Papers by Kang He

SwiftPrune: Hessian-Free Weight Pruning for Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods focus on normal class and fail to extract relational triplets precisely.
Approach:	They propose an end-to-end model which can jointly extract relational triplets from sentences . they employ two different strategies in decoding process: employing only one united decoder or applying multiple separated decodeurs.
Outcome:	The proposed model outperforms the baseline method significantly in two datasets.

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Yuhan Kang, Yang Shi, Mei Wen, Jun He, Jianchao Yang, Zeyu Xue, Jing Feng, Xinwang Liu

LEGO: A Multi-agent Collaborative Framework with Role-playing and Iterative Feedback for Causality Explanation Generation (2023.findings-emnlp)

Challenge:	a novel post-training pruning method relies on the Hessian matrix to perform pruning . current pruning methods are computationally intensive and lack performance due to second-order derivative calculations.
Approach:	They propose a Hessian-free weight pruning method that reduces computational burden . they use an Exponentially Weighted Moving Average technique to bypass weight sorting .
Outcome:	The proposed method achieves hardware-efficient model compression by eliminating computational intensive calculations.

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Zhitao He, Pengfei Cao, Yubo Chen, Kang Liu, Ruopeng Li, Mengshu Sun, Jun Zhao

Explainability & Interpretability in NLP

Challenge:	Causality explanation generation is a generative task that aims to explain why a given cause-effect pair is true using natural language.
Approach:	They propose a multi-agent framework with role-playing and iterative feedback for causality explanation generation.
Outcome:	The proposed framework is superior to existing frameworks on WIKIWHY and e-CARE datasets.

Generate-on-Graph: Treat LLM as both Agent and KG for Incomplete Knowledge Graph Question Answering (2024.emnlp-main)

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Yao Xu, Shizhu He, Jiabei Chen, Zihao Wang, Yangqiu Song, Hanghang Tong, Guang Liu, Jun Zhao, Kang Liu

Structured Data in NLP

Challenge:	Existing methods to integrate LLMs with Knowledge Graphs (KGs) however, these methods are often incomplete to cover all the knowledge required to answer questions.
Approach:	They propose to integrate LLMs with Knowledge Graphs (KGs) to address insufficient knowledge and hallucination issues in Large Language Models.
Outcome:	The proposed method outperforms existing methods on two datasets.

Query2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs (2023.findings-emnlp)

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Yao Xu, Shizhu He, Cunguang Wang, Li Cai, Kang Liu, Jun Zhao

UTBoost: Rigorous Evaluation of Coding Agents on SWE-Bench (2025.acl-long)

Challenge:	Complex Query Answering (CQA) is a challenge task of Knowledge Graphs due to incompleteness of KGs.
Approach:	They propose a query embedding approach that decouples the training for simple and complex queries.
Outcome:	The proposed approach decouples training for simple and complex queries and achieves state-of-the-art performance over three public benchmarks.

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Boxi Yu, Yuxuan Zhu, Pinjia He, Daniel Kang

Programming Languages in NLP

Challenge:	Large language models have enabled the development of coding agents for real-world code generation.
Approach:	They propose a novel LLM-driven test case generator that analyzes codebases and dependencies to generate test cases for real-world Python projects.
Outcome:	The proposed framework improves the performance of SWE-Bench by analyzing codebases and dependencies.

UFO: A UI-Focused Agent for Windows OS Interaction (2025.naacl-long)

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Chaoyun Zhang, Liqun Li, Shilin He, Xu Zhang, Bo Qiao, Si Qin, Minghua Ma, Yu Kang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

Natural Language Interfaces

Challenge:	UFO is a UI-Fcused agent designed to fulfill user requests tailored to Windows OS applications . it decomposes user requests using divide-and-conquer approach, enabling seamless navigation and addressing sub-tasks across multiple applications.
Approach:	They propose a UI-Fcused Windows OS agent that decomposes user requests using a divide-and-conquer approach and incorporates a control interaction module tailored for Windows OS.
Outcome:	The proposed agent decomposes user requests using divide-and-conquer approach, enabling seamless navigation and addressing sub-tasks across multiple applications.

Zero-Shot Conversational Stance Detection: Dataset and Approaches (2025.findings-acl)

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Yuzhe Ding, Kang He, Bobo Li, Li Zheng, Haijun He, Fei Li, Chong Teng, Donghong Ji

DI-BENCH: Benchmarking Large Language Models on Dependency Inference with Testable Repositories at Scale (2025.findings-acl)

Challenge:	Existing stance detection datasets are limited to a limited set of specific targets . current models are limited in their ability to detect large numbers of unseen targets based on a large number of unidentified targets.
Approach:	They propose a speaker interaction and target-aware prototypical contrastive learning model that can detect public opinion towards specific targets using social media data.
Outcome:	The proposed model achieves state-of-the-art in zero-shot conversational stance detection with only an F1-macro score of 43.81%.

