Papers by Jiang Cao

Event Ontology Completion with Hierarchical Structure Evolution Networks (2023.emnlp-main)

Challenge:	Existing code sandboxes fail to provide accurate verification and efficiency under high-concurrency workloads.
Approach:	They propose a high-fidelity code verification system that provides sandbox feedback for RL training and evaluation.
Outcome:	The proposed system outperforms heuristic-matching baselines on LiveCodeBench and training stability on high-concurrency workloads.

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Pengfei Cao, Yupu Hao, Yubo Chen, Kang Liu, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Jun Zhao

Soft Language Clustering for Multilingual Model Pre-training (2023.acl-long)

Challenge:	Existing methods for event detection require predefined schemas, but manual defining is expensive and labor-intensive.
Approach:	They propose a task to achieve event clustering, hierarchy expansion and type naming . they propose 'neighbor Contrastive Clustering' module and a Hierarchy-Aware Linking module .
Outcome:	The proposed method outperforms baseline methods on three datasets.

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Jiali Zeng, Yufan Jiang, Yongjing Yin, Yi Jing, Fandong Meng, Binghuai Lin, Yunbo Cao, Jie Zhou

Multilinguality

Challenge:	Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from the source language or when pre-training data is limited in size.
Approach:	They propose a method that contextually retrieves prompts as flexible guidance for encoding instances conditionally.
Outcome:	The proposed method improves on the XTREME task and also for low-resource languages in unsupervised sentence retrieval.

DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog (2023.findings-emnlp)

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Xin Zheng, Tianyu Liu, Haoran Meng, Xu Wang, Yufan Jiang, Mengliang Rao, Binghuai Lin, Yunbo Cao, Zhifang Sui

Natural Language Interfaces

Challenge:	Existing work on question-answer extraction fails to integrate incomplete utterances from dialog context for composite QA retrieval.
Approach:	They propose a task where questions and corresponding answers might be separated across different utterances.
Outcome:	The proposed methods perform well on 5 customer service datasets and set a benchmark for N-to-N DialogQAE with utterance and session level evaluation metrics.

Gaussian Process based Deep Dyna-Q approach for Dialogue Policy Learning (2021.findings-acl)

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Guanlin Wu, Wenqi Fang, Ji Wang, Jiang Cao, Weidong Bao, Yang Ping, Xiaomin Zhu, Zheng Wang

Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction (D19-1)

Challenge:	Reinforcement learning (RL) is the main dialogue policy learning method in recent years.
Approach:	They propose a Gaussian Process based Deep Dyna-Q approach to dialogue policy learning . they propose evaluating the quality of experiences generated by the world model using a discriminator .
Outcome:	The proposed approach improves the effectiveness and efficiency of dialogue policy learning by 20% with fewer human-machine interactions.

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Shun Zheng, Wei Cao, Wei Xu, Jiang Bian

Tug-of-War between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models (2024.lrec-main)

Challenge:	Existing event extraction methods are limited to extract event arguments within the sentence scope.
Approach:	They propose a model which generates an entity-based directed acyclic graph to fulfill document-level EE effectively.
Outcome:	The proposed model can generate entity-based directed acyclic graph to fulfill document-level EE effectively.

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Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu, Xiaojian Jiang, Jiexin Xu, Li Qiuxia, Jun Zhao

Unveiling Project-Specific Bias in Neural Code Models (2024.lrec-main)

Challenge:	Existing knowledge conflicts in RALMs can ensnare them in a tug-of-war between knowledge and evidence, limiting their practical applicability.
Approach:	They propose a method called Conflict-Disentangle Contrastive Decoding (CD2) to better calibrate the model’s confidence.
Outcome:	The proposed method can resolve knowledge conflicts in large language models with the help of conflict-disentangle contrast decoding (CD2) .

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Zhiming Li, Yanzhou Li, Tianlin Li, Mengnan Du, Bozhi Wu, Yushi Cao, Junzhe Jiang, Yang Liu

Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding (2022.findings-emnlp)

Challenge:	Large Language Models (LLMs) based neural code models struggle to generalize effectively to real-world inter-project out-of-distribution data.
Approach:	They propose a Cond-Idf measurement to measure the relatedness of a token with a label and its project-specificness.
Outcome:	The proposed framework improves both inter-project OOD generalization and adversarial robustness while not sacrificing accuracy on intra-project IID data.

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Jiali Zeng, Yongjing Yin, Yufan Jiang, Shuangzhi Wu, Yunbo Cao

NCRE: A Benchmark for Document-level Nominal Compound Relation Extraction (2025.coling-main)

Challenge:	Recent studies focus on instance-wise contrastive learning, attempting to construct positive pairs with textual data augmentation.
Approach:	They propose a novel Contrastive learning method with Prompt-derived Virtual semantic prototypes that constructs virtual semantic prototype to each instance and derives negative prototypes by using the negative form of the prompts.
Outcome:	The proposed method performs on semantic textual similarity, transfer, and clustering tasks compared to baselines.

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Jincheng Cao, Bobo Li, Jiang Liu, Donghong Ji

Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention Rewards (2026.acl-long)

Challenge:	Existing work focuses on detecting specific relations between entities, often constrained to specific fields and lacking general applicability.
Approach:	They propose a novel task that concentrates on abstract relation extraction between noun phrases . they annotate a Chinese dataset and develop a model incorporating a rotary position-enhanced word pair detection schema.
Outcome:	The proposed task is more efficient than previous methods.

