Papers by Song He

RWKV: Reinventing RNNs for the Transformer Era (2023.findings-emnlp)

Challenge:	MusicAgent integrates numerous music-related tools and an autonomous workflow to address user requirements.
Approach:	a new system is built to integrate music-related tools and an autonomous workflow . the system is based on large language models (LLMs) that can be used to organize and decompose requests .
Outcome:	the proposed system integrates numerous music-related tools and an autonomous workflow to address user requirements.

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Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, Hayden Lau, Jiaju Lin, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Johan Wind, Stanisław Woźniak, Zhenyuan Zhang, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu

Unsupervised Sentence Representation Learning with Syntactically Aligned Negative Samples (2025.findings-naacl)

Challenge:	recurrent neural networks struggle to match the performance of Transformers due to limitations in parallelization and scalability.
Approach:	They propose a model architecture that combines the efficient parallelizable training of transformers with the efficient inference of RNNs.
Outcome:	The proposed model performs on par with similarly sized RNNs, suggesting future work can leverage this architecture to create more efficient models.

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Zhilan Wang, Zekai Zhi, Rize Jin, Kehui Song, He Wang, Da-Jung Cho

JiraiBench: A Bilingual Benchmark for Evaluating Large Language Models’ Detection of Human risky health behavior Content in Jirai Community (2026.eacl-long)

Challenge:	Existing approaches to sentence representation learning often encounter semantic inconsistencies and feature suppression.
Approach:	They propose a method for generating syntactically aligned negative (SAN) samples using a semantic importance-aware Masked Language Model (MLM) approach.
Outcome:	The proposed method produces negative samples with substantial textual overlap with the original sentences while conveying different meanings.

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Yunze Xiao, Tingyu He, Lionel Z. Wang, Yiming Ma, Xingyu Song, Xiaohang Xu, Mona T. Diab, Irene Li, Ka Chung Ng

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

Challenge:	a cross-lingual dataset captures a transnational cultural phenomenon . risky health behaviors (RHB) are often linked to complex mental health conditions .
Approach:	They present the first cross-lingual dataset that captures a transnational cultural phenomenon . their dataset of more than 15,000 annotated social media posts forms the core of JiraiBench .
Outcome:	The study shows that cultural context can be more influential than linguistic similarity . the study also shows that the Japanese prompts better handle Chinese content .

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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.

UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation (2024.acl-long)

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Xun Liang, Shichao Song, Simin Niu, Zhiyu Li, Feiyu Xiong, Bo Tang, Yezhaohui Wang, Dawei He, Cheng Peng, Zhonghao Wang, Haiying Deng

Q-TOD: A Query-driven Task-oriented Dialogue System (2022.emnlp-main)

Challenge:	Large language models (LLMs) produce hallucinated text, compromising their practical utility in professional contexts.
Approach:	They have developed an unconstrained hallucination generation evaluation benchmark that contains hallucines generated by large language models with minimal restrictions.
Outcome:	The proposed benchmarks are based on a Chinese-language dataset that is lacking in the field.

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Xin Tian, Yingzhan Lin, Mengfei Song, Siqi Bao, Fan Wang, Huang He, Shuqi Sun, Hua Wu

Correctable-DST: Mitigating Historical Context Mismatch between Training and Inference for Improved Dialogue State Tracking (2022.emnlp-main)

Challenge:	Existing pipelined task-oriented dialogue systems have difficulties adapting to unseen domains . end-to-end systems are plagued by large-scale knowledge bases in practice .
Approach:	They propose a query-driven task-oriented dialogue system that extracts dialogue context information into a natural language query.
Outcome:	The proposed system outperforms strong baselines and establishes a new state-of-the-art performance on three publicly available task-oriented dialogue datasets.

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Hongyan Xie, Haoxiang Su, Shuangyong Song, Hao Huang, Bo Zou, Kun Deng, Jianghua Lin, Zhihui Zhang, Xiaodong He

ZSEE: A Dataset based on Zeolite Synthesis Event Extraction for Automated Synthesis Platform (2024.findings-naacl)

Challenge:	Existing dialogue state tracking approaches predict the dialogue state of a target turn sequentially based on the ground-truth previous dialogue state.
Approach:	They propose a method that predicts dialogue state sequentially based on previous dialogue state . they propose generating a previously “predicted” dialogue state using ground-truth previous dialogue states .
Outcome:	The proposed method achieves 67.51%, 68.24%, 70.30%, 71.38%, and 81.27% joint goal accuracy on MultiWOZ 2.0-2.4 datasets.

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Song He, Xin Peng, Yihan Cai, Xin Li, Zhiqing Yuan, WenLi Du, Weimin Yang

Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization (2023.acl-long)

Challenge:	Automated synthesis of zeolite holds great significance for attaining economic and environmental benefits.
Approach:	They propose an event extraction task to mine structural synthesis actions from experimental narratives for modular automated synthesis.
Outcome:	The proposed method can significantly expedite automated synthesis of zeolites owing to its machine readability.

