Papers by Xin Song

The Retrieval Bottleneck: Scaling Laws for Reinforcement Learning in RAG (2026.acl-long)

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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Shu Zhou, Jinman Leng, Yufei Song, Xin Wang, Tao Fan, Hao Wang

Information Retrieval

Challenge:	Retrieval-augmented generation (RAG) has become the dominant paradigm for building knowledge-intensive language systems.
Approach:	They propose a sigmoidal scaling law that shows that retrieval quality determines the asymptotic performance ceiling.
Outcome:	The proposed model achieves strong performance on knowledge-intensive benchmarks while retaining the predictable scaling long available for pre-training but previously absent in RAG-RL.

ZipVoice-Dialog: Non-Autoregressive Spoken Dialogue Generation with Flow Matching (2026.findings-acl)

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Han Zhu, Wei Kang, Liyong Guo, Zengwei Yao, Fangjun Kuang, Weiji Zhuang, Zhaoqing Li, Zhifeng Han, Dong Zhang, Xin Zhang, Xingchen Song, Lingxuan Ye, Long Lin, Daniel Povey

Dialogue Systems & Conversational Agents

Challenge:	Existing autoregressive models for dialogue generation suffer from high latency and stability issues.
Approach:	They propose a non-autoregressive (NAR) zero-shot spoken dialogue generation model based on flow-matching.
Outcome:	The proposed model outperforms existing models in speech generation due to poor speech intelligibility and turn-taking precision.

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

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

Natural Language Interfaces

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.

F2RL: Factuality and Faithfulness Reinforcement Learning Framework for Claim-Guided Evidence-Supported Counterspeech Generation (2024.emnlp-main)

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Haiyang Wang, Yuchen Pan, Xin Song, Xuechen Zhao, Minghao Hu, Bin Zhou

A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation (2024.acl-long)

Challenge:	Existing methods for generating evidence-supported counterspeech lack clear guidance with a core claim for organizing evidence.
Approach:	They propose a Factuality and Faithfulness Reinforcement Learning framework for generating claim-guided and evidence-supported counterspeech (F2RL) they generate counter-claims based on hate speech and design a self-evaluation mechanism to select the most appropriate one.
Outcome:	The proposed framework achieves excellent performance on three benchmark datasets with strong factuality and faithfulness.

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Kai Chen, Ye Wang, Yitong Li, Aiping Li, Han Yu, Xin Song

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

Challenge:	Existing methods for temporal knowledge graphs de-emphasize temporal correlations between facts sequences and ignore inferring clues from missing facts.
Approach:	They propose a Temporal PAth-based reasoning model that is robust to ambiguous temporal data.
Outcome:	The proposed model outperforms SOTA methods on the link prediction task.

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

Information Extraction & Text Mining

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.

RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement (2025.naacl-long)

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Jinhao Jiang, Jiayi Chen, Junyi Li, Ruiyang Ren, Shijie Wang, Xin Zhao, Yang Song, Tao Zhang

On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions (2025.findings-emnlp)

Challenge:	Existing large language models (LLMs) show exceptional problem-solving capabilities but struggle with complex reasoning tasks.
Approach:	They propose a novel RAG approach that integrates retrieved information to guide tree-based reasoning process based on LLMs.
Outcome:	The proposed approach outperforms existing methods in large language models . iteratively plans intermediate sub-queries and answers based on the LLM itself .

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Weiqi Wang, Tianqing Fang, Haochen Shi, Baixuan Xu, Wenxuan Ding, Liyu Zhang, Wei Fan, Jiaxin Bai, Haoran Li, Xin Liu, Yangqiu Song

Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting (2023.findings-emnlp)

Challenge:	Conceptualization is a fundamental element of human cognition and plays a pivotal role in generalizable reasoning.
Approach:	They propose to categorize different types of conceptualizations into four levels based on the types of instances being conceptualized.
Outcome:	The proposed categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized .

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Haoyang Huang, Tianyi Tang, Dongdong Zhang, Xin Zhao, Ting Song, Yan Xia, Furu Wei

Low-Resource NLP

Challenge:	Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages.
Approach:	They propose a generic template prompt that stimulates cross-lingual and logical reasoning skills to enhance task performance across languages.
Outcome:	The proposed method improves multilingual capability across languages and covers high-resource and low-resourced languages.

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

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

Natural Language Interfaces

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.