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Ming Li, Pei Chen, Zhenhao Zhang, Tao Yang, Xinyang Zhang, Han Li, Tianyu Cao, Ming Zeng, Zhuofeng Wu, Meng Jiang, Huasheng Li, Lihong Li, Bing Yin

DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition (2022.findings-emnlp)

Challenge:	Large Language Models exhibit strong capabilities in single-turn instruction following but suffer from Lost-in-Conversation (LiC) when instructions are revealed progressively in multi-turn settings, models get "Lost in Conversation"
Approach:	They propose a framework that encourages models to generate correct answers and judge solvability in multi-turn conversations.
Outcome:	The proposed framework improves models' ability to balance problem-solving with abstention . it reduces premature answering behaviors that cause lost-in-conversation (LiC)

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Jiali Zeng, Yufan Jiang, Yongjing Yin, Xu Wang, Binghuai Lin, Yunbo Cao

Towards Efficient NLP: A Standard Evaluation and A Strong Baseline (2022.naacl-main)

Challenge:	Existing approaches to named entity recognition (NER) are limited to high-resource languages like English and Chinese.
Approach:	They propose a framework to make full use of annotated source and unlabeled target language text for zero-shot cross-lingual named entity recognition.
Outcome:	The proposed framework makes full use of both annotated source and unlabeled target language text for zero-shot cross-lingual named entity recognition (NER).

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Xiangyang Liu, Tianxiang Sun, Junliang He, Jiawen Wu, Lingling Wu, Xinyu Zhang, Hao Jiang, Zhao Cao, Xuanjing Huang, Xipeng Qiu

Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models (2024.findings-acl)

Challenge:	Rather than pursuing the reachless SOTA accuracy, researchers are focusing on model efficiency and usability.
Approach:	They propose an evaluation and a public leaderboard for efficient NLP models that depicts the Pareto Frontier for various language understanding tasks.
Outcome:	The proposed model outperforms or performs on par with SOTA compressed and early exiting models.

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Zhuoran Jin, Pengfei Cao, Hongbang Yuan, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, Jun Zhao

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

Challenge:	Existing methods to expand internal memory boundaries of language models by providing external context can often conflict, leading to knowledge conflicts.
Approach:	They propose a method that prunes conflicting attention heads without updating model parameters.
Outcome:	The proposed method can flexibly control eight LMs to use internal memory or external context without updating model parameters.

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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He

Green & Sustainable NLP

Challenge:	Document images are characterized by higher resolutions, denser content, and more complex structural layouts.
Approach:	They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition.
Outcome:	The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead.

FreeChunker: A Cross-Granularity Chunking Framework (2026.findings-acl)

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Zhang Wenxuan, Yuan-Hao Jiang, Yang Cao, Yonghe Wu

Syntactic Text Processing

Challenge:	Existing chunking paradigms rely on static boundary identification, limiting performance . Existing methods rely only on static knowledge, resulting in hallucinated content .
Approach:	They propose a Cross-Granularity Encoding Framework that treats sentences as atomic units and shifts from static chunk segmentation to flexible retrieval supporting arbitrary sentence combinations.
Outcome:	The proposed framework avoids the computational overhead required for semantic boundary detection and enhances adaptability to complex queries.

InquireMobile: Teaching VLM-based Mobile Agent to Request Human Assistance via Reinforcement Fine-Tuning (2026.acl-long)

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Qihang Ai, Pi Bu, Yue Cao, Yingyao Wang, Jihao Gu, Jingxuan Xing, Zekun Zhu, Wei Jiang, Zhicheng Zheng, Jun Song, Yuning Jiang

Unlocking Memorization in Large Language Models with Dynamic Soft Prompting (2024.emnlp-main)

Challenge:	Recent advances in Vision-Language Models (VLMs) have enabled mobile agents to perceive and interact with real-world mobile environments based on human instructions.
Approach:	They propose a vision-language model that actively seeks human confirmation at critical decision points and a model inspired by reinforcement learning.
Outcome:	The proposed model achieves an improvement of 46.8% in inquiry success rate and the best overall success rate among existing baselines on InquireBench.

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Zhepeng Wang, Runxue Bao, Yawen Wu, Jackson Taylor, Cao Xiao, Feng Zheng, Weiwen Jiang, Shangqian Gao, Yanfu Zhang

Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment (2022.acl-long)

Challenge:	Pretrained large language models excel in a variety of natural language processing tasks . however, they pose significant security risks due to their tendency to memorize training data .
Approach:	They propose a method to estimate LLM memorization using dynamic, prefix-dependent soft prompts.
Outcome:	The proposed method can achieve maximum relative improvement of 135.3% and 39.8% over baseline compared to state-of-the-art methods.

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Zijie Huang, Zheng Li, Haoming Jiang, Tianyu Cao, Hanqing Lu, Bing Yin, Karthik Subbian, Yizhou Sun, Wei Wang

An Empirical Comparison of Unsupervised Constituency Parsing Methods (2020.acl-main)

Challenge:	Existing methods to predict missing facts in knowledge graphs are limited in language alignment . SS-AGA uses seed alignment as an edge type to fuses all KGs as a whole graph .
Approach:	They propose a self-supervised adaptive graph alignment method that fuses all KGs as a whole graph by regarding alignment as 'a new edge type' they propose SS-AGA method that uses relation-aware attention weights to capture potential alignment pairs in a new paradigm.
Outcome:	The proposed method can predict missing facts in a knowledge graph (KG) but language alignment is scarce and new alignment identification is noisy.

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Jun Li, Yifan Cao, Jiong Cai, Yong Jiang, Kewei Tu

Xiaomingbot: A Multilingual Robot News Reporter (2020.acl-demos)

Challenge:	Existing methods for unsupervised constituency parsing are inconsistent due to data preprocessing, lexicalization, and evaluation metrics.
Approach:	They propose to standardize experimental settings for better comparability between methods . they compare existing methods with those proposed by decade-old models .
Outcome:	The proposed methods perform better than decade-old models on English and Japanese, respectively, compared with decade- old models.