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Pengcheng He, Baolin Peng, Song Wang, Yang Liu, Ruochen Xu, Hany Hassan, Yu Shi, Chenguang Zhu, Wayne Xiong, Michael Zeng, Jianfeng Gao, Xuedong Huang

ALCAP: Alignment-Augmented Music Captioner (2023.emnlp-main)

Challenge:	Z-Code++ is a pre-trained language model optimized for abstractive text summarization.
Approach:	They propose a pre-trained language model optimized for abstractive text summarization that uses a two-phase pre-training technique to improve model's performance.
Outcome:	The proposed model outperforms the competing models on low-resource summarization tasks in zero-shot and few-shot settings.

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Zihao He, Weituo Hao, Wei-Tsung Lu, Changyou Chen, Kristina Lerman, Xuchen Song

GRAIT: Gradient-Driven Refusal-Aware Instruction Tuning for Effective Hallucination Mitigation (2025.findings-naacl)

Challenge:	Traditional approaches to music captioning ignore the intricate interplay between the two . however, a comprehensive understanding of music necessitates the integration of both these elements.
Approach:	They propose a method to learn multimodal alignment between audio and lyrics through contrastive learning.
Outcome:	The proposed method achieves new state-of-the-art on two music captioning datasets.

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Runchuan Zhu, Xinke Jiang, Jiang Wu, Zhipeng Ma, Jiahe Song, Fengshuo Bai, Dahua Lin, Lijun Wu, Conghui He

Robustness via Referencing: Defending against Prompt Injection Attacks by Referencing the Executed Instruction (2026.findings-acl)

Challenge:	Experimental evaluations on open-ended and multiple-choice questions demonstrate GRAIT significantly outperforms existing RAIT methods in the overall performance.
Approach:	They propose a framework to reduce the risk of over-refusal and reduce hallucinations by rejecting unknown questions to minimize hallucinism and ensuring correct answers are not rejected.
Outcome:	The proposed framework outperforms existing methods on open-ended and multiple-choice questions.

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Yulin Chen, Haoran Li, Yuan Sui, Yue Liu, Yufei He, Xiaoling Bai, Chi Fei, Li Yabo, Haozhe Ma, Yangqiu Song, Bryan Hooi

E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service (2022.lrec-1)

Challenge:	Prompt injection attacks manipulate large language models (LLMs) by misleading them to deviate from the original input instructions and execute maliciously injected instructions.
Approach:	They propose a prompt injection defense method that suppresses the model's instruction-following tendencies rather than suppressing them.
Outcome:	The proposed method outperforms prompt-engineering-based approaches and fine-tuning methods and reduces the ASR to nearly 0% in some scenarios.

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Meihuizi Jia, Ruixue Liu, Peiying Wang, Yang Song, Zexi Xi, Haobin Li, Xin Shen, Meng Chen, Jinhui Pang, Xiaodong He

From Pseudo-Balancing to True Specialization: Memory-Aware Routing for Mixture-of-Experts (2026.findings-acl)

Challenge:	Recent research has focused on developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations.
Approach:	They construct an authentic Chinese dialogue dataset consisting of over 25k dialogues and 770k utterances, which contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders.
Outcome:	The proposed dataset contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders.

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Peixuan Hou, Yunbo Hou, Bin Chen, LI He, Jian Xu, Weiping Li, Bo Zheng, Guojie Song

Green & Sustainable NLP

Challenge:	Existing methods to optimize expert-centered load balancing fail to account for pseudo-balance phenomenon . severe knowledge overlap among experts leads to redundant representations and inefficient parameter utilization .
Approach:	They propose a method that prioritizes expert utilization over semantic alignment . they use memory-aware routing to ensure expert load balancing is consistent .
Outcome:	Experimental results show that MAR improves expert specialization by 35% and accuracy by 2%-25% . MAR matches baseline performance with only half the experts .

Bi-Chainer: Automated Large Language Models Reasoning with Bidirectional Chaining (2024.findings-acl)

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Shuqi Liu, Bowei He, Linqi Song

HomoGraphAdapter: A Homogeneous Graph Neural Network as an Effective Adapter for Vision-Language Models (2025.findings-emnlp)

Challenge:	Existing unidirectional chaining methods suffer from low prediction accuracy and efficiency.
Approach:	They propose a bidirectional chaining method which dynamically switches to depth-first reasoning in the opposite reasoning direction when it encounters multiple branching options within the current direction.
Outcome:	The proposed method achieves sizable accuracy boots over unidirectional chaining frameworks on four challenging logical reasoning datasets.

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Chuan He, Zhuozhao Li, Song Guo, Xiaocheng Lu, Jinxiang Lai

D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents (2026.findings-acl)

Challenge:	Existing adaptation methods overlook structural knowledge between text and image modalities or create overly complex graphs containing redundant information for alignment.
Approach:	They propose a method to adapt visual models to downstream tasks using text and image modalities.
Outcome:	The proposed method improves classification accuracy by 1.51% for 1-shot and 0.74% for 16-shot on 11 datasets.

