Papers by Jian Zhao

Scaling Laws for Code: Every Programming Language Matters (2026.findings-acl)

Challenge:	Autoregressive (AR) and diffusion language models (DLMs) suffer from insufficient reasoning capabilities.
Approach:	They propose a fully connected Diffusion Language Model that uses a concept-level causal graph to guide attention to learn causal relationships between concepts.
Outcome:	The proposed model achieves a 12% improvement and 3.2 training speedup on the COT-OrderPerturb task, along with an average gain of 1.31% across six downstream reasoning tasks.

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Jian Yang, Shuyue Guo, Linzheng Chai, Wei Zhang, Aishan Liu, Chuan Hao, Zhoujun Li, Xin Zhao, Xianglong Liu, Weifeng Lv, Bryan Dai

NBDESCRIB: A Dataset for Text Description Generation from Tables and Code in Jupyter Notebooks with Guidelines (2025.findings-acl)

Challenge:	Existing studies focus on language-agnostic settings, neglecting the inherently multilingual nature of modern software development.
Approach:	They propose a proportion-dependent scaling law that prioritizes high-utility languages . they propose PLs to have varying effects during pre-training that affect model performance .
Outcome:	The proposed scaling law is based on 1000+ experiments across multiple languages and models.

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Xuye Liu, Tengfei Ma, Yimu Wang, Fengjie Wang, Jian Zhao

Document-Level Event Argument Extraction With a Chain Reasoning Paradigm (2023.acl-long)

Challenge:	Existing methods for Jupyter Notebooks focus on generating cell-level descriptions from code snippets or table outputs independently.
Approach:	They propose a task to generate personalized cell-level descriptions using code, tables, and user-written guidelines in Jupyter Notebooks.
Outcome:	The proposed task combines code, tables, and user-written guidelines with personalized descriptions to evaluate the performance of existing models.

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Jian Liu, Chen Liang, Jinan Xu, Haoyan Liu, Zhe Zhao

FlexQuant: A Flexible and Efficient Dynamic Precision Switching Framework for LLM Quantization (2025.findings-emnlp)

Challenge:	Document-level event argument extraction aims to identify event arguments beyond sentence level, where a significant challenge is to model long-range dependencies.
Approach:	They propose a chain reasoning paradigm which captures long-range interdependence due to the chains’ compositional nature and generates decomposable first-order logic rules for reasoning.
Outcome:	The proposed method outperforms previous methods on two benchmarks and is robust enough to defend against adversarial attacks.

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Fangxin Liu, Zongwu Wang, Jinhong Xia, Junping Zhao, Shouren Zhao, Jinjin Li, Jian Liu, Li Jiang, Haibing Guan

Green & Sustainable NLP

Challenge:	Existing methods for quantization of large language models struggle to adapt to dynamic workloads.
Approach:	a new framework optimizes the trade-off between inference speed and accuracy . FlexQuant enables fine-grained, layer-wise mixed-precision quantization .
Outcome:	a new framework optimizes the trade-off between inference speed and accuracy . it achieves a 1.3 speedup across diverse language tasks with negligible accuracy loss .

AGSC: Adaptive Granularity and Semantic Clustering for Uncertainty Quantification in Long-text Generation (2026.acl-long)

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Guanran Luo, Wentao Qiu, Wanru Zhao, Wenhan Lv, Zhongquan Jian, Meihong Wang, Qingqiang Wu

New Intent Discovery with Attracting and Dispersing Prototype (2024.lrec-main)

Challenge:	Existing methods for aggregating large-form outputs overlook the nuance of neutral information and suffer from the high computational cost of fine-grained decomposition.
Approach:	They propose a UQ framework that uses NLI neutral probabilities as triggers to distinguish irrelevance from uncertainty, reducing computation costs.
Outcome:	Experiments on BIO and LongFact show that the proposed framework reduces inference time by 60% compared to full atomic decomposition.

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Shun Zhang, Jian Yang, Jiaqi Bai, Chaoran Yan, Tongliang Li, Zhao Yan, Zhoujun Li

Text Clustering

Challenge:	Existing methods for detecting new intents with labeled data are not cluster-friendly . a robust prototypical attracting learning (RPAL) method is designed to compel instances to gravitate toward their corresponding prototype .
Approach:	They propose a robust and adaptive prototypical learning framework for globally distinct decision boundaries for both known and new intent categories.
Outcome:	The proposed method improves on CLINC, BANKING, and StackOverflow benchmarks on three challenging benchmarks.