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Linghao Zhang, Junhao Wang, Shilin He, Chaoyun Zhang, Yu Kang, Bowen Li, Jiaheng Wen, Chengxing Xie, Maoquan Wang, Yufan Huang, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

DALR: Dual-level Alignment Learning for Multimodal Sentence Representation Learning (2025.findings-acl)

Challenge:	Existing studies highlight that dependency-related issues cause over 40% of observed runtime errors on the generated repository.
Approach:	They propose a large-scale benchmark and evaluation framework specifically designed to assess LLMs’ capability on dependency inference.
Outcome:	The proposed model achieves only a 48% execution pass rate on Python, indicating room for improvement.

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Kang He, Yuzhe Ding, Haining Wang, Fei Li, Chong Teng, Donghong Ji

Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation (D19-1)

Challenge:	Existing multimodal sentence representation learning methods focus on aligning images and text at a coarse level, resulting in cross-modal misalignment bias and intra-modal semantic divergence.
Approach:	They propose a dual-level alignment learning framework for multimodal sentence representation learning that promotes cross-modal and intra-modal alignment.
Outcome:	The proposed framework outperforms state-of-the-art methods on semantic textual similarity and transfer tasks on semantic similarity, ranking distillation and global intra-modal alignment learning.

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Zhuoren Jiang, Zhe Gao, Guoxiu He, Yangyang Kang, Changlong Sun, Qiong Zhang, Luo Si, Xiaozhong Liu

Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models (2024.findings-emnlp)

Challenge:	Currently, Chinese characters share glyph and phonetic variations to escape detection algorithms due to their complexity and complexity.
Approach:	They propose a Chinese variation-enhanced Graph Embedding algorithm that can learn Chinese character embeddings and latent variation families.
Outcome:	The proposed model outperforms state-of-the-art models on Chinese spam detection datasets and review datasets.

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Kang He, Yinghan Long, Kaushik Roy

Harmonizing the Past, Present, and Future: A Null-Space Constrained Region-Specific Method for Continual Learning in LLMs (2026.acl-long)

Challenge:	Prompt-based learning is susceptible to intrinsic bias present in pre-trained language models (LMs), leading to sub-optimal performance in prompt-based zero/few-shot settings.
Approach:	They propose a null-input prompting method to calibrate intrinsic bias encoded in pre-trained language models (LMs) they leverage a diverse set of auto-selected null meaning inputs generated from GPT-4 to probe intrinsic bias.
Outcome:	The proposed method significantly improves zero/few-shot learning performance of LMs for both in-context learning and prompt-based fine-tuning (on average 9% and 2%, respectively).

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Jinhui Chen, Shizhu He, Xingchang Yang, Huanxuan Liao, Yequan Wang, Xiangwen Liao, Wenhao Teng, Kang Liu, Jun Zhao

ExpNote: Black-box Large Language Models are better Task Solvers with Experience Notebook (2023.findings-emnlp)

Challenge:	Existing continual learning paradigms prioritize instant performance through dense updates, leading to catastrophic forgetting and rapid exhaustion of model capacity.
Approach:	They propose a method that preserves previously acquired knowledge and acquires new task-specific skills while preserving sufficient parameter capacity for subsequent adaptation.
Outcome:	The proposed method is based on the brain's functional partitioning and can be used to map tasks between specialized and generalist neurons.

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Wangtao Sun, Xuanqing Yu, Shizhu He, Jun Zhao, Kang Liu

S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Model (2024.naacl-long)

Challenge:	Large Language Models (LLMs) have shown great power in solving various tasks but fail in many specific tasks.
Approach:	They propose a framework to help black-box LLMs better adapt to unfamiliar tasks by reflecting and noting experiences from training data and retrieving them from external memory during testing.
Outcome:	The proposed framework improves the performance of black-box Large Language Models on multiple tasks and demonstrates that it is a good choice for the future.

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Fangyu Lei, Qian Liu, Yiming Huang, Shizhu He, Jun Zhao, Kang Liu

LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces (2026.findings-acl)

Challenge:	Existing benchmarks fail to evaluate extremely long-context LLMs or analyze their limitations.
Approach:	They propose a Synthetic, Scalable, Systematic evaluation suite for LLMs using SQL execution.
Outcome:	The proposed evaluation suite is able to scale text length and difficulty across scenarios and provides strong correlations with real-world benchmarks.

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Yukang Feng, Jianwen Sun, Zelai Yang, Jiaxin Ai, Chuanhao Li, Zizhen Li, Fanrui Zhang, Kang He, Rui Ma, Jifan Lin, Jie Sun, Yang Xiao, Sizhuo Zhou, Wenxiao Wu, Yiming Liu, Pengfei Liu, Shenglin Zhang, Kaipeng Zhang

LogicTree: Structured Proof Exploration for Coherent and Rigorous Logical Reasoning with Large Language Models (2025.emnlp-main)

Challenge:	Existing benchmarks for agentic programming in long-horizon command-line interface tasks are limited by short task horizons, data contamination from GitHub scraping, and a lack of fine-grained evaluation metrics.
Approach:	They propose a benchmark to evaluate agentic capabilities across long-horizon command-line interface tasks.
Outcome:	The proposed benchmarks cover four engineering categories: from scratch, feature addition, bug fixing, and refactoring.