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Hongze Mi, Yibo Feng, WenJie Lu, Yuqi Wang, Jinyuan Li, Song Cao, He Cui, Tengfei Tian, Xuelin Zhang, Haotian Luo, Di Sun, Jun Fang, Hua Chai, Naiqiang Tan, Gang Pan

Challenge:	Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction.
Approach:	They propose a deliberative framework that leverages a fine-grained tip retrieval mechanism to inform its decision-making process.
Outcome:	The proposed framework achieves SOTA among open-source general models on AndroidWorld and ScreenSpot-V2 . it leverages a fine-grained, app-specific tip retrieval mechanism to inform its decision-making process .

Can Indirect Prompt Injection Attacks Be Detected and Removed? (2025.acl-long)

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Yulin Chen, Haoran Li, Yuan Sui, Yufei He, Yue Liu, Yangqiu Song, Bryan Hooi

CLEAN–EVAL: Clean Evaluation on Contaminated Large Language Models (2024.findings-naacl)

Challenge:	Recent studies have developed various detection mechanisms to protect against prompt injection attacks.
Approach:	They investigate the feasibility of detecting and removing indirect prompt injection attacks . they use two methods to evaluate their performance and train detection models .
Outcome:	The proposed method is based on a benchmark dataset and is available on github . it evaluates the performance of existing models and open-source detection models .

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Wenhong Zhu, Hongkun Hao, Zhiwei He, Yun-Ze Song, Jiao Yueyang, Yumeng Zhang, Hanxu Hu, Yiran Wei, Rui Wang, Hongyuan Lu

Cognitive Scaffold: From Fluid Context to Crystallized Memory for Long-Horizon DeepResearch Agents (2026.acl-long)

Challenge:	Existing methods to evaluate large language models are prone to data contamination.
Approach:	They propose a method which parses contaminated data and back-translates it into a candidate set.
Outcome:	The proposed method reduces data contamination and evaluates the LLMs more cleanly.

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Qiuyuan Ai, Zenghuang Fu, Zhaoyang Li, Ping Jiang, Haoyu Wu, Jie Song, Guannan He

Reasoning

Challenge:	Scaling LLM-based agents to long-horizon deep research is constrained by context-noise trade-off . solving a single query may require hundreds of interactions with noisy environments .
Approach:	They propose a factorized memory architecture that decouples the cognitive state into a Fluid Working Context for immediate reasoning and a persistent Knowledge Graph for long-term retention.
Outcome:	The Cognitive Scaffold outperforms baselines on Xbench-DeepSearch, BrowseComp-ZH, and GAIA . it achieves 74.7% Avg@3 and 87.0% Pass@3 on xbench, browseComp, and 88.3% Pass@3.

Cross-lingual Text-to-SQL Semantic Parsing with Representation Mixup (2022.findings-emnlp)

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Peng Shi, Linfeng Song, Lifeng Jin, Haitao Mi, He Bai, Jimmy Lin, Dong Yu

Multilinguality

Challenge:	Experimental results show that Rex can benefit from cross-lingual training and improve the effectiveness of semantic parsers.
Approach:	They propose a Representation Mixup Framework for effectively exploiting translations in the cross-lingual Text-to-SQL task.
Outcome:	The proposed framework can benefit from cross-lingual training and improve the effectiveness of semantic parsers, achieving state-of-the-art performance.

Modularized Interaction Network for Named Entity Recognition (2021.acl-long)

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Fei Li, Zheng Wang, Siu Cheung Hui, Lejian Liao, Dandan Song, Jing Xu, Guoxiu He, Meihuizi Jia

Token Preference Optimization with Self-Calibrated Visual-Anchored Rewards for Hallucination Mitigation (2025.findings-emnlp)

Challenge:	Named Entity Recognition (NER) models focus on word-level information, while segment-based models focus only on word level information.
Approach:	They propose a Modularized Interaction Network (MIN) model which utilizes both word-level information and segment-level dependencies.
Outcome:	The proposed model outperforms the current state-of-the-art models on three NER benchmark datasets.

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Jihao Gu, Yingyao Wang, Meng Cao, Pi Bu, Jun Song, Bo Zheng, Yancheng He, Shilong Li

Hire a Linguist!: Learning Endangered Languages in LLMs with In-Context Linguistic Descriptions (2024.findings-acl)

Challenge:	Existing methods for predicting hallucinations suffer from two drawbacks: Lack of scalable token-level rewards and Neglect of visual-anchored tokens.
Approach:	They propose a Token Preference Optimization model with self-calibrated rewards . they propose based on visual-anchored tokens and visual-aware training objective .
Outcome:	The proposed model improves hallucination performance by focusing on visual-anchored tokens without fine-grained annotations.

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Kexun Zhang, Yee Choi, Zhenqiao Song, Taiqi He, William Yang Wang, Lei Li

K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce (2021.findings-emnlp)

Challenge:	Existing LLMs rarely perform well in unseen, endangered languages . Existing models such as Llama and GPT-4 lack a rich corpus of training data .
Approach:	They propose a training-free approach to enable an LLM to process unseen languages that hardly occur in its pre-training.
Outcome:	The proposed approach elevates translation capability from GPT-4’s 0 to 10.5 BLEU for 10 language directions.