Generative Psycho-Lexical Approach for Constructing Value Systems in Large Language Models (2025.acl-long)

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Haoran Ye, TianZe Zhang, Yuhang Xie, Liyuan Zhang, Yuanyi Ren, Xin Zhang, Guojie Song

Intent-Aware and Hate-Mitigating Counterspeech Generation via Dual-Discriminator Guided LLMs (2024.lrec-main)

Challenge:	Large Language Models (LLMs) have raised concerns regarding their intrinsic values.
Approach:	They propose a psychologically grounded five-factor value system for Large Language Models that integrates psychological principles with cutting-edge AI priorities.
Outcome:	The proposed value system meets standard psychological criteria, improves LLM safety prediction, and enhances Llm alignment, when compared to the canonical Schwartz’s values.

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Haiyang Wang, Zhiliang Tian, Xin Song, Yue Zhang, Yuchen Pan, Hongkui Tu, Minlie Huang, Bin Zhou

MusKGC: A Flexible Multi-source Knowledge Enhancement Framework for Open-World Knowledge Graph Completion (2025.emnlp-main)

Challenge:	Hate speech is an aggressive expression that incites hatred towards specific groups based on their group identity.
Approach:	They propose an LLMs-based framework for counterspeech generation that uses intent-aware discriminators to decode intents of LLM models.
Outcome:	The proposed framework matches intents with hate mitigation intents and performs well.

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Xin Song, Liu Haiyan, Haiyang Wang, Ye Wang, Kai Chen, Bin Zhou

Structured Data in NLP

Challenge:	Open-world knowledge graph completion (KGC) aims to infer novel facts by enriching existing graphs with external knowledge sources while maintaining semantic consistency under the open-world assumption (OWA).
Approach:	They propose a multi-source knowledge enhancement framework based on an open-world assumption (OWA) that integrates external knowledge sources and a new evaluation strategy to validate new facts.
Outcome:	The proposed model achieves SOTA performance across benchmarks and the evaluation strategy effectively assesses new facts under OWA.

KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph (2025.acl-long)

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Jinhao Jiang, Kun Zhou, Xin Zhao, Yang Song, Chen Zhu, Hengshu Zhu, Ji-Rong Wen

Knowledge Representation

Challenge:	Existing methods to design the interaction strategy between large language models and knowledge graphs (KGs) are not effective for large language model (LLM)s to solve complex tasks due to the large volume and structured format of KG data.
Approach:	They propose an LLM-based agent framework that enables small LLMs to actively make decisions over knowledge graphs.
Outcome:	The proposed framework outperforms existing methods on in-domain and out-domain datasets using 10K samples.

Relation Discovery with Out-of-Relation Knowledge Base as Supervision (N19-1)

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Yan Liang, Xin Liu, Jianwen Zhang, Yangqiu Song

Reasoning-Enhanced Domain-Adaptive Pretraining of Multimodal Large Language Models for Short Video Content Governance (2025.emnlp-industry)

Challenge:	Existing methods to extract relations from text corpus without annotated data are violated by up to 31%.
Approach:	They propose to use out-of-relation knowledge bases to supervise the discovery of unseen relations where relations to discover from the text corpus and those in knowledge bases are not overlapped.
Outcome:	The proposed method improves the state-of-the-art relation discovery performance by a large margin.

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Zixuan Wang, Yu Sun, Hongwei Wang, Baoyu Jing, Xiang Shen, Xin Dong, Zhuolin Hao, Hongyu Xiong, Yang Song

MixTEA: Semi-supervised Entity Alignment with Mixture Teaching (2023.findings-emnlp)

Challenge:	Existing approaches to identifying inappropriate content require extensive human-labeled data and lack cross-issue generalization.
Approach:	They propose a reasoning-enhanced multimodal large language model (MLLM) pretraining paradigm for unified inappropriate content detection.
Outcome:	The proposed model improves the MLLM's performance in both zero-shot and supervised fine-tuning settings and shows strong generalization capabilities to emergent, previously unseen issues.

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Feng Xie, Xin Song, Xiang Zeng, Xuechen Zhao, Lei Tian, Bin Zhou, Yusong Tan

Low-Resource NLP

Challenge:	Existing methods to learn informative entity embeddings are insufficient for semi-supervised entity alignment.
Approach:	They propose a semi-supervised method which guides the model learning with an end-to-end mixture teaching of manually labeled mappings and probabilistic pseudo mappings.
Outcome:	The proposed method is superior to existing methods on benchmark datasets and further analyses.