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Kang He, Kaushik Roy

Find Parent then Label Children: A Two-stage Taxonomy Completion Method with Pre-trained Language Model (2023.eacl-main)

Challenge:	Large language models (LLMs) have remarkable multi-step reasoning capabilities, but they still face challenges in complex logical reasoning.
Approach:	They propose an algorithm-guided search framework that automates structured proof exploration and ensures logical coherence.
Outcome:	The proposed framework outperforms o3-mini and chain-of-thought with average gains of 23.6% and 12.5% on five datasets.

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Fei Xia, Yixuan Weng, Shizhu He, Kang Liu, Jun Zhao

LLaSA: Large Language and Structured Data Assistant (2025.naacl-long)

Challenge:	Existing taxonomies focus on adding concepts to the leaf nodes of the existing tree, which does not fully utilize the taxonomy’s knowledge and is unable to update the original taxomy structure.
Approach:	They propose a two-stage method called ATTEMPT for taxonomy completion that inserts new concepts into the correct position by finding a parent node and labeling child nodes.
Outcome:	The proposed method performs best on taxonomy completion and extension tasks, surpassing existing methods.

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Yao Xu, Shizhu He, Jiabei Chen, ZengXiangrong ZengXiangrong, Bingning Wang, Guang Liu, Jun Zhao, Kang Liu

QueueEDIT: Structural Self-Correction for Sequential Model Editing in LLMs (2026.findings-acl)

Challenge:	Structured knowledge grounding (SKG) tasks are a key part of many NLP applications.
Approach:	They propose a framework for enhancing LLMs' ability to handle structured data . they represent various types of structured data in a unified hypergraph format .
Outcome:	The proposed framework outperforms existing methods on SKG tasks using LoRA finetuning.

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Taolin Zhang, Haidong Kang, Dongyang Li, Qizhou Chen, Xiaofeng He, Chengyu Wang, Richang Hong

Neural Incompatibility: The Unbridgeable Gap of Cross-Scale Parametric Knowledge Transfer in Large Language Models (2025.acl-long)

Challenge:	Recent studies have shown that large language models (LLMs) can be effective for correcting factual inaccuracies but can still suffer from hallucinations.
Approach:	They propose a queue-based self-correction framework that addresses parameter bias during sequential model editing.
Outcome:	The proposed framework outperforms baseline models while maintaining competitive performance in single-turn editing.

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Yuqiao Tan, Shizhu He, Kang Liu, Jun Zhao

Pattern-revising Enhanced Simple Question Answering over Knowledge Bases (C18-1)

Challenge:	Large Language Models (LLMs) have a transparent brain with accessible parameters that encode extensive knowledge, which can be analyzed, located and transferred.
Approach:	They propose a new paradigm that aligns parametric spaces of LLMs using several training steps without following training.
Outcome:	The proposed model aligns parametric spaces across scales using only training steps without following training.

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Yanchao Hao, Hao Liu, Shizhu He, Kang Liu, Jun Zhao

Knowledge Representation

Challenge:	Simple question answering over knowledge bases is one of the most important natural language processing tasks.
Approach:	They propose to conduct pattern extraction and entity linking first and put forward pattern revising procedure to mitigate the error propagation problem.
Outcome:	The proposed method outperforms the current state-of-the-art in this task by an absolute large margin.

Awakening Augmented Generation: Learning to Awaken Internal Knowledge of Large Language Models for Question Answering (2025.coling-main)

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Huanxuan Liao, Shizhu He, Yao Xu, Yuanzhe Zhang, Shengping Liu, Kang Liu, Jun Zhao

Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing (2022.acl-short)

Challenge:	Recent studies indicate that Large Language Models model rich knowledge, but it is often not activated and awakened.
Approach:	They propose a framework that leverages richer context to enhance question answering . Explicit awakening fine-tunes a context generator to create a synthetic, compressed document that functions as symbolic context.
Outcome:	The proposed framework mimics the human ability to answer questions using only thinking and recalling to compensate for knowledge gaps.

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Runxin Sun, Shizhu He, Chong Zhu, Yaohan He, Jinlong Li, Jun Zhao, Kang Liu

Programming Languages in NLP

Challenge:	Text-to-SQL parsing aims to parse natural language questions into SQL queries . current attention-based approaches can only model alignments at the token level .
Approach:	They propose a method to leverage explicit lexico-logical alignments by identifying possible phrase-level alignments and injecting them as additional contexts into the parsing procedure.
Outcome:	The proposed approach improves performance by 3.4% on Squall.

CA-GAR: Context-Aware Alignment of LLM Generation for Document Retrieval (2025.findings-acl)

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Heng Yu, Junfeng Kang, Rui Li, Qi Liu, Liyang He, Zhenya Huang, Shuanghong Shen, Junyu Lu

Information Retrieval

Challenge:	Recent techniques such as Generation-Augmented Retrieval (GAR) and Generative Document Retrieleval (GDR) leverage LLMs to enhance retrieval performance but face key challenges: GAR’s generated content may not always align with the target document corpus, while GDR limits the generative capacity of LLM.
Approach:	They propose a Context-Aware Generation-Augmented Retrieval approach which integrates corpus information into their generation process.
Outcome:	Experimental results show that CA-GAR outperforms existing methods on seven tasks and four non-English languages.

MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs (2022.emnlp-demos)

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Fei Xia, Bin Li, Yixuan Weng, Shizhu He, Kang Liu, Bin Sun, Shutao Li, Jun Zhao

Natural Language Interfaces

Challenge:	Existing medical dialogue systems have the problems of weak scalability, insufficient knowledge, and poor controllability.
Approach:	They propose a medical conversational question-answering system based on the knowledge graph to improve scalability and controllability.
Outcome:	The proposed system can conduct knowledge-grounded dialogues with users, using a Chinese medical knowledge graph and a large-scale dataset.

WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models (2023.emnlp-industry)

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Jun-Yan He, Zhi-Qi Cheng, Chenyang Li, Jingdong Sun, Wangmeng Xiang, Xianhui Lin, Xiaoyang Kang, Zengke Jin, Yusen Hu, Bin Luo, Yifeng Geng, Xuansong Xie

Decoupling Mixture-of-Graphs: Unseen Relational Learning for Knowledge Graph Completion by Fusing Ontology and Textual Experts (2022.coling-1)

Challenge:	Existing typography solutions lack adaptability, creativity, and computational efficiency.
Approach:	They propose a user-driven framework for artistic typography synthesis based on the Large Language Model (LLM) the LLM Engine interprets user inputs and generates actionable prompts for the other modules, transforming abstract concepts into tangible designs.
Outcome:	The proposed framework incorporates four key modules: the LLM Engine, SemTypo, StyTyPo, and TexTyPO.

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Ran Song, Shizhu He, Suncong Zheng, Shengxiang Gao, Kang Liu, Zhengtao Yu, Jun Zhao

Vocabulary Pyramid Network: Multi-Pass Encoding and Decoding with Multi-Level Vocabularies for Response Generation (P19-1)

Challenge:	Existing methods for knowledge Graph Completion (KGC) fail in unseen relation representations.
Approach:	They propose to use three kinds of graphs to obtain unseen relation representations . they propose to decouple mixture-of-graph experts (DMoG) for unseened relations learning .
Outcome:	The proposed method outperforms the state-of-the-art methods on unseen relation representations.

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Cao Liu, Shizhu He, Kang Liu, Jun Zhao

Text Generation

Challenge:	Conventional methods employ a fixed vocabulary and one-pass decoding, which make them prone to safe and general responses and lack further refinement to the first generated raw sequence.
Approach:	They propose a Vocabulary Pyramid Network which integrates multi-pass encoding and decoding with multi-level vocabularies into response generation.
Outcome:	The proposed system outperforms strong baselines on English Twitter and Chinese Weibo datasets.

Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks (2021.emnlp-main)

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Qingbin Liu, Pengfei Cao, Cao Liu, Jiansong Chen, Xunliang Cai, Fan Yang, Shizhu He, Kang Liu, Jun Zhao

Dialogue Systems & Conversational Agents

Challenge:	Existing offline DST models require a fixed dataset to train . Existing domain-lifelong learning methods are impractical in real-world applications .
Approach:	They propose a domain-lifelong learning method to continuously train a DST model on new data to learn incessantly emerging new domains while avoiding catastrophically forgetting old learned domains.
Outcome:	The proposed method outperforms state-of-the-art lifelong learning methods by 4.25% and 8.27% on the MultiWOZ and the SGD benchmarks.

Does Large Language Model Contain Task-Specific Neurons? (2024.emnlp-main)

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Ran Song, Shizhu He, Shuting Jiang, Yantuan Xian, Shengxiang Gao, Kang Liu, Zhengtao Yu

Teaching Small Language Models to Reason for Knowledge-Intensive Multi-Hop Question Answering (2024.findings-acl)

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities in comprehensively handling various types of natural language processing (NLP) tasks.
Approach:	They propose a method for task-specific neuron localization based on Causal Gradient Variation with Special Tokens (CGVST) this method identifies task- specific neurons by concentrating on the most significant tokens during task processing, eliminating redundant tokens and minimizing interference from non-essential neurons.
Outcome:	The proposed method can locate task-specific neurons across eight public tasks.

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Xiang Li, Shizhu He, Fangyu Lei, JunYang JunYang, Tianhuang Su, Kang Liu, Jun Zhao

Shuttle Between Symbolic Instructions and Neural Parameters of Large Language Models (2026.acl-long)

Challenge:	Large language models can teach small language models to solve complex reasoning tasks by Chain-of-thought Distillation (CoTD) e.g., mathematical question answering.
Approach:	They propose a method that distills two student models to solve a multi-hop question . they use chain-of-thought distillation to generate step-by-step reasoning paths .
Outcome:	The proposed method surpasses existing methods on knowledge-intensive multi-hop questions.

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Wangtao Sun, Haotian Xu, Huanxuan Liao, Xuanqing Yu, Zhongtao Jiang, Shizhu He, Jun Zhao, Kang Liu

Why and How LLMs Benefit from Knowledge Introspection in Commonsense Reasoning (2025.emnlp-main)

Challenge:	Despite their distinct external representations, a deeper analysis reveals their intrinsic nature: instructions serve as a natural language compression devised by humans for data governing specific mapping patterns, whereas parameters act as 'neuro compression' of the same task data.
Approach:	They propose a neural network framework to model and learn the bi-directional mappings between instructions and parameters of large language models by evaluating it on the tasks of instruction deduction and induction.
Outcome:	The proposed framework can map one of the instructions/parameters to the other by evaluating it on the tasks of instruction deduction and induction.