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Runxin Xu, Jun Cao, Mingxuan Wang, Jiaze Chen, Hao Zhou, Ying Zeng, Yuping Wang, Li Chen, Xiang Yin, Xijin Zhang, Songcheng Jiang, Yuxuan Wang, Lei Li

Interpreting Sentiment Composition with Latent Semantic Tree (2023.findings-acl)

Challenge:	Xiaomingbot is a multilingual and multimodal software robot with four capabilities: news generation, news translation, news reading and avatar animation.
Approach:	They propose to build a multilingual and multimodal software robot with four inte- gal capabilities: news generation, news translation, news reading and avatar animation.
Outcome:	The proposed system generates Chinese news, then reads it in multiple languages and generates an animated avatar reading it.

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Zhongtao Jiang, Yuanzhe Zhang, Cao Liu, Jiansong Chen, Jun Zhao, Kang Liu

Information Retrieval

Challenge:	Current researches on sentiment classification are shifting from improving model performance to interpretability.
Approach:	They propose a new tree form capable of interpreting sentiment composition in a principled way.
Outcome:	The proposed tree can explain sentiment composition in a principled way.

Emotion Transfer with Enhanced Prototype for Unseen Emotion Recognition in Conversation (2025.emnlp-main)

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Kun Peng, Cong Cao, Hao Peng, Guanlin Wu, Zhifeng Hao, Lei Jiang, Yanbing Liu, Philip S. Yu

Sentiment Analysis

Challenge:	Existing research on emotion recognition in conversation does not reach a consensus on classification theories . despite this, there is no clear consensus on how to recognize previously unseen emotions in real-world applications.
Approach:	They propose a prototype-based emotion transfer framework that can be used in real-world applications.
Outcome:	The proposed framework shows promise but still faces key challenges in the field of emotion recognition in conversation.

ArchiDocGen: Multi-Agent Framework for Expository Document Generation in the Architectural Industry (2025.acl-industry)

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Junjie Jiang, Haodong Wu, Yongqi Zhang, Songyue Guo, Bingcen Liu, Caleb Chen Cao, Ruizhe Shao, Chao Guan, Peng Xu, Lei Chen

Text Generation

Challenge:	drafting method statements is labor-intensive and time-consuming . traditional methods involve using static templates filled in manually by engineers .
Approach:	They propose a framework that automates method statement generation by using multi-agent collaboration.
Outcome:	The proposed framework achieves 4.38 ContentScore, excelling in specialization, completeness, organization, and clarity.

ℛ3: Advertisement Compliance ℛectification via Group-ℛelative Experience Extractor and Curriculum ℛeinforcement (2026.acl-industry)

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Yuan Chen, Zhenyu Hu, Mengge Xue, Cao Te, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

SPM: A Split-Parsing Method for Joint Multi-Intent Detection and Slot Filling (2023.acl-industry)

Challenge:	Existing methods of content moderation are infeasible due to over-editing and compromise the advertiser’s original semantic intent.
Approach:	They propose a framework to harmonize compliance with original intent preservation that integrates a data-driven framework and a curriculum to enforce compliance while maximizing semantic consistency.
Outcome:	The proposed framework outperforms state-of-the-art baselines on industrial datasets and on online A/B testing on industrial video.

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Sheng Jiang, Su Zhu, Ruisheng Cao, Qingliang Miao, Kai Yu

Semantic Parsing

Challenge:	Existing studies focus on utterances with a single intent, but lack the ability to assign slots to each corresponding intent.
Approach:	They propose a split-parsing method for joint intent detection and slot filling . they split an input sentence into multiple sub-sentences which contain a single-intent .
Outcome:	The proposed method improves on three multi-intent datasets on multi-tasks.

TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale (2024.naacl-long)

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Pengcheng Jiang, Cao Xiao, Zifeng Wang, Parminder Bhatia, Jimeng Sun, Jiawei Han

Summarization

Challenge:	Large language models (LLMs) have advanced tasks like text summarization, but their size and computational demands limit their use in resource-constrained and privacy-centric settings.
Approach:	They propose a framework for distilling LLMs’ text summarization abilities into a compact, local model using a curriculum learning strategy that evolves from simple to complex tasks.
Outcome:	The proposed framework outperforms baseline models on CNN/DailyMail, XSum, and ClinicalTrial, and improves interpretability by providing insights into the summarization rationale.

LINKED: Eliciting, Filtering and Integrating Knowledge in Large Language Model for Commonsense Reasoning (2024.findings-emnlp)

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Jiachun Li, Pengfei Cao, Chenhao Wang, Zhuoran Jin, Yubo Chen, Kang Liu, Xiaojian Jiang, Jiexin Xu, Jun Zhao

Reasoning

Challenge:	Large language models (LLMs) often exhibit poor performance on knowledge-intensive tasks, such as commonsense reasoning.
Approach:	They propose a method to elicit, filter and integrate knowledge in large language models (LINKED) they propose 'reward model' to filter out noisy knowledge and 'take marginal consistent reasoning module'
Outcome:	The proposed method outperforms SOTA baselines on two commonsense reasoning tasks.

Retentive or Forgetful? Diving into the Knowledge Memorizing Mechanism of Language Models (2024.lrec-main)

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Boxi Cao, Qiaoyu Tang, Hongyu Lin, Shanshan Jiang, Bin Dong, Xianpei Han, Jiawei Chen, Tianshu Wang, Le Sun

LogicPro: Improving Complex Logical Reasoning via Program-Guided Learning (2025.acl-long)

Challenge:	Pre-trained language models have shown remarkable memory formation, but vanilla networks without pre-training suffer catastrophic forgetting problem.
Approach:	They conduct experiments to investigate the retentive-forgetful contradiction between vanilla and pre-trained language models by controlling the target knowledge types, learning strategies and learning schedules.
Outcome:	The results show that pre-trained language models are forgetful and pre-training leads to retentive models .