QADYNAMICS: Training Dynamics-Driven Synthetic QA Diagnostic for Zero-Shot Commonsense Question Answering (2023.findings-emnlp)

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Haochen Shi, Weiqi Wang, Tianqing Fang, Baixuan Xu, Wenxuan Ding, Xin Liu, Yangqiu Song

ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling (2026.acl-long)

Challenge:	Existing approaches to QA fine-tune language models on QA pairs constructed from CommonSense Knowledge Bases (CSKBs) however, current QA synthesis protocols introduce noise from the CSKB and generate ungrammatical questions and false negative options, which impede the model’s ability to generalize.
Approach:	They propose a framework to analyze the training dynamics of each QA pair at both the question level and option level, discarding machine-detectable artifacts and mislabeled or false-negative options.
Outcome:	The proposed framework outperforms baseline approaches while using only 33% of the synthetic data.

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Jianghao Lin, Yuanyuan Shi, Xin Peng, Renjie Ding, Hairui Wang, Yuxuan Peng, Bizhe Bai, Weixi Song, Fengshuo Bai, Huacan Chai, Weinan Zhang, Fei Huang, Ying Wen

Challenge:	Existing research on inference scaling focuses on unstructured output generation tasks, such as mathematical problems.
Approach:	They propose an inference-scaling framework that combines fine-grained beam search with ToolPRM, a process reward model scoring each intra-call decision.
Outcome:	The proposed framework outperforms outcome and coarse-grained reward models in predictive accuracy and yields consistent test-time gains on multiple function-calling benchmarks.

Exploring Discourse Structures for Argument Impact Classification (2021.acl-long)

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Xin Liu, Jiefu Ou, Yangqiu Song, Xin Jiang

Text Classification

Challenge:	Existing studies have shown that discourse structures influence the persuasiveness of arguments.
Approach:	They propose to fuse sentence-level structural discourse information with contextualized features derived from large-scale language models to investigate how discourse relations influence argument impact.
Outcome:	The proposed model improves its backbone RoBERTa around 1.67%, compared with other models, but side effects are brought by other models.

AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph (2024.findings-naacl)

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Zhaowei Wang, Haochen Shi, Weiqi Wang, Tianqing Fang, Hongming Zhang, Sehyun Choi, Xin Liu, Yangqiu Song

NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding (2024.findings-emnlp)

Challenge:	Existing language models only touch nouns or verbs within simplified events or specific domains.
Approach:	They propose an entailment graph that collects abstract knowledge for 3 components of diverse events to comprehensively evaluate the abstraction ability of language models.
Outcome:	The proposed benchmark improves LLMs across two previous abstraction tasks.

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Chunkit Chan, Cheng Jiayang, Yauwai Yim, Zheye Deng, Wei Fan, Haoran Li, Xin Liu, Hongming Zhang, Weiqi Wang, Yangqiu Song

A Chinese Corpus for Fine-grained Entity Typing (2020.lrec-1)

Challenge:	Theory of mind evaluations currently focus on testing models using machine-generated data or game settings prone to shortcuts and spurious correlations.
Approach:	They propose a benchmark to stress-test machine ToM in real-world negotiation surrounding covered multi-dimensional mental states.
Outcome:	The proposed benchmark builds upon the Belief-Desire-Intention theory and conducts the necessary empirical experiments to evaluate large language models.

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Chin Lee, Hongliang Dai, Yangqiu Song, Xin Li

Information Extraction & Text Mining

Challenge:	Existing datasets for fine-grained entity typing are limited to English . a corpus of 4,800 mentions is manually labeled with free-form entity types .
Approach:	They propose a Chinese fine-grained entity typing task that uses crowdsourcing . they categorize each mention into 10 general types and use a large tag set to predict open set of types .
Outcome:	The proposed dataset contains 4,800 mentions manually labeled in Chinese . it also categorizes all the fine-grained types into 10 general types .

Data-Centric Explainable Debiasing for Improving Fairness in Pre-trained Language Models (2024.findings-acl)

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Yingji Li, Mengnan Du, Rui Song, Xin Wang, Ying Wang

PLAN-TUNING: Post-Training Language Models to Learn Step-by-Step Planning for Complex Problem Solving (2025.emnlp-main)

Challenge:	Existing data-centric debiasing strategies mainly leverage explicit bias words for counterfactual data augmentation to balance the training data.
Approach:	They propose a method which uses an explainability method to search for implicit bias words to assist in debiasing PLMs.
Outcome:	Extensive results show that the proposed method achieves state-of-the-art debiasing performance and strong generalization while maintaining predictive abilities.