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Song Xu, Haoran Li, Peng Yuan, Yujia Wang, Youzheng Wu, Xiaodong He, Ying Liu, Bowen Zhou

See the World, Discover Knowledge: A Chinese Factuality Evaluation for Large Vision Language Models (2025.findings-acl)

Challenge:	Existing pre-trained language models are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios.
Approach:	They propose a knowledge-injected pre-trained language model that can be transferred to both natural language understanding and generation tasks.
Outcome:	The proposed model significantly outperforms baselines across the board in e-commerce scenarios.

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Jihao Gu, Yingyao Wang, Pi Bu, Chen Wang, Ziming Wang, Tengtao Song, Donglai Wei, Jiale Yuan, Yingxiu Zhao, Yancheng He, Shilong Li, Jiaheng Liu, Meng Cao, Jun Song, Yingshui Tan, Xiang Li, Wenbo Su, Xiaoyong Zhu, Bo Zheng

Why LLMs Hallucinate on Structured Knowledge: A Mechanistic Analysis of Reasoning over Linearized Representations (2026.acl-long)

Challenge:	Existing models for large vision language models do not fully reflect their knowledge capacity and reliability, resulting in erroneous outputs that do not align with the image content or provide answers lacking knowledge evidence.
Approach:	They propose a Chinese-based benchmark for visual factuality across 8 major topics and 56 subtopics and a multi-hop question construction.
Outcome:	The proposed model decouples visual factuality into two parts: seeing the world and discovering knowledge.

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Shanghao Li, Jinda Han, Yibo Wang, Yuanjie Zhu, Zihe Song, Langzhou He, Kenan Kamel A Alghythee, Philip S. Yu

CMHKF: Cross-Modality Heterogeneous Knowledge Fusion for Weakly Supervised Video Anomaly Detection (2025.acl-long)

Challenge:	Existing literature primarily addresses this problem through external interventions such as retrieval augmentation and prompt engineering at the input or output level.
Approach:	They find that LLMs can still produce hallucinated outputs when using structured external knowledge.
Outcome:	The proposed models fail to ground the provided knowledge, causing the model to revert to parametric memory.

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Guohua Wang, Shengping Song, Wuchun He, Yongsen Zheng

WebSTAR: Scalable Data Synthesis for Computer Use Agents with Step-Level Filtering (2026.acl-long)

Challenge:	Existing methods focus mainly on visual modalities, neglecting rich multi-modality information.
Approach:	They propose a framework that integrates cross-modality knowledge from video, audio and text to improve anomaly detection and localization.
Outcome:	The proposed framework improves detection and localization of anomalies using video-level labels.

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Yifei He, Pranit Chawla, Yaser Souri, Subhojit Som, Xia Song

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

Challenge:	Existing datasets rely on human demonstrations, limiting scalability.
Approach:	They propose a scalable data synthesis pipeline that transforms noisy rollouts into reliable supervision without human annotation.
Outcome:	The proposed pipeline transforms noisy rollouts into reliable supervision without human annotation.

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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

Beyond the Tip of Efficiency: Uncovering the Submerged Threats of Jailbreak Attacks in Small Language Models (2025.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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Sibo Yi, Tianshuo Cong, Xinlei He, Qi Li, Jiaxing Song

CAMIEval: Enhancing NLG Evaluation through Multidimensional Comparative Instruction-Following Analysis (2025.naacl-long)

Challenge:	Small language models (SLMs) have become increasingly prominent in the deployment on edge devices due to their high efficiency and low computational cost.
Approach:	They evaluate the security performance of 13 state-of-the-art small language models under various jailbreak attacks.
Outcome:	The proposed methods demonstrate that SLMs are quite susceptible to jailbreak attacks and some are even vulnerable to harmful prompts.

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Ziyue Fan, Junliang He, Li Xiaoqing, Shaohui Kuang, Kai Song, Yaqian Zhou, Xipeng Qiu

Text Generation

Challenge:	Evaluating the quality of texts generated by language models has always been a challenging task in natural language processing (NLP).
Approach:	They propose a multidimensional comparative evaluation method based on instruction-following that combines relevance, factuality, and adherence with a concrete Chain-of-Thoughts process to enhance the accuracy of evaluations.
Outcome:	The proposed method outperforms existing methods in correlation with human evaluations on two NLG evaluation benchmarks.

Self-Attention Guided Copy Mechanism for Abstractive Summarization (2020.acl-main)

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Song Xu, Haoran Li, Peng Yuan, Youzheng Wu, Xiaodong He, Bowen Zhou

Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset (2021.emnlp-main)

Challenge:	Abstractive summarization models have been widely used to extract words from source into summary, but how to ensure that important words in source are copied remains a challenge.
Approach:	They propose a Transformer-based model to enhance copy mechanism by identifying the importance of each source word based on the degree centrality.
Outcome:	The proposed model outperforms baseline methods on CNN/Daily Mail and Gigaword datasets.

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Tianqing Fang, Weiqi Wang, Sehyun Choi, Shibo Hao, Hongming Zhang, Yangqiu Song, Bin He

Reasoning

Challenge:	Existing evaluations on the population task are either not accurate (automatic evaluation with randomly sampled negative examples) or of small scale (human annotation).
Approach:	They propose a reasoning over commonsense knowledge bases (CSKBs) that are free-text and have a human annotation set to probe commonsensical reasoning.
Outcome:	The proposed model is based on a human-annotated evaluation set and is compared with existing models on the population task.