Mixture-of-Modules: Reinventing Transformers as Dynamic Assemblies of Modules (2024.emnlp-main)

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Zhuocheng Gong, Ang Lv, Jian Guan, Wei Wu, Huishuai Zhang, Minlie Huang, Dongyan Zhao, Rui Yan

Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning (2025.acl-long)

Challenge:	Empirical results show that MoMs consistently outperform vanilla transformers .
Approach:	They propose an architecture that allows for a mixture-of-modules computation that uses a finite set of modules defined by multi-head attention and feed-forward networks.
Outcome:	The proposed architecture outperforms vanilla Transformers and their variants in multiple ways.

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Zhu Xu, Zhiqiang Zhao, Zihan Zhang, Yuchi Liu, Quanwei Shen, Fei Liu, Yu Kuang, Jian He, Conglin Liu

Challenge:	Large language models (LLMs) use tokenization methods but often obscure internal character structures within tokens.
Approach:	They propose a method that improves models’ ability to capture character positions within tokens by training them on reverse character prediction tasks using the tokenizer’s vocabulary.
Outcome:	Experiments show that the proposed method improves position prediction accuracy in large language models, enabling more precise identification of target characters in original text.

Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation (2022.acl-long)

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Yingxiu Zhao, Zhiliang Tian, Huaxiu Yao, Yinhe Zheng, Dongkyu Lee, Yiping Song, Jian Sun, Nevin Zhang

AOT*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree Search (2026.findings-acl)

Challenge:	Building models of natural language processing (NLP) is challenging in low-resource scenarios where limited data are available.
Approach:	They propose a memory imitation meta-learning method that enhances the model’s reliance on support sets for task adaptation.
Outcome:	The proposed method outperforms baselines on both text classification and generation tasks.

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Xiaozhuang Song, Xuanhao Pan, Xinjian Zhao, Hangting Ye, Shufei Zhang, Jian Tang, Tianshu Yu

Green & Sustainable NLP

Challenge:	Experimental evaluation shows that AOT* achieves competitive solve rates using 3-5 fewer iterations than existing LLM-based approaches.
Approach:	They propose a framework that integrates LLM-generated chemical synthesis pathways with systematic AND-OR tree search.
Outcome:	Experimental results show that AOT* improves search efficiency and solves faster than existing approaches.

Semi-Supervised Lifelong Language Learning (2022.findings-emnlp)

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Yingxiu Zhao, Yinhe Zheng, Bowen Yu, Zhiliang Tian, Dongkyu Lee, Jian Sun, Yongbin Li, Nevin L. Zhang

Low-Resource NLP

Challenge:	Existing methods to learn languages only focus on supervised learning, and unlabeled data is underexplored.
Approach:	They propose a semi-supervised lifelong language learning setting where a model learns sequentially arriving language tasks with both labeled and unlabeled data.
Outcome:	The proposed model outperforms baseline models on various language tasks and is effective and superior to existing models.

RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension (2026.acl-long)

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Yelin Chen, Fanjin Zhang, Suping Sun, Yunhe Pang, Yuanchun Wang, Jian Song, XiaoYan Li, Lei Hou, Shu Zhao, Jie Tang, Juanzi Li

Challenge:	Existing benchmarks for understanding research papers offer limited fine-grained evaluation at scale.
Approach:	They propose a large-scale question-answering benchmark built from review–rebuttal exchanges of high-quality computer science papers.
Outcome:	The proposed model is based on human-verified QA pairs and contains 15K questions.

Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue (2022.emnlp-main)

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Yingxiu Zhao, Yinhe Zheng, Zhiliang Tian, Chang Gao, Jian Sun, Nevin L. Zhang

The Mark Fades: Adaptive Evolutionary Paraphrase-based Attack against LLM Watermarks (2026.findings-acl)

Challenge:	Existing generative replay methods use only a single task-specific token to control their models.
Approach:	They propose a method to capture task-specific distributions with a conditional variational autoencoder, conditioned on natural language prompts to guide the pseudo-sample generation.
Outcome:	The proposed method outperforms baselines on natural language understanding tasks of advanced task-oriented dialogue (ToD) systems.