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Mihir Parmar, Palash Goyal, Xin Liu, Yiwen Song, Mingyang Ling, Chitta Baral, Hamid Palangi, Tomas Pfister

Extracting and Combining Abilities For Building Multi-lingual Ability-enhanced Large Language Models (2025.emnlp-main)

Challenge:	Recent studies have shown that decomposing complex problems into simple subtasks has significantly boosted the performance of large language models (LLMs).
Approach:	They propose a unified post-training framework that distills synthetic task decompositions and fine-tunes smaller LLMs via supervised and reinforcement-learning objectives to improve complex reasoning.
Outcome:	The proposed framework outperforms strong baselines on GSM8k and MATH benchmarks and shows that it can improve generalization capabilities on out-of-domain datasets.

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Zhipeng Chen, Kun Zhou, Liang Song, Xin Zhao, Bingning Wang, Weipeng Chen, Ji-Rong Wen

YuLan-Mini: Pushing the Limits of Open Data-efficient Language Model (2025.acl-long)

Challenge:	Existing work relies on training with multi-lingual ability-related data, which may not be available for low-resource languages.
Approach:	They propose a multi-lingual ability-enhanced LLM that extracts language-agnostic ability-related weights from LLMs and combine them across different languages by simple addition and subtraction operations without training.
Outcome:	The proposed approach extracts language-agnostic ability-related weights from LLMs and combine them across different languages without training.

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Hu Yiwen, Huatong Song, Jie Chen, Jia Deng, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Zican Dong, Yang Lu, Xu Miao, Xin Zhao, Ji-Rong Wen

DeepPlanner: Scaling Planning Capability for Deep Research Agents via Advantage Shaping (2026.findings-acl)

Challenge:	prevailing pre-training approaches for large language models involve several complexities.
Approach:	They propose a low-cost training recipe and a robust optimization approach to mitigate training instability . they also propose synthesis, curriculum, and data selection pipelines to integrate data .
Outcome:	The proposed model achieves top-tier performance among models with similar parameter scale . it is comparable to industry-leading models that require significantly more data .

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Wei Fan, Wenlin Yao, Zheng Li, Feng Yao, Xin Liu, Liang Qiu, Qingyu Yin, Yangqiu Song, Bing Yin

Scale-VAE: Preventing Posterior Collapse in Variational Autoencoder (2024.lrec-main)

Challenge:	Existing approaches to planning involve implicit planning or introduce explicit planners without systematically optimizing the planning stage.
Approach:	They propose an end-to-end RL framework that enhances the planning capabilities of deep research agents.
Outcome:	Experiments show that DeepPlanner improves planning quality and achieves state-of-the-art results under a lower training budget.

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Tianbao Song, Jingbo Sun, Xin Liu, Weiming Peng

AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)

Challenge:	Variational autoencoder (VAE) is a widely used generative model . but when employing strong autoregressive generation networks, VAE tends to converge to a degenerate local optimum known as posterior collapse.
Approach:	They propose a model called Scale-VAE to solve a posterior collapse problem . they use a factor to keep the posterior dimension discriminative across data instances .
Outcome:	The proposed model outperforms state-of-the-art models in density estimation and representation learning.

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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song

Smart-Searcher: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning (2025.findings-emnlp)

Challenge:	Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage.
Approach:	They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas.
Outcome:	The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality.

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Huatong Song, Jinhao Jiang, Wenqing Tian, Zhipeng Chen, Yuhuan Wu, Jiahao Zhao, Yingqian Min, Xin Zhao, Lei Fang, Ji-Rong Wen

IPS: In-Prompt Process Supervision for Short Video Content Moderation (2026.acl-industry)

Challenge:	Large Language Models (LLMs) are powerful but prone to hallucinations due to static knowledge. Retrieval-augmented generation (RAG) helps by injecting external information, but current methods are costly, generalize poorly, or ignore the model’s internal knowledge.
Approach:	They propose a framework to train large language models to leverage both internal and external knowledge sources.
Outcome:	The proposed framework outperforms existing methods and achieves efficient retrieval-augmented reasoning.