PRINCE: Prefix-Masked Decoding for Knowledge Enhanced Sequence-to-Sequence Pre-Training (2022.emnlp-main)

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Song Xu, Haoran Li, Peng Yuan, Youzheng Wu, Xiaodong He

FinReporting: An Agentic Workflow for Localized Reporting of Cross-Jurisdiction Financial Disclosure (2026.acl-demo)

Challenge:	Existing studies focus on injecting noises into the input sequence, but feasibility of injecting them into the decoding sequence remains an open question.
Approach:	They propose a pre-training paradigm that integrates knowledge-enhanced decoding with noises in the prefix to strengthen the representation learning of entities that span over multiple input tokens.
Outcome:	The proposed model achieves state-of-the-art results on two knowledge-driven data-to-text generation tasks with up to 2% BLEU gains.

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Fan Zhang, Mingzi Song, Rania Elbadry, Yankai Chen, Shaobo Wang, Yixi Zhou, Xunwen Zheng, Yueru He, Yuyang Dai, Georgi Nenkov Georgiev, Ayesha Gull, Muhammad Usman Safder, Fan Wu, Liyuan Meng, Fengxian Ji, Junning Zhao, Xueqing Peng, Jimin Huang, YU Chen, Xue Liu, Preslav Nakov, Zhuohan Xie

Challenge:	FinReporting is an agentic workflow for localized cross-jurisdiction financial reporting . existing approaches assume a single-market setting and overlook structural differences across jurisdictions .
Approach:	They propose a workflow that decomposes financial reporting into auditable stages . they use Large Language Models to extract and summarize corporate disclosures .
Outcome:	The proposed system decomposes reporting into auditable stages . it improves consistency and reliability under heterogeneous reporting regimes.

Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPT (2023.emnlp-main)

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Xiaoshuai Song, Keqing He, Pei Wang, Guanting Dong, Yutao Mou, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu

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

Challenge:	Existing methods to fine-tune discriminative models address these challenges by focusing on in-domain intents.
Approach:	They evaluate ChatGPT on OOD intent discovery and generalized intent discovery tasks . they outline the strengths and weaknesses of ChatGPt and outline their results .
Outcome:	The proposed task aims to extend a closed intent classifier to open-world intent sets.

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

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

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

FiNE: Filtering and Improving Noisy Data Elaborately with Large Language Models (2025.naacl-long)

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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Junliang He, Ziyue Fan, Shaohui Kuang, Li Xiaoqing, Kai Song, Yaqian Zhou, Xipeng Qiu

Reading Like HER: Human Reading Inspired Extractive Summarization (D19-1)

Challenge:	Currently, there are two mainstream methods for improving data integrity: data filtering and data augmentation.
Approach:	They propose a method to improve data integrity by combining data filtering and data augmentation with LLMs.
Outcome:	The proposed method surpasses the open-source chat version on HalluQA by 8.45 on the open source version.

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Ling Luo, Xiang Ao, Yan Song, Feiyang Pan, Min Yang, Qing He

Continual Generalized Intent Discovery: Marching Towards Dynamic and Open-world Intent Recognition (2023.findings-emnlp)

Challenge:	Existing methods for extracting text summarization are abstractive and extractive.
Approach:	They propose a novel approach for extractive summarization by simulating two stages . they adopt a convolutional neural network to encode gist of paragraphs for rough reading .
Outcome:	The proposed method significantly outperforms the state-of-the-art extractive methods on CNN and DailyMail datasets.

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Xiaoshuai Song, Yutao Mou, Keqing He, Yueyan Qiu, Jinxu Zhao, Pei Wang, Weiran Xu

Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection (2024.naacl-long)

Challenge:	Currently, the generalized intent classification system only considers one stage of OOD learning and requires all IND data for joint training.
Approach:	They propose a task that detects OOD intents from dynamic OOD data streams . they propose CGID method that bootstraps new intent discovery through class prototypes .
Outcome:	The proposed task can detect out-of-domain (OOD) queries and extend them to the in-domain classifier . it can safely and efficiently detect out of-domain queries and avoid wrong operations .

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Jianfeng He, Hang Su, Jason Cai, Igor Shalyminov, Hwanjun Song, Saab Mansour

Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection (2024.lrec-main)

Challenge:	Semi-supervised dialogue summarization (SSDS) leverages model-generated summaries to reduce reliance on human-labeled data.
Approach:	They propose a scoring approach that encapsulates three primary dimensions of summarization model quality.
Outcome:	The proposed method reduces reliance on human-labeled data and improves the performance of summarization models.

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Pei Wang, Keqing He, Yejie Wang, Xiaoshuai Song, Yutao Mou, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu

Sentiment Analysis

Challenge:	Out-of-domain (OOD) intent detection is crucial for task-oriented dialogue systems.
Approach:	They conduct a comprehensive evaluation of large language models (LLMs) under various experimental settings and outline their strengths and weaknesses.
Outcome:	The proposed models exhibit strong zero-shot and few-shot capabilities, but is still at a disadvantage compared to models fine-tuned with full resource.

