Papers by Jiaxin Liu

Prophecy Distillation for Boosting Abstractive Summarization (2024.lrec-main)

Challenge:	Large Language Models (LLMs) are increasingly integrated into our daily lives, raising ethical concerns, especially about perpetuating stereotypes.
Approach:	They propose a method that incorporates a neutral word semantics-based loss function to alleviate the deterioration of the LMS during debiasing.
Outcome:	The proposed method alleviates the deterioration of the Language Modeling Score (LMS) by incorporating a neutral word semantics-based loss function.

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Jiaxin Duan, Fengyu Lu, Junfei Liu

Summarization

Challenge:	Abstractive summarization models with maximum likelihood estimation generate unfaithful facts alongside ambiguous focus.
Approach:	They propose a framework which learns a regular summarization model to mimic the behavior of being guided by prophecy for boosting abstractive summaries.
Outcome:	The proposed model achieves new or matched state-of-the-art on four well-known datasets.

G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks (2022.emnlp-main)

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Zhongwei Wan, Yichun Yin, Wei Zhang, Jiaxin Shi, Lifeng Shang, Guangyong Chen, Xin Jiang, Qun Liu

On LLM-Based Scientific Inductive Reasoning Beyond Equations (2025.emnlp-main)

Challenge:	Existing domain-adaptive pre-training (DAPT) models tend to forget the general knowledge acquired by general PLMs, leading to catastrophic forgetting and sub-optimal performance.
Approach:	They propose a framework which augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge.
Outcome:	The proposed framework augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge.

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Brian S. Lin, Jiaxin Yuan, Zihan Zhou, Shouli Wang, Shuo Wang, Cunliang Kong, Qi Shi, Yuxuan Li, Liner Yang, Zhiyuan Liu, Maosong Sun

Reasoning

Challenge:	Existing research on inductive reasoning models emphasizes rule design without grounding them in specific scenarios.
Approach:	They propose to use LLMs to learn underlying patterns from limited examples in entirely new environments.
Outcome:	The proposed benchmark evaluates the inductive reasoning abilities of large language models in scientific settings.

Learning from Mistakes: Iterative Prompt Relabeling for Text-to-Image Diffusion Model Training (2024.findings-emnlp)

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Xinyan Chen, Jiaxin Ge, Tianjun Zhang, Jiaming Liu, Shanghang Zhang

Evaluating and Aligning Human Economic Risk Preferences in LLMs (2025.emnlp-main)

Challenge:	Recent advances in diffusion models have shown impressive performance in many domains, but their ability to follow instructions is still unsatisfactory.
Approach:	They propose an algorithm that aligns images to text through iterative image sampling and prompt relabeling with feedback.
Outcome:	The proposed algorithm improves on the spatial relation VISOR benchmark by 15.22% compared to previous methods.

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Jiaxin Liu, Yixuan Tang, Yi Yang, Kar Yan Tam

Program Transfer for Answering Complex Questions over Knowledge Bases (2022.acl-long)

Challenge:	Large Language Models (LLMs) are increasingly used in decision-making scenarios that involve risk assessment, yet their alignment with human economic rationality remains unclear.
Approach:	They propose an evaluation metric called Risk Disparity Score (RDS) and assess whether LLM-generated responses reflect appropriate levels of risk aversion or risk-seeking behavior based on individual’s persona.
Outcome:	The proposed evaluation metric assesses whether LLM-generated responses reflect appropriate levels of risk aversion or risk-seeking behavior based on individual’s persona.

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Shulin Cao, Jiaxin Shi, Zijun Yao, Xin Lv, Jifan Yu, Lei Hou, Juanzi Li, Zhiyuan Liu, Jinghui Xiao

Knowledge Representation

Challenge:	Program induction for complex questions over knowledge bases relies on a large number of parallel question-program pairs for the given KB, but the gold program annotations are usually lacking, making learning difficult.
Approach:	They propose an approach to leverage program annotations on rich KBs as external supervision signals to aid program induction for low-resourced KB.
Outcome:	The proposed approach outperforms SOTA methods on ComplexWebQuestions and WebQuestionSP.