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Mingchao Liu, Yu Sun, Ruixiao Sun, Xin Dong, Xiang Shen, Hongwei Wang, Hongyu Xiong, Yang Song

Getting the Most out of Simile Recognition (2022.findings-emnlp)

Challenge:	Multimodal large language models (MLLMs) capture semantics of short video content but fail to account for policy-specific details.
Approach:	They propose a framework that integrates In-prompt Process Supervision into MLLMs . they propose sequential reasoning over ancillary questions during fine-tuning .
Outcome:	IPS outperforms baseline MLLMs on public and proprietary benchmarks . replacing human-annotated ancillary labels with MLML-generated ones results in performance degradation.

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Xiaoyue Wang, Linfeng Song, Xin Liu, Chulun Zhou, Hualin Zeng, Jinsong Su

Challenge:	Recent work ignores features other than surface strings and suffers from data hunger issue.
Approach:	They propose to use simile sentence classification and simile component extraction to find simile components.
Outcome:	The proposed model outperforms current state-of-the-art systems and baselines.

Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)

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Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen

Improving Fine-grained Entity Typing with Entity Linking (D19-1)

Challenge:	Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks.
Approach:	They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs.
Outcome:	The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs.

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Hongliang Dai, Donghong Du, Xin Li, Yangqiu Song

Knowledge Representation

Challenge:	Existing methods for fine-grained entity typing require a large tag set and knowledge of the context.
Approach:	They propose a deep neural model that uses context and information from entity linking to improve fine-grained entity typing.
Outcome:	The proposed model achieves 5% absolute strict accuracy improvement over the state of the art on two datasets.

Step Feasibility-Aware and Error-Correctable Entailment Tree Generation (2024.lrec-main)

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Junyue Song, Xin Wu, Yi Cai

AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation (2024.acl-long)

Challenge:	Existing methods for generating entailment trees suffer from false feasible steps, resulting in error propagation.
Approach:	They propose an iterative entailment tree generation framework with step feasibility perception and state error handling mechanisms to enhance the interpretability of QA systems.
Outcome:	The proposed framework improves the interpretation of QA systems by demonstrating that it is feasible to choose steps that are false feasible and error propagating.

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Zhaowei Wang, Wei Fan, Qing Zong, Hongming Zhang, Sehyun Choi, Tianqing Fang, Xin Liu, Yangqiu Song, Ginny Wong, Simon See

Explainability & Interpretability in NLP

Challenge:	Existing work shows that LLMs are deficient in abstract ability, and how to improve it remains unexplored.
Approach:	They propose a framework AbsInstruct to enhance LLMs’ abstract ability through instruction tuning.
Outcome:	The proposed framework can enhance LLMs’ abstraction ability with strong generalization performance while maintaining their general instruction-following abilities.

JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs (2021.findings-acl)

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Pei Ke, Haozhe Ji, Yu Ran, Xin Cui, Liwei Wang, Linfeng Song, Xiaoyan Zhu, Minlie Huang

Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform (2022.emnlp-main)

Challenge:	Existing pre-trained models for knowledgegraph-to-text generation ignore graph structure during encoding and lack elaborate pre-training tasks to explicitly model graph-text alignments.
Approach:	They propose a graph-text joint representation learning model called JointGT which incorporates a structure-aware semantic aggregation module into each Transformer layer to preserve the graph structure.
Outcome:	The proposed model achieves state-of-the-art performance on various KG-to-text datasets.

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Huiru Xiao, Xin Liu, Yangqiu Song, Ginny Wong, Simon See

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

Challenge:	Existing studies have shown that the choice of space for knowledge graph (KG) embeddings has significant effects on the performance of KG completion tasks.
Approach:	They propose to use the Fourier transform to convert between real and complex hyperbolic space to capture hierarchical patterns.
Outcome:	The proposed models outperform the baseline models for knowledge graph (KG) embeddings.

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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

Advancing Abductive Reasoning in Knowledge Graphs through Complex Logical Hypothesis Generation (2024.acl-long)

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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Jiaxin Bai, Yicheng Wang, Tianshi Zheng, Yue Guo, Xin Liu, Yangqiu Song

Multimodality

Challenge:	Abductive reasoning is the process of making educated guesses to provide explanations for observations.
Approach:	They propose a task of complex logical hypothesis generation to generate a complex logique hypothesis that can explain a set of observations.
Outcome:	The proposed model generates logical hypotheses closer to the reference hypothesis, but not better on unseen observations.