Scaling Laws for Multilingual Language Models (2025.findings-acl)

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Yifei He, Alon Benhaim, Barun Patra, Praneetha Vaddamanu, Sanchit Ahuja, Parul Chopra, Vishrav Chaudhary, Han Zhao, Xia Song

On the Faithfulness for E-commerce Product Summarization (2020.coling-main)

Challenge:	Existing scaling laws for language models are limited to a limited number of languages, but they can be applied to arbitrary number of different languages.
Approach:	They propose a scaling law for general-purpose decoder-only language models trained on multilingual data that shifts focus from individual languages to language families.
Outcome:	The proposed scaling law can be applied to models trained on multilingual data . it can be used to predict performance across multiple languages and models .

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Peng Yuan, Haoran Li, Song Xu, Youzheng Wu, Xiaodong He, Bowen Zhou

Text Generation

Challenge:	e-commerce product summarization requires consistency between product attributes and summary . inconsistent product summaries can mislead users and decrease public credibility .
Approach:	They propose a model to generate e-commerce product summaries with product attributes . they encode product attribute table and constrain attribute words to be presented only through copying .
Outcome:	The proposed model significantly improves the faithfulness of e-commerce product summarization tasks.

Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discovery (2023.acl-long)

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Yutao Mou, Xiaoshuai Song, Keqing He, Chen Zeng, Pei Wang, Jingang Wang, Yunsen Xian, Weiran Xu

Never Lost in the Middle: Mastering Long-Context Question Answering with Position-Agnostic Decompositional Training (2024.acl-long)

Challenge:	Existing methods for generalized intent discovery lack pseudo label disambiguation and representation learning.
Approach:	They propose a prototype learning framework to decouple pseudo label disambiguation and representation learning.
Outcome:	The proposed method can decouple pseudo label disambiguation and representation learning.

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Junqing He, Kunhao Pan, Xiaoqun Dong, Zhuoyang Song, LiuYiBo LiuYiBo, Qianguosun Qianguosun, Yuxin Liang, Hao Wang, Enming Zhang, Jiaxing Zhang

APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection (2023.findings-emnlp)

Challenge:	Large language models suffer from severe hallucinations, compromising performance in knowledge-oriented QA, dialogue, and writing.
Approach:	They propose to enhance the information searching and reflection ability of large language models by training them in position-agnostic multi-step QA tasks to improve their model's accuracy.
Outcome:	The proposed model improves in multi-doc QA and other benchmarks by 13.7% absolute gain in shuffled settings, by 21.5% in passage retrieval task.

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Pei Wang, Keqing He, Yutao Mou, Xiaoshuai Song, Yanan Wu, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu

Low-Resource NLP

Challenge:	Existing methods for detecting out-of-domain (OOD) intents are hard to label . previous studies use labeled in-domain data to learn intent representations .
Approach:	They propose a prototypical pseudo-labeling method for few-shot OOD detection . they propose 'protoOOD' framework and adaptive pseudo-labeled method .
Outcome:	The proposed method is able to detect out-of-domain (OOD) intents from user queries.

EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association (2025.acl-long)

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Weiqi Wang, Limeng Cui, Xin Liu, Sreyashi Nag, Wenju Xu, Chen Luo, Sheikh Muhammad Sarwar, Yang Li, Hansu Gu, Hui Liu, Changlong Yu, Jiaxin Bai, Yifan Gao, Haiyang Zhang, Qi He, Shuiwang Ji, Yangqiu Song

Beyond Emotion: A Multi-Modal Dataset for Human Desire Understanding (2022.naacl-main)

Challenge:	Goal-oriented script planning is used by humans to plan for typical activities . however, this capability remains underexplored due to several challenges .
Approach:	They propose a framework that enables product-enriched scripts by associating products with each step based on the semantic similarity between the actions and their purchase intentions.
Outcome:	The proposed framework can generate product-enriched scripts from 2.4 million scripts . human annotations are conducted to provide gold labels for a sampled subset .

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Ao Jia, Yu He, Yazhou Zhang, Sagar Uprety, Dawei Song, Christina Lioma

CoRAG: Enhancing Hybrid Retrieval-Augmented Generation through a Cooperative Retriever Architecture (2025.findings-emnlp)

Challenge:	Desire is a primitive instinct and a need for strongly expressing human desires to get or possess something.
Approach:	They propose to use MSED to model and understand human desire . they propose to provide a benchmark for human desire analysis .
Outcome:	The proposed dataset contains 9,190 text-image pairs with English text.

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Zaiyi Zheng, Song Wang, Zihan Chen, Yaochen Zhu, Yinhan He, Liangjie Hong, Qi Guo, Jundong Li

Double Path Networks for Sequence to Sequence Learning (C18-1)

Challenge:	Existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base, which often miss relevant information located further away from a global view.
Approach:	Hybrid-RAG combines textual documents and graph-structured relational information for RAG . existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base .
Outcome:	Hybrid-RAG combines textual documents and graph-structured relational information . existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base .