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

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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

Reasoning

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 .

LLMs Trust Humans More, That’s a Problem! Unveiling and Mitigating the Authority Bias in Retrieval-Augmented Generation (2025.acl-long)

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Yuxuan Li, Xinwei Guo, Jiashi Gao, Guanhua Chen, Xiangyu Zhao, Jiaxin Zhang, Quanying Liu, Haiyan Wu, Xin Yao, Xuetao Wei

Information Retrieval

Challenge:	Large language models (LLMs) generate outputs that stray from user input or contravene established knowledge.
Approach:	They propose a new phenomenon, Authority Bias, where LLMs favor one knowledge source over the other . they propose atomic information that generates conflicts and a Conflict Detection Enhanced Query framework .
Outcome:	The proposed framework reduces Authority bias in large language models . it detects conflicts, performs credibility assessment on conflicting paragraphs, and detects perturbed text .

Alleviating Exposure Bias in Abstractive Summarization via Sequentially Generating and Revising (2024.lrec-main)

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Jiaxin Duan, Fengyu Lu, Junfei Liu

Text Generation

Challenge:	Existing approaches to abstractive summarization suffer from exposure bias . Existing solutions bridge this gap through un- or semi-supervised holistic learning .
Approach:	They propose to reformat abstractive summarization to sequential generation and revision (SeGRe) this allows the model to assess the flawed summary from a global perspective and modify inappropriate expressions.
Outcome:	The proposed model can assess the flawed summary from a global view and modify inappropriate expressions.

Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs (2026.acl-long)

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Yihua Zhu, Qianying Liu, Jiaxin Wang, Fei Cheng, Chaoran Liu, Akiko Aizawa, Sadao Kurohashi, Hidetoshi Shimodaira

Explainability & Interpretability in NLP

Challenge:	Autoregressive LLMs perform well on relational tasks that require linking entities via relational words, but it is unclear whether they learn the logical semantics of such relations or whether left-to-right order bias is involved.
Approach:	They propose a framework that generates text from symmetric/inverse triples and trains autoregressive models from scratch.
Outcome:	The proposed framework generates text from symmetric/inverse triples, trains autoregressive models from scratch, and evaluates memorization, logical inference, and in-context generalization to unseen entities.

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

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

M³GQA: A Multi-Entity Multi-Hop Multi-Setting Graph Question Answering Benchmark (2025.acl-long)

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

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Boci Peng, Yongchao Liu, Xiaohe Bo, Jiaxin Guo, Yun Zhu, Xuanbo Fan, Chuntao Hong, Yan Zhang

Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey (2025.findings-naacl)

Challenge:	GraphRAG systems have achieved remarkable progress in enhancing performance and reliability of large language models.
Approach:	They propose a GraphRAG benchmark focusing on multi-entity queries with six settings for comprehensive evaluation.
Outcome:	The proposed method can construct diverse data with semantically correct ground-truth reasoning paths.

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Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang

Can LLMs Hear the Dogwhistle? (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have shown great potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability.
Approach:	They evaluate or improve generative Large Language Models from a causal perspective in areas such as reasoning capacity, fairness and safety issues, explainability, and handling multimodality.
Outcome:	The proposed models can be used to perform causal relationship discovery and causal effect estimation tasks.

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Yifan Liu, Yi Lin, Xinwei Guo, Ziwei Wang, Jiaxin Zhang, Guanhua Chen, Haiyan Wu, Xiangyu Zhao, Xin Yao, Xuetao Wei

CMMaTH: A Chinese Multi-modal Math Skill Evaluation Benchmark for Foundation Models (2025.coling-main)

Challenge:	Existing safety benchmarks focus on explicitly harmful content, but ignore context-dependent expressions such as dogwhistles.
Approach:	They propose a benchmark for evaluating LLM safety under dogwhistle-driven prompts . their findings expose a blind spot in current safety evaluation practices .
Outcome:	The proposed benchmark compared safety performance with toxic terms using dogwhistle-driven prompts.