Battling against Tough Resister: Strategy Planning with Adversarial Game for Non-collaborative Dialogues (2025.acl-long)

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Haiyang Wang, Zhiliang Tian, Yuchen Pan, Xin Song, Xin Niu, Minlie Huang, Bin Zhou

EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs (2024.lrec-main)

Challenge:	Non-collaborative dialogue involves two participants with conflicting interests engaging in multiround dialogue to achieve their own goals.
Approach:	They propose a Game-based Adversarial self-play InterActive training paradigm which constructs an adversarial two-player (a persuader and a resister) zero-sum game and guides the game to approximate Nash Equilibrium (NE) via reinforcement learning.
Outcome:	The proposed model achieves state-of-the-art performance on three datasets.

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Cheng Jiayang, Lin Qiu, Chunkit Chan, Xin Liu, Yangqiu Song, Zheng Zhang

InteGround: On the Evaluation of Verification and Retrieval Planning in Integrative Grounding (2025.findings-emnlp)

Challenge:	Existing frameworks for leveraging background knowledge of narratives are limited.
Approach:	They propose a framework to ground free-texts to eventuality-centric KGs for narrative reasoning . their framework is based on a set of probabilistic probabilistic models that are grounded in the real world .
Outcome:	The proposed framework outperforms baseline models while providing interpretable evidence.

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Cheng Jiayang, Qianqian Zhuang, Haoran Li, Chunkit Chan, Xin Liu, Lin Qiu, Yangqiu Song

Information Retrieval

Challenge:	Existing grounding approaches work well for simple queries, but many real-world information needs require synthesizing multiple pieces of evidence.
Approach:	They introduce "integrative grounding" to evaluate the ability to ground large language models in external knowledge sources.
Outcome:	The proposed approach is robust to redundant evidence, but rationalizes using internal knowledge when information is incomplete.

MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding (2024.emnlp-main)

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Baixuan Xu, Weiqi Wang, Haochen Shi, Wenxuan Ding, Huihao Jing, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Long Chen, Yangqiu Song

Green & Sustainable NLP

Challenge:	Existing methods for acquiring large-scale intentions generate product-centric intentions without product images and incur high costs for scalability.
Approach:	They propose a multimodal framework that allows Large Vision-Language Models to infer purchase intentions from multimodal product metadata and prioritize human-centric ones.
Outcome:	The proposed framework shows that it is robust to different prompts and superior to previous methods.

ValueBench: Towards Comprehensively Evaluating Value Orientations and Understanding of Large Language Models (2024.acl-long)

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Yuanyi Ren, Haoran Ye, Hanjun Fang, Xin Zhang, Guojie Song

Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection (2023.findings-emnlp)

Challenge:	Large Language Models (LLMs) are transforming diverse fields and gaining increasing influence as human proxies.
Approach:	They propose a psychometric evaluation pipeline grounded in realistic human-AI interactions to probe value orientations and novel tasks for evaluating value understanding in an open-ended value space.
Outcome:	The proposed evaluation pipeline is grounded in realistic human-AI interactions and performs tasks that approximate expert conclusions in value-related extraction and generation tasks.

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Zheye Deng, Weiqi Wang, Zhaowei Wang, Xin Liu, Yangqiu Song

Instantly Learning Preference Alignment via In-context DPO (2025.naacl-long)

Challenge:	Existing methods to construct CSKGs with large semantic coverage are expensive and introduce spurious noise.
Approach:	They propose a denoising framework that incorporates entity semantic information, global rules, and local structural information from the CSKG.
Outcome:	The proposed framework outperforms baseline methods in noise detection tasks on synthetic noisy CSKG benchmarks.

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Feifan Song, Yuxuan Fan, Xin Zhang, Peiyi Wang, Houfeng Wang

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

Challenge:	Presently, mainstream approaches to HPA heavily depend on fine-tuning . however, the huge computational and annotation costs of fine-timing are hard to ignore .
Approach:	They propose a tuning-free approach to HPA using LLMs' decoding . they first rethink the derivation procedures of DPO and build an instant scorer .
Outcome:	The proposed approach outperforms existing methods even with tuning-free baselines and an upgraded scorer.