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Chengfeng Zhao, Shizhu He, Shanshan Jiang, Bin Dong, Jun Zhao, Kang Liu

Reasoning

Challenge:	Large Language Models (LLMs) can improve commonsense reasoning by generating intermediate knowledge, but the effectiveness of this knowledge introspection is not always guaranteed.
Approach:	They propose a training-free strategy that optimizes introspection via two stages: Knowledge Detection and Knowledge Regeneration.
Outcome:	The proposed approach mitigates the limitations of standard introspection and has consistent performance gains across all settings.

Class Lifelong Learning for Intent Detection via Structure Consolidation Networks (2023.findings-acl)

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Qingbin Liu, Yanchao Hao, Xiaolong Liu, Bo Li, Dianbo Sui, Shizhu He, Kang Liu, Jun Zhao, Xi Chen, Ningyu Zhang, Jiaoyan Chen

Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database (2024.lrec-main)

Challenge:	Existing intent detection models can only handle predefined intent classes in the offline environment.
Approach:	They propose a method that continually learns new intent classes from new data . structure-based retrospection and contrastive knowledge distillation are used to solve these problems .
Outcome:	The proposed method outperforms existing models on three benchmarks.

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Minjun Zhu, Yixuan Weng, Shizhu He, Kang Liu, Haifeng Liu, Yang jun Jun, Jun Zhao

Pre-trained Language Model Based Active Learning for Sentence Matching (2020.coling-main)

Challenge:	Existing benchmarks for textual question answering only focus on single-chain or single-hop retrieval . Existing approaches to answer complex questions have limitations .
Approach:	They propose to conduct Graph-Hop, a novel multi-chains and multi-hops retrieval paradigm in complex question answering.
Outcome:	The proposed model provides explicit and fine-grained evidence graphs for complex question to support comprehensive and detailed reasoning.

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Guirong Bai, Shizhu He, Kang Liu, Jun Zhao, Zaiqing Nie

ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation (2025.emnlp-main)

Challenge:	Existing active learning approaches for natural language processing ignore the characteristics of natural language.
Approach:	They propose a pre-trained language model based active learning approach for sentence matching that provides linguistic criteria to measure instances and help select more effective instances for annotation.
Outcome:	The proposed approach can achieve greater accuracy with fewer labeled training instances.

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Minghua He, Yue Chen, Fangkai Yang, Pu Zhao, Wenjie Yin, Yu Kang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents (2026.acl-long)

Challenge:	Existing code translation models only learn the contextual semantics of code during pre-training, neglecting executability information closely related to the execution state of the code.
Approach:	They propose an LLM specifically designed for code translation called ExeCoder . it uses executability representations such as functional semantics and syntax structures to enhance LLMs' capabilities.
Outcome:	The proposed model outperforms existing open-source code translation models on two metrics.

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Pengxiang Zhao, Guangyi Liu, Yaozhen Liang, Weiqing He, Zhengxi Lu, WenHao Wang, Yuehao Huang, Yuxiang Chai, Zhaolu Kang, Yaxuan Guo, Hao Wang, Kexin Zhang, Liang Liu, Yong Liu

Green & Sustainable NLP

Challenge:	Shortcuts such as APIs and deep-links have emerged as efficient complements to flexible GUI operations, but systematic evaluation of GUI–shortcut hybrid agents remains underexplored.
Approach:	They propose a benchmark that evaluates GUI-shortcut hybrid agents with a specific focus on the mobile domain.
Outcome:	MAS-Bench evaluates agent's ability to generate shortcuts by discovering and creating reusable, low-cost workflows.

Incremental Intent Detection for Medical Domain with Contrast Replay Networks (2022.findings-acl)

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Guirong Bai, Shizhu He, Kang Liu, Jun Zhao

ItD: Large Language Models Can Teach Themselves Induction through Deduction (2024.acl-long)

Challenge:	Existing methods to detect medical intents require fixed pre-defined intent categories . however, novel medical intent categories incessantly emerge with new data and intents in the real world .
Approach:	They propose to incrementally learn emerged medical intents from continually arriving data of new intents while avoiding catastrophically forgetting old ones.
Outcome:	The proposed method outperforms the state-of-the-art model on two benchmarks by 5.7% and 9.1% accuracy.

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Wangtao Sun, Haotian Xu, Xuanqing Yu, Pei Chen, Shizhu He, Jun Zhao, Kang Liu

Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning (D19-1)

Challenge:	Recent studies have shown that Large Language Models (LLMs) have limited ability to conduct induction.
Approach:	They propose a framework to enable LLMs to teach themselves induction through deduction.
Outcome:	The proposed framework improves performance on two induction benchmarks and shows that it can be used to teach induction through deduction.

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Xiangrong Zeng, Shizhu He, Daojian Zeng, Kang Liu, Shengping Liu, Jun Zhao

Information Extraction & Text Mining

Challenge:	Existing works didn’t consider the extraction order of relational facts in a sentence.
Approach:	They propose to take the extraction order into consideration by applying reinforcement learning into a sequence-to-sequence model.
Outcome:	The proposed model could generate relational facts freely.