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Zhongzhi Li, Ming-Liang Zhang, Pei-Jie Wang, Jian Xu, Rui-Song Zhang, Yin Fei, Zhi-Long Ji, Jin-Feng Bai, Zhen-Ru Pan, Jiaxin Zhang, Cheng-Lin Liu

Numerical Reasoning

Challenge:	Large language models excel in various language tasks, while large multimodal models effectively handle visual-language problems.
Approach:	They propose to use a multimodal multimodal model evaluation benchmark to evaluate model performance in Chinese K12 classrooms.
Outcome:	The proposed model evaluation tool is integrated with the CMMaTH dataset.

How Far are We from Robust Long Abstractive Summarization? (2022.emnlp-main)

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Huan Yee Koh, Jiaxin Ju, He Zhang, Ming Liu, Shirui Pan

Robustness in NLP

Challenge:	Abstractive summarization has made tremendous progress in recent years . however, even under a short document setting, abstractive models often generate summaries that are repetitive, ungrammatical, and factually inconsistent with the source.
Approach:	They perform fine-grained human annotations to evaluate long document abstractive summarization systems and develop factual consistency metrics.
Outcome:	The proposed model can generate more relevant summaries but not factual ones.

How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering (2024.findings-acl)

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Jinxin Liu, Shulin Cao, Jiaxin Shi, Tingjian Zhang, Lunyiu Nie, Linmei Hu, Lei Hou, Juanzi Li

GeoAgent: To Empower LLMs using Geospatial Tools for Address Standardization (2024.findings-acl)

Challenge:	Recent studies have validated that large language models (LLMs) are capable of solving some KBQA problems, but there has been little discussion on the differences in LLMs’ proficiency in formal languages used in semantic parsing.
Approach:	They propose to evaluate the understanding and generation ability of large language models (LLMs) to deal with differently structured logical forms by examining the inter-conversion of natural and formal language through in-context learning of LLMs.
Outcome:	The proposed model can understand formal languages as well as humans, but generating correct logical forms remains a challenge.

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Chenghua Huang, Shisong Chen, Zhixu Li, Jianfeng Qu, Yanghua Xiao, Jiaxin Liu, Zhigang Chen

Leveraging Information Bottleneck for Scientific Document Summarization (2021.findings-emnlp)

Challenge:	Existing approaches to address address standardization are lacking in the current field.
Approach:	They propose a framework that incorporates spatial knowledge into address texts and achieves efficient address standardization.
Outcome:	The proposed framework incorporates spatial knowledge into address texts and achieves efficient address standardization.

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Jiaxin Ju, Ming Liu, Huan Yee Koh, Yuan Jin, Lan Du, Shirui Pan

Summarization

Challenge:	Existing methods to extract salient sentences from document are unsupervised and rely on graph-based methods for sentence ranking.
Approach:	They propose an unsupervised extractive approach to document level summarization based on the Information Bottleneck principle.
Outcome:	The proposed framework can be extended to a multi-view framework by different signals.

AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction (2026.acl-long)

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Hong Ting Tsang, Jiaxin Bai, Haoyu Huang, Qiao Xiao, Tianshi Zheng, Baixuan Xu, Shujie Liu, Yangqiu Song

Structured Data in NLP

Challenge:	Currently, knowledge graphs are decoupled from their downstream application, resulting in suboptimal graph structures.
Approach:	They propose a framework to directly optimize KG construction for task performance using Reinforcement Learning (RL).
Outcome:	The proposed framework improves performance across multiple QA benchmarks and consistently achieves significant performance gains over task-agnostic baseline graphs.