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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

Context-Value-Action Architecture for Value-Driven Large Language Model Agents (2026.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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TianZe Zhang, Sirui Sun, Yuhang Xie, Xin Zhang, Zhiqiang Wu, Guojie Song

CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering (2023.findings-emnlp)

Challenge:	Existing LLMs exhibit behavioral rigidity, a flaw often masked by the self-referential bias of current "LLM-as-a-judge" evaluations.
Approach:	They propose a Context-Value-Action architecture that decouples action generation from cognitive reasoning via a Value Verifier trained on authentic human data to explicitly model dynamic value activation.
Outcome:	The proposed architecture significantly outperforms baseline models on 1.1 million real-world interaction traces on CVABench.

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Weiqi Wang, Tianqing Fang, Wenxuan Ding, Baixuan Xu, Xin Liu, Yangqiu Song, Antoine Bosselut

CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning (2024.acl-long)

Challenge:	Existing approaches to zero-shot commonsense question answering use incomplete CSKBs . lack of human annotations makes sampled negative examples potentially uninformative and contradictory.
Approach:	They propose a framework that abstracts a commonsense knowledge triple to many higher-level instances, which increases the coverage of the CSKB and expands the ground-truth answer space.
Outcome:	Experiments show that CAR can generalize to zero-shot commonsense scenarios . lack of human annotations makes sampled negative examples potentially uninformative and contradictory.

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Weiqi Wang, Tianqing Fang, Chunyang Li, Haochen Shi, Wenxuan Ding, Baixuan Xu, Zhaowei Wang, Jiaxin Bai, Xin Liu, Cheng Jiayang, Chunkit Chan, Yangqiu Song

Exploring the Potential of ChatGPT on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations (2024.findings-eacl)

Challenge:	Existing approaches to generalize commonsense reasoning lack instantiated knowledge and require pre-built concept taxonomies and annotations.
Approach:	They propose a framework that iteratively performs contextualized conceptualization and instantiation over commonsense knowledge bases by instructing large language models to generate both types of knowledge with critic filtering.
Outcome:	Empirical results show that distilling CANDLE on student models provides benefits across three downstream tasks.

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Chunkit Chan, Cheng Jiayang, Weiqi Wang, Yuxin Jiang, Tianqing Fang, Xin Liu, Yangqiu Song

SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis (2025.findings-emnlp)

Challenge:	Recent studies have demonstrated ChatGPT's remarkable few-shot, even zero-shot learning abilities when compared to other models.
Approach:	They quantitatively evaluate the performance of ChatGPT on inter-sentential relations such as temporal relations, causal relations, and discourse relations.
Outcome:	The proposed model performs well on temporal relations, causal relations, and discourse relations.

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Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren, Jinhao Jiang, Junjie Zhang, Fei Bai, Jia Deng, Xin Zhao, Zheng Liu, Lei Fang, Zhongyuan Wang, Ji-Rong Wen

Intention Knowledge Graph Construction for User Intention Relation Modeling (2026.eacl-long)

Challenge:	Existing approaches to deep search training lack high-quality training trajectories, prohibitive computational costs and lack of high-fidelity training data.
Approach:	They propose a framework that synthesizes high-quality training data by simulating real user interactions in live web search environments.
Outcome:	The proposed framework synthesizes high-quality training data by simulating user interactions in live web search environments.

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Jiaxin Bai, Zhaobo Wang, Junfei Cheng, Dan Yu, Zerui Huang, Weiqi Wang, Xin Liu, Chen Luo, Yanming Zhu, Bo Li, Yangqiu Song

Multimodality

Challenge:	Existing knowledge graphs focus on connecting intentions but lacks the ability to model the relationships between different intentions.
Approach:	They propose a framework to automatically generate an intention knowledge graph, capturing connections between user intentions.
Outcome:	The proposed model outperforms state-of-the-art methods and shows its utility.

Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type Information (2022.coling-1)

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Xuhui Sui, Ying Zhang, Kehui Song, Baohang Zhou, Guoqing Zhao, Xin Wei, Xiaojie Yuan

DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition (2023.findings-acl)

Challenge:	Entity linking is a task of assigning entity mentions to referent entities in a knowledge base.
Approach:	They propose to use ultra-fine-grained type information to improve the generalization ability of EL models by utilizing a low-level task to extract ultra-finish entity type information.
Outcome:	The proposed model achieves state-of-the-art in the zero-shot entity linking task .