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Yusheng Zhao, Jian Zhao, Tianle Zhang, Feng Wei, Xuelong Li

Robustness in NLP

Challenge:	Existing paraphrase-based watermark removal methods struggle to balance efficacy with text quality.
Approach:	They propose a training-free evolutionary framework that models watermark removal as a constrained multi-objective optimization problem by using a Pseudo-Log-Likelihood-guided mutation to precisely target and modify watermark-carrying tokens.
Outcome:	The proposed method outperforms baseline methods on a Qwen3 series watermark scheme while maintaining high semantic fidelity.

Leveraging Argumentation Knowledge Graph for Interactive Argument Pair Identification (2021.findings-acl)

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Jian Yuan, Zhongyu Wei, Donghua Zhao, Qi Zhang, Changjian Jiang

Learning How to Remember: A Meta-Cognitive Management Method for Structured and Transferable Agent Memory (2026.findings-acl)

Challenge:	Existing researches focus on sentence matching but the interaction of opinions requires reasoning of knowledge, which is beyond textual information.
Approach:	They propose to leverage external knowledge to enhance the identification of interactive argument pairs by analyzing the discussion thread of the target topic in an online forum.
Outcome:	The proposed model achieves state-of-the-art in the benchmark dataset.

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Sirui Liang, Pengfei Cao, Jian Zhao, Wenhao Teng, Xiangwen Liao, Jun Zhao, Kang Liu

Challenge:	Existing approaches store memory in fixed representations and reuse it at a single or implicit level of abstraction, which limits generalization and often leads to negative transfer when distribution shift.
Approach:	They propose a Meta-Cognitive Memory Abstraction method which decouples task execution from memory management by combining a frozen task model with a learned memory copilot.
Outcome:	Experiments on ALFWorld, ScienceWorld, and BabyAI show that the proposed method improves performance, out-of-distribution generalization, and cross-task transfer over several baselines.

MUR: Momentum Uncertainty guided Reasoning for Large Language Models (2026.acl-long)

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Hang Yan, Fangzhi Xu, Rongman Xu, Yifei Li, Jian Zhang, Haoran Luo, Xiaobao Wu, Anh Tuan Luu, Haiteng Zhao, Qika Lin, Jun Liu

Visual Attention Reasoning via Hierarchical Search and Self-Verification (2026.acl-long)

Challenge:	Existing methods for optimizing reasoning quality are limited by overthinking.
Approach:	They propose a method that allocates thinking budgets to critical reasoning steps by tracking and aggregating step-wise uncertainty over time.
Outcome:	The proposed method reduces computation by over 45% on average while improving accuracy by 0.33–3.46%.

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Wei Cai, Jian Zhao, Yuchen Yuan, Tianle Zhang, Ming Zhu, Haichuan Tang, Xuelong Li

Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets (P19-1)

Challenge:	Multimodal Large Language Models (MLLMs) often hallucinate due to fragile, linear reasoning and weak visual grounding.
Approach:	They propose a framework that reformulates reasoning as a hierarchical search with self-verification and replaces linear Chain-of-Thought with a tree-search policy capable of backtracking to correct logical errors.
Outcome:	The proposed framework outperforms state-of-the-art methods on hallucination and safety benchmarks.

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Guanhua Zhang, Bing Bai, Jian Liang, Kun Bai, Shiyu Chang, Mo Yu, Conghui Zhu, Tiejun Zhao

Responsible & Trustworthy NLP

Challenge:	Natural Language Sentence Matching (NLSM) is a popular NLP task.
Approach:	They propose to use QuoraQP to train and evaluate NLSM models using a selection bias framework.
Outcome:	The proposed framework can improve generalization ability of trained models and give more trustworthy evaluation results for real-world adoptions.

Table-R1: Region-based Reinforcement Learning for Table Understanding (2026.findings-acl)

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Zhenhe Wu, Jian Yang, Zhongjiang He, Changzai Pan, Jiaheng Liu, Xianjie Wu, Yu Zhao, Shuangyong Song, Yongxiang Li, Zhoujun Li, Xuelong Li

Generating Deep Questions with Commonsense Reasoning Ability from the Text by Disentangled Adversarial Inference (2023.findings-acl)

Challenge:	Tables are a widely used data format that poses unique challenges for language models due to their structured row-column interactions.
Approach:	They propose a region-based reinforcement learning approach that integrates region evidence into reasoning steps.
Outcome:	The proposed method outperforms baseline models on three benchmark datasets and significantly reduces the reasoning token consumption by 67.5%.