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Jin Jiang, Yuchen Yan, Yang Liu, Jianing Wang, Shuai Peng, Xunliang Cai, Yixin Cao, Mengdi Zhang, Liangcai Gao

Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection (2026.findings-acl)

Challenge:	LogicPro is a data synthesis method that uses LeetCode-style algorithm problems and their corresponding Program solutions to generate complex logic data.
Approach:	They propose a new method which leverages LeetCode-style algorithm Problems and their corresponding Program solutions to synthesize complex logic data in text format.
Outcome:	The proposed method outperforms existing models for BBH27, LogicBench, DROP, AR-LSAT, and GSM8K, and a wide range of reasoning datasets.

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Zhiwei Liu, Yupeng Cao, Yuechen Jiang, Mohsinul Kabir, Polydoros Giannouris, Chen Xu, Ziyang Xu, Tianlei Zhu, Md. Tariquzzaman, Triantafillos Papadopoulos, Yan Wang, Lingfei Qian, Xueqing Peng, Zhuohan Xie, Ye Yuan, Saeed Almheiri, Abdulrazzaq Alnajjar, Ming-Bin Chen, Harry Stuart, Paul Thompson, Prayag Tiwari, Alejandro Lopez-Lira, Xue Liu, Jimin Huang, Sophia Ananiadou

INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent (2025.acl-long)

Challenge:	Existing research on LLM biases has focused on direct questioning or general-purpose settings . pronounced behavioral biase despite their growing deployment in financial analysis, forecasting, and decision support.
Approach:	They propose a benchmark to evaluate behavioral biases of large language models in MFMD . they use a multilingual financial misinformation dataset to integrate these with misinformation claims .
Outcome:	The proposed benchmark evaluates behavioral biases of large language models across economic scenarios.

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Haohang Li, Yupeng Cao, Yangyang Yu, Shashidhar Reddy Javaji, Zhiyang Deng, Yueru He, Yuechen Jiang, Zining Zhu, K.p. Subbalakshmi, Jimin Huang, Lingfei Qian, Xueqing Peng, Jordan W. Suchow, Qianqian Xie

Prompting for Multimodal Hateful Meme Classification (2022.emnlp-main)

Challenge:	Recent advances have underscored the potential of large language model (LLM)-based agents in financial decision-making.
Approach:	They propose to evaluate LLM agents using 13 different LLMs as backbone models across various market environments and tasks.
Outcome:	The proposed framework assesses the reasoning and decision-making capabilities of 13 different LLMs across various market environments and tasks.

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Rui Cao, Roy Ka-Wei Lee, Wen-Haw Chong, Jing Jiang

ReMedi: Reasoner for Medical Clinical Prediction (2026.findings-acl)

Challenge:	Hateful meme classification requires complex reasoning and contextual background knowledge.
Approach:	They propose a simple yet effective prompt-based model that prompts pre-trained language models for hateful meme classification.
Outcome:	The proposed model outperforms state-of-the-art models on hateful meme classification task.

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Yushi Cao, Yiming Chen, Hongchao Jiang, Hung-yi Lee, Robby T. Tan

Reasoning

Challenge:	Existing approaches to predicting future clinical outcomes from EHRs focus on enhancing medical knowledge through distillation or RAG while relying on the model’s internal ability to interpret contextual information.
Approach:	They propose a framework for improving clinical outcome prediction from EHR using a sample regeneration mechanism that leverages ground-truth answers as hints to enhance reasoning.
Outcome:	Experiments on multiple EHR prediction tasks show significant gains of up to 19.9% over state-of-the-art baselines in terms of F1 score, underscoring ReMedi’s effectiveness in real-world clinical prediction.

Visual Prompt Tuning for Few-Shot Text Classification (2022.coling-1)

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Jingyuan Wen, Yutian Luo, Nanyi Fei, Guoxing Yang, Zhiwu Lu, Hao Jiang, Jie Jiang, Zhao Cao

Jailbreaking Safeguarded Text-to-Image Models via Large Language Models (2026.findings-eacl)

Challenge:	Existing work on pretraining models for text classification uses image encoders instead of visual prompts.
Approach:	They propose a method to deploy large-scale pre-trained models in the prompt-tuning paradigm in few-shot learning.
Outcome:	The proposed method outperforms the most recent prompt-tuning methods on five public text classification datasets.

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Zhengyuan Jiang, Yuepeng Hu, Yuchen Yang, Yinzhi Cao, Neil Zhenqiang Gong

Modularized Zero-shot VQA with Pre-trained Models (2023.findings-acl)

Challenge:	Text-to-image models generate harmful content when unsafe prompts are submitted . authors propose a method to jailbreak text-to image models with safety guardrails .
Approach:	They propose a method to jailbreak text-to-image models with safety guardrails . they use a fine-tuned large language model to generate adversarial prompts based on unsafe prompts.
Outcome:	The proposed method bypasses safety guardrails and outperforms existing no-box attacks . the proposed method generates adversarial prompts efficiently after fine-tuning the model .

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Rui Cao, Jing Jiang

Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data (2021.acl-long)

Challenge:	Recent work on zero-shot visual question answering does not explicitly consider multi-step reasoning chains, making them less interpretable compared with a decomposition-based approach.
Approach:	They propose a modularized zero-shot network that explicitly decomposes questions into sub reasoning steps and is highly interpretable.
Outcome:	The proposed model decomposes questions into sub reasoning steps and is highly interpretable.