When Phrases Meet Probabilities: Enabling Open Relation Extraction with Cooperating Large Language Models (2024.acl-long)

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Jiaxin Wang, Lingling Zhang, Wee Sun Lee, Yujie Zhong, Liwei Kang, Jun Liu

MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization (2020.acl-main)

Challenge:	Existing clustering-based open relation extraction methods use pre-trained language models . embeddings from language models are high-dimensional and anisotropic, so there is a gap .
Approach:	They propose a framework that makes two LLMs work collaboratively to achieve clustering.
Outcome:	The proposed framework outperforms existing methods by 1.4%3.13% on different datasets.

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Canwen Xu, Jiaxin Pei, Hongtao Wu, Yiyu Liu, Chenliang Li

Information Extraction & Text Mining

Challenge:	Existing datasets labeled for one task hinder multi-task learning . task-specific data make models learn task-related leakage features rather than meaningful knowledge that could generalize to other tasks.
Approach:	They propose to jointly label large-scale NLP dataset MATINF . it contains 1.07 million question-answer pairs with human-labeled categories .
Outcome:	The proposed dataset is applicable for classification, question answering, and summarization.

Holistic Evaluation for Interleaved Text-and-Image Generation (2024.emnlp-main)

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Minqian Liu, Zhiyang Xu, Zihao Lin, Trevor Ashby, Joy Rimchala, Jiaxin Zhang, Lifu Huang

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

Challenge:	Existing evaluation benchmarks do not support arbitrarily interleaved images and text for both inputs and outputs.
Approach:	They propose to use a benchmark to evaluate interleaved text-and-image generation . they define five evaluation aspects for InterleavatedEval, a reference-free metric .
Outcome:	The proposed benchmarks cover a limited number of domains and use cases and lack comparableity-based metrics.

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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

Robustness Testing of Language Understanding in Task-Oriented Dialog (2021.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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Jiexi Liu, Ryuichi Takanobu, Jiaxin Wen, Dazhen Wan, Hongguang Li, Weiran Nie, Cheng Li, Wei Peng, Minlie Huang

Robustness in NLP

Challenge:	a lack of systematic studies on the robustness of language understanding models in task-oriented dialog systems is limiting . authors propose a model-agnostic toolkit LAUG to approximate natural language perturbations .
Approach:	They propose a model-agnostic toolkit LAUG to approximate natural language perturbations for testing the robustness of language understanding models in task-oriented dialog systems.
Outcome:	The proposed toolkit reveals critical robustness issues in state-of-the-art models.

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

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

GeoEval: Benchmark for Evaluating LLMs and Multi-Modal Models on Geometry Problem-Solving (2024.findings-acl)

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.

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Jiaxin Zhang, Zhong-Zhi Li, Ming-Liang Zhang, Fei Yin, Cheng-Lin Liu, Yashar Moshfeghi

Numerical Reasoning

Challenge:	Recent advances in large language models (LLMs) and multi-modal models (MMs) have demonstrated remarkable capabilities in problem-solving, but their proficiency in tackling geometry math problems has not been thoroughly evaluated.
Approach:	They propose a benchmark to evaluate the performance of large language models and multi-modal models in solving geometry math problems.
Outcome:	The proposed model achieves 55.67% accuracy on main subset but only 6.00% accuracy on hard subset.

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.

Beyond Surface Similarity: Detecting Subtle Semantic Shifts in Financial Narratives (2024.findings-naacl)

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Jiaxin Liu, Yi Yang, Kar Yan Tam

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

Challenge:	Existing pretrained embeddings and LLM embeddables fail to discern subtle financial narrative shifts, resulting in a lack of insight for investors and regulators.
Approach:	They propose a financial domain-specific NLP task to measure nuanced semantic similarity between pairs of financial narratives.
Outcome:	The proposed method outperforms existing methods trained on classic STS tasks and generic LLM embeddings on a human-annotated dataset.