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Jianxing Yu, Shiqi Wang, Libin Zheng, Qinliang Su, Wei Liu, Baoquan Zhao, Jian Yin

ELIOT: Zero-Shot Video-Text Retrieval through Relevance-Boosted Captioning and Structural Information Extraction (2025.naacl-srw)

Challenge:	Existing methods for commonsense question generation produce shallow questions that can be answered by simple word matching.
Approach:	They propose a task of commonsense question generation that aims to yield deep-level questions from the text.
Outcome:	The proposed model can yield deep-level and to-the-point questions from the text.

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Xuye Liu, Yimu Wang, Jian Zhao

Learning with Partial Annotations for Event Detection (2023.acl-long)

Challenge:	Recent advances in video-text retrieval (VTR) have relied on supervised learning and fine-tuning.
Approach:	They propose a zero-shot video-text retrieval framework that leverages off-the-shelf captioners, large language models, and text retrieval methods without additional training or annotated data.
Outcome:	The proposed framework outperforms existing methods on video-text retrieval benchmarks without data.

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Jian Liu, Dianbo Sui, Kang Liu, Haoyan Liu, Zhe Zhao

LoRE: Enhancing Search Relevance with Progressive Chain-of-Thought and Preference Alignment (2026.findings-acl)

Challenge:	Event detection (ED) requires fully labeled and high-quality training data.
Approach:	They propose a new trigger localization formulation using contrastive learning to distinguish ground-truth triggers from contexts and show a decent robustness for addressing partial annotation noise.
Outcome:	The proposed approach achieves an F1 score of over 60% in an extreme scenario where 90% of events are unlabeled.

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Chenji Lu, Zhuo Chen, Hui Zhao, Zhiyuan Zeng, Gang Zhao, Junjie Ren, null Lihaoran, Songyan Liu, Pengjie Wang, Chuan Yu, Jian Xu, Bo Zheng

ReaGeo: Reasoning-Enhanced End-to-End Geocoding with LLMs (2026.findings-acl)

Challenge:	E-commerce search relevance is a critical component of retrieval systems.
Approach:	They propose a large-generative model for search relevance that trains reasoning knowledge, multi-modal understanding and rule awareness into three core competencies.
Outcome:	The proposed model outperforms GPT-5 in Macro-F1 and achieves 27% online gain.

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Jian Cui, Zhiyuan Ren, Desheng Weng, Yongqi Zhao, Gong Wenbin, Yu Lei, Zhenning Dong

Novel Slot Detection With an Incremental Setting (2023.findings-emnlp)

Challenge:	Existing methods rely on text retrieval and geographic knowledge bases to generate coordinates, and they are prone to error propagation and dependency on structured knowledge bases.
Approach:	They propose to use large language models to convert geographic coordinates into geohash sequences and introduce a Chain-of-Thought mechanism to enhance the model’s reasoning over spatial relationships.
Outcome:	The proposed framework can handle explicit address queries in single-point predictions and effectively resolve vague relative location queries.

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Chen Liang, Hongliang Li, Changhao Guan, Qingbin Liu, Jian Liu, Jinan Xu, Zhe Zhao

Dialogue Systems & Conversational Agents

Challenge:	Current dialogue systems face diverse user requests and rapid change domains, making quickly adapt to scenarios with previous unseen slot types becomes a major challenge.
Approach:	They propose an incremental novel slot detection task which separates the dialogue system to deal with novel types as two major phrases: 1) model discovers unknown slots; 2) training model to possess the capability to handle new classes.
Outcome:	The proposed approach overcomes catastrophic forgetting during the process of INSD and is highly effective.

Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment (2026.acl-long)

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Yuming Yang, Mingyoung Lai, Wanxu Zhao, Xiaoran Fan, Zhiheng Xi, Mingqi Wu, Chiyue Huang, Jun Zhao, Haijun Lv, Jian Tong, Yunhua Zhou, Yicheng Zou, Qipeng Guo, Tao Gui, Qi Zhang, Xuanjing Huang

A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation (2020.tacl-1)

Challenge:	Existing methods assess suitability primarily through student likelihood, favoring trajectories that align closely with the student model’s current behavior but overlooking more informative ones.
Approach:	They propose a Rank–Surprisal Ratio metric that captures both alignment and informativeness to assess the suitability of a reasoning trajectory.
Outcome:	The proposed metric captures both alignment and informativeness to assess the suitability of a reasoning trajectory.