KMatrix-2: A Comprehensive Heterogeneous Knowledge Collaborative Enhancement Toolkit for Large Language Model (2025.emnlp-demos)

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Shun Wu, Di Wu, Wangtao Sun, Ziyang Huang, Xiaowei Yuan, Kun Luo, XueYou Zhang, Shizhu He, Jun Zhao, Kang Liu

GATE: Graph-based Adaptive Tool Evolution Across Diverse Tasks (2026.acl-long)

Challenge:	Existing studies on K-LLMs systems focus on declarative knowledge and procedural knowledge (rules) .
Approach:	They propose to build a toolkit that supports comprehensive heterogeneous knowledge collaborative enhancement for Large Language Models (LLMs).
Outcome:	The proposed toolkit provides unified knowledge integration and joint knowledge retrieval methods to achieve more comprehensive heterogeneous knowledge collaborative enhancement.

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Jianwen Luo, Yiming Huang, Jinxiang Meng, Fangyu Lei, Shizhu He, Xiao Liu, Shanshan Jiang, Bin Dong, Jun Zhao, Kang Liu

MoDE-CoTD: Chain-of-Thought Distillation for Complex Reasoning Tasks with Mixture of Decoupled LoRA-Experts (2024.lrec-main)

Challenge:	Existing toolsets that use large language models are limited to single-task settings.
Approach:	They propose a framework that dynamically constructs and evolves a hierarchical graph of reusable tools across multiple scenarios.
Outcome:	The proposed framework achieves up to 4.3 faster milestone completion in Minecraft compared to the previous state-of-the-art method and provides an average improvement of 9.23% over existing tool-making methods in code generation tasks and 10.03% in agent tasks.

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Xiang Li, Shizhu He, Jiayu Wu, Zhao Yang, Yao Xu, Yang jun Jun, Haifeng Liu, Kang Liu, Jun Zhao

Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning (2024.lrec-main)

Challenge:	Current Chain-of-thought Distillation methods hinder CoT reasoning performance . student models are separately distilled from specific reasoning tasks . parameter update of student models severely harms CoT ability on unseen reasoning tasks.
Approach:	They propose a method which distills Chain-of-thought reasoning ability of large language models to much smaller student models.
Outcome:	The proposed method improves the reasoning ability of large language models on 14 datasets.

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Zhitao He, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu, Zhiqiang Zhang, Mengshu Sun, Jun Zhao

FocalOrder: Focal Preference Optimization for Reading Order Detection (2026.acl-long)

Challenge:	Existing studies focus on sentence-level ECI with high-resource languages, leaving document-level DECI with low-resourced languages under-explored.
Approach:	They propose a Heterogeneous Graph Interaction Model with Multi-granularity Contrastive Transfer Learning for zero-shot cross-lingual ECI.
Outcome:	The proposed model outperforms the state-of-the-art model on monolingual and multilingual scenarios by 9.4% and 8.2% of average F1 score.

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Fuyuan Liu, Dianyu Yu, He Ren, Nayu Liu, Xiaomian Kang, Delai Qiu, Fa Zhang, Genpeng Zhen, Shengping Liu, Liang Jiaen, null Weihuang, Yining Wang, Junnan Zhu

Challenge:	Existing methods for document comprehension rely on uniform supervision, resulting in a performance degradation in the intermediate sections.
Approach:	They propose a framework driven by Focal Preference Optimization to detect reading order in document layouts.
Outcome:	The proposed framework outperforms competing baselines and surpasses large-scale general VLMs.

Uncertainty-Calibrated Elastic Alignment for Multimodal Sentiment Analysis with Missing Modalities (2026.findings-acl)

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Kang He, Yuzhe Ding, Rao Fu, Yukang Feng, Kaipeng Zhang, Yiming Liu, Fei Li, Chong Teng, Donghong Ji

S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering (2023.acl-short)

Challenge:	Existing methods for multimodal sentiment analysis are often dynamically incomplete.
Approach:	They propose a new uncertainty-calibrated elastic alignment framework to address these issues by employing probabilistic imputation to capture cross-modal ambiguity and leverage the estimated uncertainty to drive elastic alignment.
Outcome:	The proposed framework outperforms state-of-the-art models in multiple benchmarks and consistently outperformed existing models.

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Fangyu Lei, Xiang Li, Yifan Wei, Shizhu He, Yiming Huang, Jun Zhao, Kang Liu

Why Are We Moral? An LLM-based Agent Simulation Approach to the Study of Moral Evolution (2026.acl-long)

Challenge:	Existing question answering systems use a retriever-reader framework to answer multi-hop questions . existing models lack retrieval, selector, and reasoner capabilities .
Approach:	They propose a three-stage text tableQA framework which comprises of retriever, selector, and reasoner.
Outcome:	The proposed framework outperforms baseline methods in the few-shot setting and ranks first on the HybridQA leaderboard.