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Haoming Jiang, Danqing Zhang, Tianyu Cao, Bing Yin, Tuo Zhao

The Linguistic Connectivities Within Large Language Models (2025.findings-acl)

Challenge:	Existing work focuses on learning deep NER models with weak supervision without any human annotation.
Approach:	They propose a framework that can suppress the noise of the weak labels and fine-tune over the strongly labeled data.
Outcome:	The proposed framework outperforms existing methods on Named Entity Recognition tasks with weak supervision and weakly labeled data.

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Dan Wang, Boxi Cao, Ning Bian, Xuanang Chen, Yaojie Lu, Hongyu Lin, Jia Zheng, Le Sun, Shanshan Jiang, Bin Dong, Xianpei Han

Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation (2020.findings-emnlp)

Challenge:	Recent studies have discovered notable disparities in their performance across different languages.
Approach:	They conduct a systematic investigation into the behaviors of large language models across 27 different languages on 3 different scenarios and reveals a Linguistic Map correlates with the richness of available resources and linguistic family relations.
Outcome:	The proposed model demonstrates that there are significant disparities in performance across languages across 27 different languages on 3 different scenarios.

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Chujie Zheng, Yunbo Cao, Daxin Jiang, Minlie Huang

All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection (2026.acl-long)

Challenge:	Existing knowledge selection models are limited by the context, but the difference between selected knowledge at different turns is often overlooked.
Approach:	They propose a difference-aware knowledge selection method that computes the difference between the candidate knowledge sentences provided at the current turn and those chosen in the previous turns.
Outcome:	The proposed method outperforms the state-of-the-art methods in a knowledge-grounded dialog.

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Yuechen Jiang, Zhiwei Liu, Yupeng Cao, Yueru He, Ziyang Xu, Chen Xu, Zhiyang Deng, Prayag Tiwari, Xi Chen, Alejandro Lopez-Lira, Jimin Huang, Junichi Tsujii, Sophia Ananiadou

Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration (2024.findings-emnlp)

Challenge:	RFC-Bench evaluates large language models on financial misinformation under realistic news . current models struggle to maintain coherent belief states without external grounding, study finds .
Approach:	They propose a benchmark for evaluating large language models on financial misinformation under realistic news.
Outcome:	The proposed model performs better when context is available, while reference-free settings expose significant weaknesses.

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Weikang Yuan, Junjie Cao, Zhuoren Jiang, Yangyang Kang, Jun Lin, Kaisong Song, Tianqianjin Lin, Pengwei Yan, Changlong Sun, Xiaozhong Liu

K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters (2021.findings-acl)

Challenge:	Existing studies have found that when LLMs are given criminal facts and legal rules, then asked whether cases constitute a certain charge, they struggle to understand legal theories and perform basic legal reasoning tasks.
Approach:	They propose a task to assess LLMs' understanding of legal theories and reasoning capabilities by using a novel framework: Multi-Agent framework for improving complex legal reasoning capability.
Outcome:	The proposed framework improves LLMs' understanding of legal theories and reasoning abilities in real-world scenarios.

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Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, Ming Zhou

Revisiting the Evaluation of End-to-end Event Extraction (2021.findings-acl)

Challenge:	Existing methods for injecting knowledge into pre-trained models are inconsistent and can flush out knowledge when multiple kinds of knowledge are injected.
Approach:	They propose a framework that retains the original parameters of pre-trained models fixed and supports the development of versatile knowledge-infused models.
Outcome:	The proposed framework retains the original parameters of the pre-trained model fixed and supports the development of versatile knowledge-infused models.

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Shun Zheng, Wei Cao, Wei Xu, Jiang Bian

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

Challenge:	Existing EE research uses the role-averaged evaluation metric, but it is misleading to downstream applications.
Approach:	They propose two new evaluation metrics that explicitly penalize wrongly identified event arguments.
Outcome:	The proposed evaluation metrics improve the initial evaluation by 10% . the proposed training scheme is better than the existing one, the authors show .

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

Knowledge Generation for Zero-shot Knowledge-based VQA (2024.findings-eacl)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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Rui Cao, Jing Jiang

Alleviating Over-smoothing for Unsupervised Sentence Representation (2023.acl-long)

Challenge:	Recent knowledge-based visual question answering methods do not explicitly show the knowledge needed to answer the questions and therefore lack interpretability.
Approach:	They propose a method which generates knowledge from an LLM and incorporates it into a zero-shot manner.
Outcome:	The proposed method performs better than previous zero-shot K-VQA methods on two benchmarks and is generally relevant and helpful.

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Nuo Chen, Linjun Shou, Jian Pei, Ming Gong, Bowen Cao, Jianhui Chang, Jia Li, Daxin Jiang

Towards Better Chain-of-Thought: A Reflection on Effectiveness and Faithfulness (2025.findings-acl)

Challenge:	Existing approaches to learn better unsupervised sentence representations have been successful . over-smoothing problem in unsupervised sentences reduces the capacity of powerful PLMs .
Approach:	They propose a method to solve the over-smoothing problem in unsupervised sentence representations by combining negatives from PLMs intermediate layers.
Outcome:	The proposed method improves on different strong baselines on Semantic Textual Similarity and Transfer datasets.

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Jiachun Li, Pengfei Cao, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, Jun Zhao

Challenge:	Chain-of-thought (CoT) prompting demonstrates varying performance under different reasoning tasks.
Approach:	They propose to recall extra information from the question to enhance CoT generation and evaluate CoTs based on their information gain.
Outcome:	The proposed method improves both the faithfulness and effectiveness of CoT and evaluates it based on their information gain.

XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation (2020.emnlp-main)

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Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou

Multilinguality

Challenge:	XGLUE provides a benchmark dataset to train large-scale cross-lingual pre-trained models . XCLUE provides 11 diversified tasks that cover both understanding and generation scenarios .
Approach:	They introduce a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora.
Outcome:	The proposed dataset is labeled in English and includes only natural language understanding tasks.

Global Context or Local Detail? Adaptive Visual Grounding for Hallucination Mitigation (2026.findings-acl)

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Yubo Jiang, Xin Yang, Abudukelimu Wuerkaixi, Zheming Yuan, Xuxin Cheng, Cao Liu, Ke Zeng, Fengying Xie, Zhiguo Jiang, Haopeng Zhang

CodeJudgeBench: Benchmarking LLM-as-a-Judge for Coding Tasks (2026.acl-long)

Challenge:	Large vision–language models suffer from object-existence hallucinations when multi-step deliberation decouples from visual evidence.
Approach:	They propose a framework that allocates visual computation by uncertainty . they propose highlighting retains global context, while selective zoom-in performs local verification.
Outcome:	The proposed framework reduces the complexity of multimodal reasoning by minimizing the operator trade-off.

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Hongchao Jiang, Yiming Chen, Yushi Cao, Hung-yi Lee, Robby T. Tan

Robustness in NLP

Challenge:	Large Language Models (LLMs) are increasingly used to judge code, but their reliability remains poorly understood.
Approach:	They propose a benchmark to evaluate Large Language Models as code judges . they find that small reasoning models outperform larger non-reasoning models .
Outcome:	The proposed benchmark evaluates LLM-as-a-Judge models across three coding tasks.

LeCoDe: A Benchmark Dataset for Interactive Legal Consultation Dialogue Evaluation (2026.acl-long)

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Weikang Yuan, Kaisong Song, Zhuoren Jiang, Junjie Cao, Yujie Zhang, Jun Lin, Kun Kuang, Ji Zhang, Xiaozhong Liu

When 20 Agents Fail to Sort: The Distributed Sorting Benchmark for Scalable Multi-Agent Systems (2026.findings-acl)

Challenge:	Current systems for legal consultation are insufficient to handle the knowledge-intensive nature of real-world consultations.
Approach:	They propose a multi-turn benchmark dataset to evaluate LLMs in legal consultation settings.
Outcome:	The proposed framework assesses LLMs’ consultation capabilities in terms of (1) clarification capability and (2) professional advice quality.

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Xin Yang, Junhao Wang, Bintao Tang, Xuxin Cheng, Cao Liu, Ke Zeng, Wenyuan Jiang

Challenge:	MAS-BENCH isolates coordination under explicit communication constraints . CAMOC significantly improves coordination success and efficiency across backends .
Approach:	They propose a distributed-sorting benchmark that isolates coordination under explicit communication constraints.
Outcome:	MAS-BENCH improves coordination success and efficiency across backends . CAMOC significantly improves efficiency under shared-state interaction .

MIE: A Medical Information Extractor towards Medical Dialogues (2020.acl-main)

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Yuanzhe Zhang, Zhongtao Jiang, Tao Zhang, Shiwan Liu, Jiarun Cao, Kang Liu, Shengping Liu, Jun Zhao

Simulating Classroom Education with LLM-Empowered Agents (2025.naacl-long)

Challenge:	EMRs are important but many doctors suffer from writing them, which is time-consuming and tedious.
Approach:	They propose an automatic conversion of medical dialogues to EMRs using a window-sliding style . they propose a medical information extractor (MIE) that extracts medical information from medical dialogue .
Outcome:	The proposed model extracts medical information from doctor-patient dialogues.

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Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, Linlu Gong, Jinchang Zhou, Zhanxin Hao, Jianxiao Jiang, Jie Cao, Huiqin Liu, Zhiyuan Liu, Lei Hou, Juanzi Li

Complex Event Schema Induction with Knowledge-Enriched Diffusion Model (2023.findings-emnlp)

Challenge:	Initial studies have focused on task-specific, independent LLM-empowered agents, but the potential of LLMs within a multi-agent collaborative framework for classroom simulation with real user participation remains unexplored.
Approach:	They propose a multi-agent classroom simulation teaching framework that recognizes representative class roles and introduces a novel class control mechanism for automatic classroom teaching.
Outcome:	The proposed framework can simulate dynamic learning environment for users with active teacher-student and student-studente interactions.

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Yupu Hao, Pengfei Cao, Yubo Chen, Kang Liu, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Jun Zhao

Challenge:	Existing studies on event schema induction have been hindered by errors and data quality issues.
Approach:	They propose a knowledge-enriched discrete diffusion model that distills event scenario knowledge from LLMs.
Outcome:	The proposed model achieves outstanding performance across evaluation metrics.

Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis (2022.naacl-main)

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Jiahao Cao, Rui Liu, Huailiang Peng, Lei Jiang, Xu Bai

Sentiment Analysis

Challenge:	Existing approaches to classify aspects with aspect sentiment bias are hard to find .
Approach:	They propose a no-aspect differential sentiment framework for the ABSA task that eliminates aspect sentiment bias and uses differential sentiment loss instead of cross-entropy loss to better classify the sentiments.
Outcome:	The proposed framework can be combined with almost all traditional ABSA methods.

Do LLMs Encode Frame Semantics? Evidence from Frame Identification (2025.emnlp-main)

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Jayanth Krishna Chundru, Rudrashis Poddar, Jie Cao, Tianyu Jiang

SiBert: Enhanced Chinese Pre-trained Language Model with Sentence Insertion (2020.lrec-1)

Challenge:	Using the FrameNet lexical resource, we evaluate large language models under prompt-based inference and observe that they can perform frame identification effectively even without explicit supervision.
Approach:	They evaluate large language models under prompt-based inference and observe that they encode latent knowledge of frame semantics.
Outcome:	The proposed model can generate coherent frame definitions while generalizing well to out-of-domain benchmarks.

