provides strategic insights . MHaluBench is a meta-evaluation benchmark designed to facilitate evaluation .

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Jipeng Qiang, Jiahao Zhu, Yi Zhu, Chaowei Zhang

Explainability & Interpretability in NLP

Challenge:	Live-stream E-commerce faces significant challenges from morphs, deliberate linguistic variants used to evade real-time voice filters and amplify product claims illegally.
Approach:	They propose a framework that resolves morphs and generates structured explanations . they propose morph-aware dual-output refinement framework that detects inconsistencies .
Outcome:	The proposed framework improves morph resolution accuracy and interpretability.

Learn to Cross-lingual Transfer with Meta Graph Learning Across Heterogeneous Languages (2020.emnlp-main)

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Zheng Li, Mukul Kumar, William Headden, Bing Yin, Ying Wei, Yu Zhang, Qiang Yang

Enhancing Cross Text-Molecule Learning by Self-Augmentation (2024.findings-acl)

Challenge:	Existing mPLM-based methods focus on designing costly model pre-training while ignoring equally crucial downstream adaptation.
Approach:	They propose a meta graph learning method that extracts meta-knowledge from historical CLT experiences to learn to cross-lingual transfer.
Outcome:	The proposed method can learn to cross-lingual transfer by extracting meta-knowledge from historical CLT experiences (tasks) it can also capture intrinsic language relationships to explicitly guide cross-linguistic transfer.

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Yinuo Jiang, Xiang Zhuang, Keyan Ding, Qiang Zhang, Huajun Chen