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Chunkit Chan, Xin Liu, Jiayang Cheng, Zihan Li, Yangqiu Song, Ginny Wong, Simon See

SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding (2026.findings-acl)

Challenge:	Existing works on implicit discourse relation recognition focus on syntax features and lack of connectives.
Approach:	They propose a prompt-based path prediction method that integrates the interactive information and intrinsic senses among the hierarchy in IDRR.
Outcome:	The proposed method shows significant improvement against competitive baselines.

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Yuqi Yang, Weiqi Wang, Baixuan Xu, Wei Fan, Qing Zong, Chunkit Chan, Zheye Deng, Xin Liu, Yifan Gao, Changlong Yu, Chen Luo, Yang Li, Zheng Li, Qingyu Yin, Bing Yin, Yangqiu Song

FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery (2023.findings-acl)

Challenge:	Existing models fail to capture and model customer intention effectively because of insufficient information exploitation and only apparent information like descriptions and titles are used.
Approach:	They propose to exploit existing session data to capture and model intention in E-commerce product purchase sessions using a multimodal benchmark.
Outcome:	The proposed framework can bridge the gap between intention understanding in simplified research cases like co-buy intention and more complex yet practical scenarios like session history.

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Changlong Yu, Weiqi Wang, Xin Liu, Jiaxin Bai, Yangqiu Song, Zheng Li, Yifan Gao, Tianyu Cao, Bing Yin

Commonsense Reasoning

Challenge:	Existing intention-based studies on recommendation tasks are limited and use models to implicitly model the intention memberships.
Approach:	They propose a framework that leverages the generation power of large language models and human-in-the-loop annotation to semi-automatically construct the intention knowledge graph.
Outcome:	The proposed framework can model e-commerce knowledge and have many potential applications.

Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG (2025.acl-long)

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Xin Sun, Jianan Xie, Zhongqi Chen, Qiang Liu, Shu Wu, Yuehe Chen, Bowen Song, Zilei Wang, Weiqiang Wang, Liang Wang

Information Retrieval

Challenge:	Large language models (LLMs) augmented with retrieval systems have significantly advanced natural language processing tasks by integrating external knowledge sources.
Approach:	They propose a method that conditions large language models to generate answers even in the absence of reliable knowledge.
Outcome:	The proposed approach balances accuracy with appropriate abstention, enhancing the reliability and trustworthiness of retrieval-augmented systems.

PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models (2024.acl-long)

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Haoran Li, Dadi Guo, Donghao Li, Wei Fan, Qi Hu, Xin Liu, Chunkit Chan, Duanyi Yao, Yuan Yao, Yangqiu Song

A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification (N19-1)

Challenge:	generative large language models (LLMs) exhibit surprising capability and integrate previous tasks into a unified text generation formulation.
Approach:	They propose a privacy evaluation benchmark to quantify the privacy leakage of language models.
Outcome:	The proposed benchmark compares PPLMs with different privacy implementations to find out how privacy leakage is handled.

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Ziqian Zeng, Wenxuan Zhou, Xin Liu, Yangqiu Song

Sentiment Analysis

Challenge:	Existing weakly supervised methods for document-level multi-aspect sentiment classification are not easy to obtain.
Approach:	They propose a variational approach to weakly supervised document-level multi-aspect sentiment classification using target-opinion word pairs as "supervision" they aim to learn a sentiment polarity classifier by optimizing the lower bound .
Outcome:	The proposed method outperforms weakly supervised baselines on TripAdvisor and BeerAdvocate datasets and can be comparable to state-of-the-art supervised methods with hundreds of labels per aspect.

ContextCheck: Sentence-Level Faithfulness Verification with Context-Aware Disambiguation (2026.findings-acl)

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Yueqin Yin, Yaxi Li, Xin Liu, Xun Wang, Kaiqiang Song, Simin Ma, Shujian Liu, Sathish Reddy Indurthi, Haoyun Deng, Pengcheng He, Mingyuan Zhou, Song Wang

Challenge:	Large language models often hallucinate, producing content that is factually incorrect or not grounded in the sources.
Approach:	They propose a framework for sentence-level faithfulness verification with context-aware disambiguation.
Outcome:	The proposed framework improves Macro F1 by over 10 points compared to baselines on three context-dependent datasets.

Parrot: Enhancing Multi-Turn Instruction Following for Large Language Models (2024.acl-long)

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Yuchong Sun, Che Liu, Kun Zhou, Jinwen Huang, Ruihua Song, Xin Zhao, Fuzheng Zhang, Di Zhang, Kun Gai