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Jiahao Chen, Chenjie Cao, Xiuyan Jiang

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment (2025.findings-acl)

Challenge:	Recent studies show that pre-trained models can learn unsupervised language representations by self-supervised tasks on large-scale corpora.
Approach:	They propose a pre-training task called Sentence Insertion for Chinese query-passage pairs NLP tasks . they propose 'word segmentation' method to enhance Chinese Bert performance .
Outcome:	The proposed task improves Chinese pre-trained models significantly.

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Zhuoran Jin, Hongbang Yuan, Tianyi Men, Pengfei Cao, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, Jun Zhao

Information Retrieval

Challenge:	Existing retrieval augmented language models often overlook effective alignment with human preferences.
Approach:	They propose a benchmark to evaluate RMs in retrieval augmented language models . they incorporate 18 RAG subsets, six retrievers, and 24 RALMs to increase diversity .
Outcome:	The proposed benchmark combines 18 RAG subsets, six retrievers, and 24 RALMs to increase diversity of data sources.

From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization (2026.findings-acl)

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Chenxi Zhou, Pengfei Cao, Jiang Li, Bohan Yu, Jinyu Ye, Jun Zhao, Kang Liu

Challenge:	Existing research on PTQ spans three primary directions.
Approach:	They conduct a systematic analysis of post-training quantization failures using PTQ . they show that targeted repair can mitigate Signal Degradation but remains ineffective for Computation Collapse .
Outcome:	The proposed method mitigates Signal Degradation but remains ineffective for Computation Collapse.

Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models (2026.findings-acl)

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Chenxi Zhou, Pengfei Cao, Jiang Li, Bohan Yu, Jinyu Ye, Jun Zhao, Kang Liu

Few-shot Temporal Pruning Accelerates Diffusion Models for Text Generation (2024.lrec-main)

Challenge:	Existing scaling laws focus on general performance, overlooking crucial fine-grained factors and how quantization differentially impacts diverse knowledge capabilities.
Approach:	They propose a framework that unifies model size, bit-width, and fine-grained factors into memorization, application, and reasoning.
Outcome:	The proposed framework shows strong fit and cross-architecture consistency on 293 different PTQ configurations.

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Bocheng Li, Zhujin Gao, Yongxin Zhu, Kun Yin, Haoyu Cao, Deqiang Jiang, Linli Xu

Low-Resource NLP

Challenge:	Existing acceleration methods for text generation ignore the importance of the distribution of sampling steps, resulting in slow sampling rates.
Approach:	They propose a technique to accelerate diffusion models for text generation without additional training by using a Bayesian optimization approach.
Outcome:	The proposed technique achieves 400x acceleration even with minimal sampling steps after down to less than 1 minute of optimization yielding a competitive performance even with minimum sampling steps.

Lightweight Haar Wavelet Subband Pruning for LLMs (2026.findings-acl)

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Jiang Li, Pengfei Cao, Chenxi Zhou, Tian Lan, Xiangdong Su, Kang Liu, Jun Zhao, Guanglai Gao

Challenge:	Large language models (LLMs) have impressive performance but require computational and memory resources.
Approach:	They propose a post-training framework that uses a Haar wavelet transform to prune weights.
Outcome:	The proposed pruning framework reduces pruning time and computational costs by removing less important weights while preserving model architecture.

From Scores to Preferences: Redefining Evaluation Paradigm for Speech Quality Reward Modeling (2026.findings-acl)

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Yifei Cao, Changhao Jiang, Jiabao Zhuang, Jiajun Sun, Ming Zhang, Zhiheng Xi, Hui Li, Shihan Dou, Yuran Wang, Yunke Zhang, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang

SSR-A: Spatial- and Semantic-Aware Instructions and Curriculum Reinforcement for Advertisement Compliant Rectification (2026.acl-industry)

Challenge:	Experimental results show that the MOS-aware GRM significantly improves fine-grained speech quality discrimination.
Approach:	They propose a MOS-aware reward model that incorporates MOS gap into reward function during reinforcement learning.
Outcome:	The proposed model significantly improves fine-grained speech quality discrimination.

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Cao Te, Mengge Xue, Zhenyu Hu, Yuan Chen, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

Improving Grammatical Error Correction by Correction Acceptability Discrimination (2024.lrec-main)

Challenge:	Existing methods to fix non-compliant images suffer from over-editing, destroying original intent and perceptual similarity.
Approach:	They propose a framework for the minimalist rectification of non-compliant image ads.
Outcome:	The proposed framework outperforms state-of-the-art baselines in both compliance and preservation of visual and commercial consistency.

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Bin Cao, Kai Jiang, Fayu Pan, Chenlei Bao, Jing Fan

Syntactic Text Processing

Challenge:	Existing Grammatical Error Correction (GEC) methods overlook the assessment of sentence-level syntax and semantics in the corrected sentence.
Approach:	They propose a correction acceptance discrimination task to assess sentence-level syntax and semantics in corrected sentences and a pipeline method to remove invalid corrections.
Outcome:	The proposed method improves F0.5 score by 1.01% over 13 GEC systems in the BEA-2019 test set.

Generative Calibration for In-context Learning (2023.findings-emnlp)

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Zhongtao Jiang, Yuanzhe Zhang, Cao Liu, Jun Zhao, Kang Liu

Low-Resource NLP

Challenge:	In-context learning is one of the most exciting features of large language models . performance is sensitive to various configurations of the prompt, such as the choice or order of the training examples.
Approach:	They propose to calibrate the in-context predictive distribution by adjusting the label marginal . they find that the proposed method outperforms the ICL and state-of-the-art calibration methods .
Outcome:	The proposed method outperforms state-of-the-art methods by 27% absolute in macro-F1.