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Zhou Ziheng, Huacong Tang, Mingjie Bi, Wanying He, Fang Sun, Yizhou Sun, Ying Nian Wu, Demetri Terzopoulos, Yipeng Kang, Fangwei Zhong

Linguistics & Cognitive NLP

Challenge:	Existing models of moral evolution must abstract away cognitive processes . et al. (2017): evolution of morality presents a puzzle: natural selection favors selfish .
Approach:	They propose an LLM-based agent simulation framework that manipulates cognitive factors to understand moral evolution.
Outcome:	The proposed model exploits cognitive realism to explore moral evolution in a hunter-gatherer society.

A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis (2021.emnlp-main)

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Jiawei Liu, Kaisong Song, Yangyang Kang, Guoxiu He, Zhuoren Jiang, Changlong Sun, Wei Lu, Xiaozhong Liu

Dialogue Systems & Conversational Agents

Challenge:	Recent efforts to predict chatbot failure hatches vital apprehensions due to complexity of human conversation.
Approach:	They propose a model that integrates dialogue satisfaction estimation and handoff prediction in one multi-task learning framework.
Outcome:	The proposed model integrates dialogue satisfaction estimation and handoff prediction in one multi-task learning framework.

SKIntern: Internalizing Symbolic Knowledge for Distilling Better CoT Capabilities into Small Language Models (2025.coling-main)

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Huanxuan Liao, Shizhu He, Yupu Hao, Xiang Li, Yuanzhe Zhang, Jun Zhao, Kang Liu

Prediction and Calibration: Complex Reasoning over Knowledge Graph with Bi-directional Directed Acyclic Graph Neural Network (2023.findings-acl)

Challenge:	Large Language Models (LLMs) have high computational costs and privacy concerns due to their high computational expenses and data privacy.
Approach:	They propose a method that empowers SLMs to internalize symbolic knowledge and few-shot examples gradually through a progressive fine-tuning process.
Outcome:	The proposed approach outperforms state-of-the-art baselines by over 5% while reducing inference costs by up to 4 across a wide range of SLMs in both in-domain (ID) and out-of domain (OOD) tasks.

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Yao Xu, Shizhu He, Li Cai, Kang Liu, Jun Zhao

Structured Data in NLP

Challenge:	Knowledge graphs (KGs) organize world knowledge as interlinked triples which describe entities and their relationships.
Approach:	They propose a bi-directional Directed Acyclic Graph neural network that splits the reasoning process into prediction and calibration.
Outcome:	The proposed model outperforms previous QE models on FB15k, FB16k-237, and NELL995 on prediction and calibration.

Alignment Precedes Fusion: Open-Vocabulary Named Entity Recognition as Context-Type Semantic Matching (2023.findings-emnlp)

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Zhuoran Jin, Pengfei Cao, Zhitao He, Yubo Chen, Kang Liu, Jun Zhao

DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models (2024.emnlp-main)

Challenge:	Continual learning and zero-shot learning approaches have not been adopted to scale to novel-emerging types.
Approach:	They propose a method to recognize entities in novel types by their textual names or descriptions.
Outcome:	The proposed method outperforms the state-of-the-art methods on three challenging OVNER benchmarks by 9.7%, 9.5%, and 1.8% F1-score of novel types.

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Yiming Huang, Jianwen Luo, Yan Yu, Yitong Zhang, Fangyu Lei, Yifan Wei, Shizhu He, Lifu Huang, Xiao Liu, Jun Zhao, Kang Liu

Programming Languages in NLP

Challenge:	DA-Code is a code generation benchmark designed to assess LLMs on agent-based data science tasks.
Approach:	They propose a code generation benchmark specifically designed for LLMs on agent-based data science tasks.
Outcome:	The benchmark performs better than existing frameworks, but lacks accuracy . it is based on real-world data, and includes examples that cover a wide range of tasks .

LaoBench: A Large-Scale Multidimensional Lao Benchmark for Large Language Models (2026.acl-long)

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Jian Gao, Richeng Xuan, Zhaolu Kang, Dingshi Liao, Wenxin Huang, Zongmou Huang, Yangdi Xu, Bowen Qin, Zheqi He, Xi Yang, null Changjinli, Yonghua Lin

Multilinguality

Challenge:	Existing SEA-focused benchmarks miss Lao-specific cultural grounding and linguistic properties.
Approach:	They propose a multi-dimensional benchmark for assessing large language models in Lao . they use open-source and held-out subsets to evaluate languages with a hybrid pipeline .
Outcome:	LaoBench is the first large-scale, high-quality, and multidimensional benchmark for assessing LLM language understanding and reasoning in Lao.

AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing (P19-1)

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Xiang Zhang, Shizhu He, Kang Liu, Jun Zhao

Multilingual Knowledge Graph Completion from Pretrained Language Models with Knowledge Constraints (2023.findings-acl)

Challenge:	Semantic parsing (SP) maps a natural language utterance into a formal language . standard Seq2Seq models ignore underlying grammars and may give ill-formed results.
Approach:	They propose an end-to-end model for semantic parsing that transduces a natural language sentence to the formal semantic representation.
Outcome:	The proposed model outperforms the state-of-the-art models and does not need expertise like predefined grammar or sketches in the meantime.