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Jian Guan, Fei Huang, Zhihao Zhao, Xiaoyan Zhu, Minlie Huang

Addressing NER Annotation Noises with Uncertainty-Guided Tree-Structured CRFs (2023.emnlp-main)

Challenge:	Existing models for story generation suffer from repetition, logic conflicts, and lack of long-range coherence .
Approach:	They propose to utilize commonsense knowledge from external knowledge bases to generate reasonable stories by multi-task learning.
Outcome:	The proposed model can generate more reasonable stories than state-of-the-art models, compared with existing models, showing that it can capture useful semantic and syntactic features.

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Jian Liu, Weichang Liu, Yufeng Chen, Jinan Xu, Zhe Zhao

Beyond Modality Collapse: Taming Guided Modality Entropy for Omni-modal Emotion Reasoning (2026.findings-acl)

Challenge:	Named entity recognition datasets are notorious for their noisy nature due to annotation errors, inconsistencies, and subjective interpretations.
Approach:	They propose a method that considers NER as a constituency tree parsing problem and uses a tree-structured Conditional Random Fields with uncertainty evaluation for integration.
Outcome:	The proposed model exhibits superb performance even in extreme scenarios with 90% annotation noise.

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Xian Zhao, Rui Hu, Yuxiang Zhang, Delai Qiu, Yining Wang, Shengping Liu, Jian Yu, Jitao Sang

ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models (2024.emnlp-main)

Challenge:	EmoOmni is a data paradigm for omni-modal large language models that can be used for emotion reasoning.
Approach:	They propose a data paradigm that interleaves guided tokens into reasoning traces to enforce structured evidence extraction.
Outcome:	The proposed paradigm over-relys on a dominant modality while neglecting complementary cues.

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Haiquan Zhao, Lingyu Li, Shisong Chen, Shuqi Kong, Jiaan Wang, Kexin Huang, Tianle Gu, Yixu Wang, Jian Wang, Liang Dandan, Zhixu Li, Yan Teng, Yanghua Xiao, Yingchun Wang

Dialogue Systems & Conversational Agents

Challenge:	Emotion Support Conversation (ESC) is a crucial application for reducing stress and providing emotional guidance.
Approach:	They re-organize 2,801 role-playing cards to define roles of role-players . they train a specific role- playing model called ESC-Role which behaves more like a confused person than GPT-4 .
Outcome:	The proposed model behaves more like a confused person than GPT-4, and the model performs better than GPLs.

PromptCoT: Synthesizing Olympiad-level Problems for Mathematical Reasoning in Large Language Models (2025.findings-acl)

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Xueliang Zhao, Wei Wu, Jian Guan, Lingpeng Kong

Beyond Static Rules: Automated Discovery of Latent Vulnerabilities in Text-to-SQL (2026.findings-acl)

Challenge:	PromptCoT is a novel approach for synthesizing Olympiad-level math problems . it integrates rationale generation and mathematical concepts to generate complex problems based on concepts and rationale behind problem construction.
Approach:	They propose a method for automatically generating high-quality Olympiad-level math problems . they use mathematical concepts and the rationale behind problem construction to synthesize complex problems based on mathematical concepts .
Outcome:	The proposed method outperforms existing problem generation methods on benchmarks including GSM8K, MATH-500, and AIME2024.

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Hanqing Wang, Yongdong Chi, Jian Yang, Lei Yang, Jiehui Zhao, Yun Chen, Guanhua Chen

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) have been successful in Text-to-SQL tasks, but their deployment in real-world environments is hindered by latent reliability issues.
Approach:	They propose a framework to autonomously uncover latent failure patterns in LLM-based Text-to-SQL generation.
Outcome:	The proposed framework uncovers a substantial number of failure cases on state-of-the-art open-source LLMs.