EvoWiki: Evaluating LLMs on Evolving Knowledge (2025.acl-long)

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Wei Tang, Yixin Cao, Yang Deng, Jiahao Ying, Bo Wang, Yizhe Yang, Yuyue Zhao, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang, Yong Liao

Challenge:	Existing knowledge evolution benchmarks are static and fail to capture the evolving nature of LLMs and knowledge.
Approach:	They propose an evolving dataset that categorizes information into stable, evolved, and uncharted states.
Outcome:	The proposed dataset is auto-updatable and enables evaluation of continuously changing knowledge and newly released LLMs.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization (2026.acl-long)

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Yihong Dong, Xue Jiang, Yongding Tao, Huanyu Liu, Kechi Zhang, Lili Mou, Rongyu Cao, Yingwei MA, Jue Chen, Binhua Li, Zhi Jin, Fei Huang, Yongbin Li, Ge Li

Reasoning

Challenge:	Reinforcement Learning with Verifiable Reward (RLVR) has significantly advanced the complex reasoning abilities of Large Language Models (LLMs).
Approach:	They propose a hybrid-policy optimization approach that synergizes internal exploitation with external data to achieve stronger reasoning capabilities.
Outcome:	The proposed approach achieves state-of-the-art performance on six math reasoning benchmarks and superior performance on out-of distribution reasoning tasks.

OpenRLHF: A Ray-based Easy-to-use, Scalable and High-performance RLHF Framework (2025.emnlp-demos)

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Jian Hu, Xibin Wu, Wei Shen, Jason Klein Liu, Weixun Wang, Songlin Jiang, Haoran Wang, Hao Chen, Bin Chen, Wenkai Fang, null Xianyu, Yu Cao, Haotian Xu, Yiming Liu

PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits (2024.findings-naacl)

Challenge:	Existing RLHF frameworks face inference bottlenecks and complexity barriers restricting their accessibility for newcomers.
Approach:	They propose an open-source RLHF framework that can be used to train large language models.
Outcome:	The proposed framework achieves superior training efficiency with speedups ranging from 1.22 to 1.68 across different model sizes compared to state-of-the-art frameworks, while requiring significantly fewer lines of code for implementation.

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Hang Jiang, Xiajie Zhang, Xubo Cao, Cynthia Breazeal, Deb Roy, Jad Kabbara

Beyond Scaling: Measuring and Predicting the Upper Bound of Knowledge Retention in Language Model Pre-Training (2026.acl-long)

Challenge:	Recent studies have shown that LLMs can generate content that aligns with their assigned personality traits, but there is limited research on whether they consistently reflect specific personality traits.
Approach:	They propose to study the behavior of LLM-based agents which they refer to as LLM personas and simulate them to measure their personality traits.
Outcome:	The proposed model is based on the Big Five personality model and has been validated by human evaluations and automatic evaluations.

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Changhao Jiang, Ming Zhang, Yifei Cao, Junjie Ye, Xiaoran Fan, Shihan Dou, Zhiheng Xi, Jiajun Sun, Yi Dong, Yujiong Shen, Jingqi Tong, Baoyu Fan, Tao Gui, Qi Zhang, Xuanjing Huang

GMN: Generative Multi-modal Network for Practical Document Information Extraction (2022.naacl-main)

Challenge:	Existing methods to predict performance of large language models are lacking . authors propose a size-dependent mutual information predictor for closed-book question answering accuracy .
Approach:	They propose a size-dependent mutual information predictor that integrates knowledge frequency, knowledge specificity, and model size to forecast closed-book question answering accuracy.
Outcome:	The proposed method outperforms baseline models and achieves R2 > 0.7 in predicting QA accuracy without additional training.

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Haoyu Cao, Jiefeng Ma, Antai Guo, Yiqing Hu, Hao Liu, Deqiang Jiang, Yinsong Liu, Bo Ren

Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering (2022.acl-long)

Challenge:	Document Information Extraction (DIE) has attracted increasing attention due to its various advanced applications in the real world.
Approach:	They propose a multi-modal generation method without predefined label categories for real-world scenarios using a spatial encoder and modal-aware mask module.
Outcome:	The proposed method can deal with complex documents that are hard to serialize into sequential order.

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Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen

Leros: Learning Explicit Reasoning on Synthesized Data for Commonsense Question Answering (2024.lrec-main)

Challenge:	Existing methods to train dense passage retrieval have a large data gap between upstream and downstream relevance.
Approach:	They propose a method to pre-train the dense retriever with the text relevance induced by hyperlinks within Web documents.
Outcome:	The proposed method outperforms existing methods under different scenarios and in the open-domain question answering domain.

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Chenhao Wang, Pengfei Cao, Jiachun Li, Yubo Chen, Kang Liu, Xiaojian Jiang, Jiexin Xu, Li Qiuxia, Jun Zhao

Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training (2026.acl-long)

Challenge:	Recent work shows large language models can generate useful rationales for commonsense question answering (CQA) however, the cost of deployment and further tuning is relatively expensive for the large models.
Approach:	They propose a framework that leverages both knowledge graphs and large language models to synthesize rationale-augmented CQA data.
Outcome:	The proposed model can generate useful rationales on unseen CQA benchmarks.

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Jihao Gu, Qihang Ai, Yingyao Wang, Pi Bu, Jingxuan Xing, Yue Cao, Zekun Zhu, Wei Jiang, Ziming Wang, Yingxiu Zhao, Ming-Liang Zhang, Jun Song, Yuning Jiang, Bo Zheng