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Kaitao Song, Xu Tan, Di He, Jianfeng Lu, Tao Qin, Tie-Yan Liu

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

Challenge:	Existing approaches for Sequence to Sequence learning have been developed . convolutional neural networks and self-attention networks are the most popular .
Approach:	They propose to integrate convolutional and self-attention layers into a double path network for sequence to sequence learning.
Outcome:	The proposed method significantly improves performance over state-of-the-art systems.

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

AceGPT, Localizing Large Language Models in Arabic (2024.naacl-long)

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.

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Huang Huang, Fei Yu, Jianqing Zhu, Xuening Sun, Hao Cheng, Song Dingjie, Zhihong Chen, Mosen Alharthi, Bang An, Juncai He, Ziche Liu, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu

Faithful, Unfaithful or Ambiguous? Multi-Agent Debate with Initial Stance for Summary Evaluation (2025.naacl-long)

Challenge:	Significant concerns emerge when addressing cultural sensitivity and local values.
Approach:	They propose a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models.
Outcome:	The proposed model sets the state-of-the-art standard for open Arabic LLMs across various benchmarks.

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Mahnaz Koupaee, Jake W. Vincent, Saab Mansour, Igor Shalyminov, Han He, Hwanjun Song, Raphael Shu, Jianfeng He, Yi Nian, Amy Wing-mei Wong, Kyu J. Han, Hang Su

Challenge:	Existing approaches to evaluate faithfulness of summaries are often fooled by the fluency of the text and struggle with identifying errors.
Approach:	They propose an approach to summary faithfulness evaluation where multiple LLM-based agents are assigned initial stances and forced to come up with a reason to justify belief.
Outcome:	The proposed approach can identify ambiguities and have even stronger performance on non-ambiguous summaries.

Tracking Satisfaction States for Customer Satisfaction Prediction in E-commerce Service Chatbots (2022.coling-1)

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Yang Sun, Liangqing Wu, Shuangyong Song, Xiaoguang Yu, Xiaodong He, Guohong Fu

IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce (2024.findings-emnlp)

Challenge:	Existing models for customer satisfaction prediction (CSP) focus on analyzing subjective customer satisfaction in conversational service, but they are hard to represent the important dynamic satisfaction states throughout the customer journey.
Approach:	They propose a model to track customer satisfaction in chatbots using a dialogue-level classification module to represent the dynamic satisfaction states at each turn.
Outcome:	The proposed model outperforms baselines and shows that it significantly outperformed multiple baselines.

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Wenxuan Ding, Weiqi Wang, Sze Kwok, Minghao Liu, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Junxian He, Yangqiu Song

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

Challenge:	Existing approaches that distill intentions from LMs fail to generate meaningful and human-centric intentions applicable in real-world E-commerce contexts.
Approach:	They propose a double-task multiple-choice question answering benchmark to evaluate LMs' comprehension of purchase intentions in E-commerce.
Outcome:	The proposed benchmark consists of 4,360 carefully curated problems across three difficulty levels, constructed using an automated pipeline to ensure scalability on large E-commerce platforms.

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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

Learn to Copy from the Copying History: Correlational Copy Network for Abstractive Summarization (2021.emnlp-main)

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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Haoran Li, Song Xu, Peng Yuan, Yujia Wang, Youzheng Wu, Xiaodong He, Bowen Zhou

Text Generation

Challenge:	Existing methods for abstractive summarization use encoder-decoder attention, but this leads to incomplete copying.
Approach:	They propose a copying scheme that takes advantage of prior copying distributions and explicitly encourages the model to copy the input word that is relevant to the previously copied one.
Outcome:	The proposed scheme achieves state-of-the-art on summarization benchmarks . it takes advantage of prior copying distributions and explicitly encourages copying .

Sens-Merging: Sensitivity-Guided Parameter Balancing for Merging Large Language Models (2025.findings-acl)

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Shuqi Liu, Han Wu, Bowei He, Xiongwei Han, Mingxuan Yuan, Linqi Song

Uni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systems (2023.findings-acl)

Challenge:	Existing task vector-based model merging methods apply uniform coefficients across all parameters, overlooking varying parameter importance both within and across tasks.
Approach:	They propose a sensitivity-guided coefficient adjustment method that optimizes existing model merging techniques by operating at both task-specific and cross-task levels.
Outcome:	The proposed method outperforms existing model merging techniques on mistral 7B and LLaMA2 7B/13B models and enables them to outperformed specialized models.

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Chiyu Song, Hongliang He, Haofei Yu, Pengfei Fang, Leyang Cui, Zhenzhong Lan

INT: Establishing Information Transfer for Multilingual Intent Detection and Slot Filling (2025.findings-acl)

Challenge:	Existing approaches to decode a given context-candidate pair are expensive and time-consuming.
Approach:	They propose a new paradigm that keeps full attention over each pair while only encoding the context once.
Outcome:	The proposed paradigm achieves new state-of-the-art results on four benchmark datasets with high computational efficiency.