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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

The GaoYao Benchmark: A Comprehensive Framework for Evaluating Multilingual and Multicultural Abilities of Large Language Models (2026.acl-long)

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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Yilun Liu, Chunguang Zhao, Mengyao Piao, Lingqi Miao, Shimin Tao, Minggui HE, Chenxin Liu, Zhang Li, null Mahongxia, Jiaxin Guo, Chen Liu, Liqun Deng, Jiansheng Wei, Xiaojun Meng, Fanyi Du, Daimeng Wei, Yanghua Xiao

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

Challenge:	Existing multilingual evaluation benchmarks neglect cultural nuances and lack language coverage in subjective tasks.
Approach:	They propose a framework that categorizes evaluation tasks into three cultural layers and nine cognitive sub-layers.
Outcome:	The proposed framework surpasses prior coverage by up to 111% on 20+ LLMs.

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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

MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering (2022.emnlp-main)

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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Jiaxin Wang, Lingling Zhang, Jun Liu, Xi Liang, Yujie Zhong, Yaqiang Wu

Information Extraction & Text Mining

Challenge:	Existing methods for open relation extraction (OpenRE) focus on labeled and pre-defined instances, which are costly to acquire in reality.
Approach:	They propose a framework that can extract relations without pre-defined types from open-domain corpus with efficient knowledge transfer from a few pre-determined relational instances.
Outcome:	The proposed framework achieves the new SOTA results for OpenRE on different datasets.

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

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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

DIXITWORLD: Evaluating Multimodal Abductive Reasoning in Vision-Language Models with Multi-Agent Dixit Gameplay (2026.acl-short)

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.

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Yunxiang MO, Tianshi Zheng, Qing Zong, Jiayu Liu, Baixuan Xu, Yauwai Yim, Chunkit Chan, Jiaxin Bai, Yangqiu Song

Prompt Refinement with Image Pivot for Text-to-Image Generation (2024.acl-long)

Challenge:	Existing evaluations of multimodal abductive reasoning are limited to static, single-agent tasks.
Approach:	They propose a multiagent evaluation suite that deconstructs the current evaluations of multimodal abductive reasoning in vision–language models.
Outcome:	The evaluation suite is based on two core components: DixitArena and DixitsBench.

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Jingtao Zhan, Qingyao Ai, Yiqun Liu, Yingwei Pan, Ting Yao, Jiaxin Mao, Shaoping Ma, Tao Mei

Multilinguality

Challenge:	Recent advances in text-to-image generation have markedly expanded the boundaries of digital artistry, enabling the creation of visually compelling images with unprecedented ease.
Approach:	They propose to decompose the prompt refinement process into two tasks: inferring user-preferred images from user languages and translating them into system languages.
Outcome:	Experiments show that PRIP outperforms baselines and transfers to unseen systems in a zero-shot manner.

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

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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.

OneRec-Think: In-Text Reasoning for Generative Recommendation (2026.acl-long)

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Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang, Wuchao Li, PengFei Zheng, Xiangyu Wu, Yifei Hu, Qigen Hu, Xinchen Luo, Lejian Ren, Zhang Zixing, Qianqian Wang, Kuo Cai, Yunfan Wu, Hongtao Cheng, Zexuan Cheng, Lu Ren, Huanjie Wang, Yi Su, Ruiming Tang, Kun Gai, Guorui Zhou

Reasoning

Challenge:	Existing generative models lack the capacity for explicit and controllable reasoning, a key advantage of LLMs.
Approach:	They propose a framework that integrates dialogue, reasoning, and personalized recommendation.
Outcome:	Experiments across public benchmarks show state-of-the-art performance.

Revealing Procedural Reasoning Structures in Chain-of-Thought Training via Span-Level Gradient Organization (2026.acl-long)

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Jia Liu, Jiaxin Luo, Weiwen Xu, Jonathan M. Garibaldi, Xiao-Kun Wu, Yixue Hao, Min Chen