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Ran Song, Shizhu He, Shengxiang Gao, Li Cai, Kang Liu, Zhengtao Yu, Jun Zhao

Instance-Level Dynamic LoRAs Composition for Cross-Task Generalization (2024.findings-emnlp)

Challenge:	Existing methods for multilingual knowledge graph completion do not align with mKGC tasks because of their English-centric bias.
Approach:	They propose to use multilingual pretrained language models to solve queries in different languages by reasoning a tail entity.
Outcome:	The proposed method outperforms the previous SOTA on Hits@1 and Hits @10 by 12.32% and 16.03% on public datasets.

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Zhiqi Wang, Shizhu He, Kang Liu, Jun Zhao

Challenge:	Large language models perform well on tasks that have undergone fine-tuning of instructions, but performance on completely unseen tasks is often less than ideal.
Approach:	They propose a task-level LoRAs combination which learns the LoRA modules combination weights based on a small number of samples to form the task model.
Outcome:	The proposed method outperforms the typical method, LoraHub, on 16 out of 27 tasks.

Refining and Synthesis: A Simple yet Effective Data Augmentation Framework for Cross-Domain Aspect-based Sentiment Analysis (2024.findings-acl)

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Haining Wang, Kang He, Bobo Li, Lei Chen, Fei Li, Xu Han, Chong Teng, Donghong Ji

AgentsCourt: Building Judicial Decision-Making Agents with Court Debate Simulation and Legal Knowledge Augmentation (2024.findings-emnlp)

Challenge:	Aspect-based Sentiment Analysis (ABSA) data augmentation has attracted increasing attention in recent years due to data sparsity.
Approach:	They propose a framework to augment ABSA data using pseudo labels for target domain . they refine generated labeled data using a natural language inference filter .
Outcome:	The proposed framework outperforms 7 strong baselines on 4 kinds of ABSA tasks.

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Zhitao He, Pengfei Cao, Chenhao Wang, Zhuoran Jin, Yubo Chen, Jiexin Xu, Huaijun Li, Kang Liu, Jun Zhao

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

Challenge:	Recent advances in deep learning have significantly impacted the legal domain.
Approach:	They propose a multi-agent framework for judicial decision-making that simulates the court trial process . they propose 420 Chinese judgment documents to support their framework and build a large-scale legal knowledge base .
Outcome:	The proposed framework outperforms existing methods in various aspects, especially in generating legal articles.

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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.

Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss (D19-1)

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Cao Liu, Kang Liu, Shizhu He, Zaiqing Nie, Jun Zhao

Knowledge Representation

Challenge:	Conventional methods for question generation neglect two crucial research issues: 1) the given predicate needs to be expressed; 2) the answer to the generated question needs to have a definitive answer.
Approach:	They propose a neural encoder-decoder model with multi-level copy mechanisms to generate questions . they also introduce answer-aware loss to make generated questions correspond to more definitive answers.
Outcome:	The proposed model achieves state-of-the-art performance while corresponding to more definitive answers.

Large Language Models are Better Reasoners with Self-Verification (2023.findings-emnlp)

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Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Shengping Liu, Bin Sun, Kang Liu, Jun Zhao

Spectral Disentanglement: Rank-Aware Task Adaptation for Rehearsal-free Continual Learning in LLMs (2026.acl-long)

Challenge:	Existing methods to solve complex natural language processing tasks require multiple steps to verify the answers.
Approach:	They propose to use chain of thought prompting to solve reasoning tasks with large language models.
Outcome:	The proposed method can improve reasoning performance on arithmetic, commonsense, and logical reasoning datasets.

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Huanxuan Liao, Shizhu He, Yupu Hao, Yequan Wang, Wenhao Teng, Xiangwen Liao, Jun Zhao, Kang Liu

Green & Sustainable NLP

Challenge:	Continual Learning (CL) for Large Language Models faces a fundamental Stability-Plasticity Dilemma . Rank-Blindness enforces a single rank constraint across diverse tasks, leading to catastrophic forgetting of earlier tasks and underfitting on complex new ones.
Approach:	They propose a rank-spectrum-based rehearsal-free framework that explicitly disentangles knowledge into two orthogonal subspaces.
Outcome:	The proposed framework achieves a superior stability-plasticity balance compared to single-rank baselines.

Multilingual Knowledge Graph Completion via Efficient Multilingual Knowledge Sharing (2025.findings-emnlp)

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Cunli Mao, Xiaofei Gao, Ran Song, Shizhu He, Shengxiang Gao, Kang Liu, Zhengtao Yu

EMO-RL: Emotion-Rule-Based Reinforcement Learning Enhanced Audio-Language Model for Generalized Speech Emotion Recognition (2025.findings-emnlp)

Challenge:	Existing MKGC research ignores the shareability of cross-lingual knowledge.
Approach:	They propose a multilingual knowledge Graph Completion framework that leverages multilingual shared knowledge to significantly enhance performance through two components: Knowledge-level Grouped Mixture of Experts (KL-GMoE) and Iterative Entity Reranking (IER).
Outcome:	The proposed framework achieves improvements of 5.47%, 3.27%, and 1.01% in the Hits@1, Hits @3, and Hits_10 metrics, respectively, compared with existing state-of-the-art (SOTA) MKGC method.

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Pengcheng Li, Botao Zhao, Zuheng Kang, Junqing Peng, Xiaoyang Qu, Yayun He, Jianzong Wang