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Di Wu, Liting Jiang, Bohui Mao, Hongyan Xie, Haoxiang Su, Zhongjiang He, Ruiyu Fang, Shuangyong Song, Hao Huang, Xuelong Li

Sentiment Analysis

Challenge:	Existing studies struggle to achieve performance comparable to that on high-resource languages due to inherent linguistic diversity of multilingual SLU tasks.
Approach:	They propose a multilingual information transfer network to solve these challenges . they propose to reformulate SF as a span prediction problem and introduce a slot-matching attention mechanism to achieve slot alignment across languages.
Outcome:	The proposed model outperforms baseline models on the MASSIVE and MASSIV-UG datasets in overall accuracy across all languages.

LMGQS: A Large-scale Dataset for Query-focused Summarization (2023.findings-emnlp)

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Ruochen Xu, Song Wang, Yang Liu, Shuohang Wang, Yichong Xu, Dan Iter, Pengcheng He, Chenguang Zhu, Michael Zeng

Summarization

Challenge:	Lack of large-scale datasets for query-focused summarization hinders model development . lack of data limits the ability of QFS models to train robust neural models .
Approach:	They propose to generate a query for each summary sentence in a generic summarization annotation using a pretrained language model.
Outcome:	The proposed model achieves state-of-the-art zero-shot and supervised performance on multiple existing QFS benchmarks.

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

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

ISR: Self-Refining Referring Expressions for Entity Grounding (2025.acl-long)

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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Zhuocheng Yu, Bingchan Zhao, Yifan Song, Sujian Li, Zhonghui He

BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data (2026.eacl-long)

Challenge:	Entity grounding is a crucial task in the construction of multimodal knowledge graphs.
Approach:	They propose a novel scheme to enhance the multimodal large language model's capability to generate high quality REs for the given entities as explicit contextual clues.
Outcome:	The proposed method surpasses other methods in entity grounding, highlighting its effectiveness, robustness and potential for broader applications.

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Jaap Jumelet, Abdellah Fourtassi, Akari Haga, Bastian Bunzeck, Bhargav Shandilya, Diana Galvan-Sosa, Faiz Ghifari Haznitrama, Francesca Padovani, Francois Meyer, Hai Hu, Julen Etxaniz, Laurent Prevot, Linyang He, María Grandury, Mila Marcheva, Negar Foroutan, Nikitas Theodoropoulos, Pouya Sadeghi, Siyuan Song, Suchir Salhan, Susana Zhou, Yurii Paniv, Ziyin Zhang, Arianna Bisazza, Alex Warstadt, Leshem Choshen

Multilinguality

Challenge:	prevailing trend in language modeling research is to prioritize scaling, authors say . from infancy to maturity, English learners acquire language through exposure to less than 100M words .
Approach:	They propose a multilingual collection of datasets modeling the language a person observes from birth until they acquire a native language.
Outcome:	The proposed models outperform models trained on a fixed, developmentally plausible English corpus on various benchmarks.

WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback (2026.acl-long)

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Taiwei Shi, Zhuoer Wang, Longqi Yang, Ying-Chun Lin, Zexue He, Mengting Wan, Pei Zhou, Sujay Kumar Jauhar, Sihao Chen, Shan Xia, Hongfei Zhang, Jieyu Zhao, Xiaofeng Xu, Xia Song, Jennifer Neville

Challenge:	Traditional alignment methods rely on human annotations and are subjective and misalignment with real-world user preferences.
Approach:	They propose a framework that leverages in-situ user feedback during conversations with LLMs to create preference datasets automatically.
Outcome:	The proposed framework identifies and classifies user feedback to LLM responses between conversation turns and creates examples of preferred and dispreferred responses according to user preferences.

RevCore: Review-Augmented Conversational Recommendation (2021.findings-acl)

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Yu Lu, Junwei Bao, Yan Song, Zichen Ma, Shuguang Cui, Youzheng Wu, Xiaodong He

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

Challenge:	Existing conversational recommendation systems lack item information when conducted on short dialogue history and unfamiliar items.
Approach:	They propose a framework where reviews are seamlessly incorporated into conversational recommendation systems.
Outcome:	The proposed framework yields better performance on recommendation and conversation responding.

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

TinyJudge: Unverifiable Constraint Alignment via Lightweight Specialist Ensembles (2026.acl-long)

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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Yirong Zeng, Yufei Liu, Xiao Ding, Yutai Hou, Yuxian Wang, Wu Ning, Haonan Song, Dandan Tu, Qixun Zhang, Yuxiang He, Bibo Cai, Ting Liu

Challenge:	Instruction Following (IF) is a core capability of LLMs, requiring strict adherence to diverse constraints.
Approach:	They propose a framework that uses tiny language models to evaluate instruction following . they propose to use a set of specialized tiny language model to provide rewards for soft constraints.
Outcome:	The proposed framework outperforms baseline models by 12% and speeds up training time by 3.

GenTool: Enhancing Tool Generalization in Language Models through Zero-to-One and Weak-to-Strong Simulation (2025.findings-acl)

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Jie He, Jennifer Neville, Mengting Wan, Longqi Yang, Hui Liu, Xiaofeng Xu, Xia Song, Jeff Z. Pan, Pei Zhou