Large Language Models Badly Generalize across Option Length, Problem Types, and Irrelevant Noun Replacements (2025.emnlp-main)

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Guangxiang Zhao, Saier Hu, Xiaoqi Jian, Wu Jinzhu, Yuhan Wu, Lin Sun, Xiangzheng Zhang

Ambiguity Awareness Optimization: Towards Semantic Disambiguation for Direct Preference Optimization (2025.emnlp-main)

Challenge:	Existing benchmarks have exposed patterns and may not truly assess generalization ability of Large Language Models (LLMs).
Approach:	They propose a “Generalization Stress Test” to assess Large Language Models’ generalization ability under slight and controlled perturbations, including option length, problem types, and irrelevant noun replacements.
Outcome:	The proposed test shows that LLMs exhibit severe accuracy drops and unexpected biases when faced with minor but content-preserving modifications.

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Jian Li, Shenglin Yin, Yujia Zhang, Alan Zhao, Xi Chen, Xiaohui Zhou, Pengfei Xu

T2R-BENCH: A Benchmark for Real World Table-to-Report Task (2025.emnlp-main)

Challenge:	Direct Preference Optimization (DPO) is a widely used reinforcement learning from human feedback (RLHF) method across various domains.
Approach:	They propose an approach that automatically re-weights ambiguous content to reduce ambiguities by calculating semantic similarity from preference pairs.
Outcome:	The proposed approach outperforms state-of-the-art approaches in performance across multiple model scales and widely adopted benchmark datasets.

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Jie Zhang, Changzai Pan, Sishi Xiong, Kaiwen Wei, Yu Zhao, Xiangyu Li, Jiaxin Peng, Xiaoyan Gu, Jian Yang, Wenhan Chang, Zhenhe Wu, Jiang Zhong, Shuangyong Song, Xuelong Li

Structured Data in NLP

Challenge:	Existing table benchmarks lack the capacity to adequately assess the practical application of table reasoning in industrial applications.
Approach:	They propose a bilingual table-to-report task and a table-based benchmark to assess the quality of table reasoning.
Outcome:	The proposed task is based on a bilingual benchmark with 457 industrial tables and evaluation criteria to measure the quality of report generation.

UCS-SQL: Uniting Content and Structure for Enhanced Semantic Bridging In Text-to-SQL (2025.findings-acl)

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Zhenhe Wu, Zhongqiu Li, JieZhangChinaTele JieZhangChinaTele, Zhongjiang He, Jian Yang, Yu Zhao, Ruiyu Fang, Bing Wang, Hongyan Xie, Shuangyong Song, Zhoujun Li

MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools (2026.acl-long)

Challenge:	Existing methods overlook the challenge of effectively transforming structure information from NL to SQL.
Approach:	They propose a text-to-SQL framework that unites content and structure pipes to bridge the gap between NL and SQL.
Outcome:	The proposed framework bridges the gap between natural language questions and SQL by combining content and structure pipes.

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WenHao Wang, Peizhi Niu, Zhao Xu, Zhaoyu Chen, Jian Du, Yaxin Du, Xianghe Pang, Keduan Huang, Yanfeng Wang, Qiang Yan, Siheng Chen

Graph-Based Knowledge Integration for Question Answering over Dialogue (2020.coling-main)

Challenge:	Existing research on Large Language Models (LLMs) relies on few servers and lacks training support.
Approach:	They propose a web-agent-driven pipeline for large-scale server discovery, data synthesis, and model training that collects and filters data from 1166 servers and 11536 tools.
Outcome:	Empirical evidence shows that MCP-Flow generates higher quality instruction-function call pairs and higher agentic task performance than previous work.

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Jian Liu, Dianbo Sui, Kang Liu, Jun Zhao

Natural Language Interfaces

Challenge:	Existing approaches for question answering over dialogue did not consider dialogue structure and background knowledge (e.g., relationships between speakers).
Approach:	They propose a method which organizes a dialogue as a "relational graph" and uses edges to represent relationships between entities to encode multi-relations knowledge for reasoning.
Outcome:	The proposed method is better at tackling complex questions requiring relational reasoning and defending adversarial attacks with distracting sentences.

Dual Activation-Weight Sparsity: A Training-Free Framework for Efficient Large Language Model Compression (2026.acl-long)

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Luoyang Sun, Guangyan Li, Cheng Deng, Haifeng Zhang, Jian Zhao, Yongqiang Tang, Wensheng Zhang, Jun Wang

M2RC-EVAL: Massively Multilingual Repository-level Code Completion Evaluation (2025.acl-long)

Challenge:	Large language models (LLMs) excel at natural language tasks but face deployment bottlenecks due to computational demands.
Approach:	They propose a training-free framework that exploits activation and weight sparsity . they use a three-tier routing strategy that uses magnitude-based pruning .
Outcome:	Experiments on Llama and Mistral models show that DAWS outperforms activation-weight sparsity pruning methods.

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Jiaheng Liu, Ken Deng, Congnan Liu, Jian Yang, Shukai Liu, He Zhu, Peng Zhao, Linzheng Chai, Yanan Wu, JinKe JinKe, Ge Zhang, Zekun Moore Wang, Guoan Zhang, Yingshui Tan, Bangyu Xiang, Zhaoxiang Zhang, Wenbo Su, Bo Zheng

Programming Languages in NLP

Challenge:	Existing repository-level code completion benchmarks focus on a limited number of languages . existing benchmarks report overall average scores of different languages ignoring fine-grained abilities .
Approach:	They propose to use repository-level code completion benchmarks to evaluate general code intelligence abilities across languages for existing code Large Language Models.
Outcome:	The proposed benchmarks improve the code completion abilities of existing LLMs by using two types of annotations on the parsed syntax tree.

My Words Imply Your Opinion: Reader Agent-Based Propagation Enhancement for Personalized Implicit Emotion Analysis (2025.acl-long)

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Jian Liao, Yu Feng, Yujin Zheng, Jun Zhao, Suge Wang, JianXing Zheng

Sentiment Analysis

Challenge:	Current studies personalize emotion analysis by focusing on the author but neglect the impact of the intended reader on implicit emotional feedback.
Approach:	They propose a model which incorporates reader feedback into implicit emotion analysis (IEA) they use large language models to create reader agents to simulate reader feedback .
Outcome:	The proposed model outperforms state-of-the-art models in a text-centric environment.

AIMMerging: Adaptive Iterative Model Merging Using Training Trajectories for Language Model Continual Learning (2025.emnlp-main)

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Yujie Feng, Jian Li, Xiaoyu Dong, Pengfei Xu, Xiaohui Zhou, Yujia Zhang, Zexin Lu, Yasha Wang, Alan Zhao, Xu Chu, Xiao-Ming Wu

Analyzing Uncertainty of LLM-as-a-Judge: Interval Evaluations with Conformal Prediction (2025.emnlp-main)

Challenge:	Recent model merging-based methods struggle to effectively manage the trade-off between learning new knowledge and preventing catastrophic forgetting.
Approach:	They propose a model merging framework that utilizes learning and forgetting signals from the training trajectory to dynamically monitor the model’s training status.
Outcome:	The proposed framework achieves significant performance improvements over existing state-of-the-art methods on three CL benchmarks with various model sizes (from 770M to 13B).

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Huanxin Sheng, Xinyi Liu, Hangfeng He, Jieyu Zhao, Jian Kang

Challenge:	Large language models (LLMs) are powerful automatic evaluators for natural language generation (NLG) tasks, but their uncertainty may limit their deployment in many applications.
Approach:	They propose a conformal prediction framework that provides a prediction interval with coverage guarantees and a midpoint-based score as a low-bias alternative to raw model score and weighted average.
Outcome:	The proposed framework provides a prediction interval with coverage guarantees and a midpoint-based score as a low-bias alternative to raw model score and weighted average.

Improving Speech Translation by Fusing Speech and Text (2023.findings-emnlp)

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Wenbiao Yin, Zhicheng Liu, Chengqi Zhao, Tao Wang, Jian Tong, Rong Ye

CAT: Causal Attention Tuning For Injecting Fine-grained Causal Knowledge into Large Language Models (2025.emnlp-main)

Challenge:	In speech translation, multimodal data to address limitations of individual modalities has shown significant effectiveness.
Approach:	They propose a cross-modal model which supports three input modalities for speech, text and fused speech-text.
Outcome:	The proposed model achieves an average of 34.0 BLEU on MuST-C, GigaST and newstest benchmark.

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Kairong Han, Wenshuo Zhao, Ziyu Zhao, Ye Jun Jian, Lujia Pan, Kun Kuang