Papers by Ming Jiang

WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach (2021.findings-emnlp)

Challenge:	Existing benchmarks and MLLMs focus on single-image input scenarios, leaving performance of ML models when handling multiple images underexplored.
Approach:	They propose a benchmark to evaluate fine-grained abilities of multimodal large language models in multi-image scenarios.
Outcome:	The proposed benchmark categorizes the multi-image abilities into three scenarios: MII, MKS and MIC.

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Junjie Huang, Duyu Tang, Wanjun Zhong, Shuai Lu, Linjun Shou, Ming Gong, Daxin Jiang, Nan Duan

Muse: Towards Reproducible Long-Form Song Generation with Fine-Grained Style Control (2026.findings-acl)

Challenge:	Pre-trained language models perform well on learning sentence semantics when fine-tuned with supervised data.
Approach:	They conduct a thorough examination of pretrained model based unsupervised sentence embeddings.
Outcome:	The proposed approach improves on whitening-based vector normalization with less than 10 lines of code.

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Changhao Jiang, Jiahao Chen, Zhenghao Xiang, Zhixiong Yang, Hanchen Wang, Jiabao Zhuang, Xinmeng Che, Jiajun Sun, Hui Li, Yifei Cao, Shihan Dou, Ming Zhang, Junjie Ye, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang

Speech & Audio in NLP

Challenge:	Recent commercial systems such as Suno demonstrate strong capabilities in long-form song generation, but academic research remains non-reproducible due to the lack of publicly available training data.
Approach:	They propose a system for long-form song generation with fine-grained style conditioning that includes a licensed synthetic dataset and a song generation model, Muse.
Outcome:	The proposed system achieves competitive performance on phoneme error rate, text–music style similarity, and audio aesthetic quality while enabling controllable segment-level generation across different musical structures.

LCR-RAG: Enhancing Logical Consistency in Retrieval-Augmented Generation via Neuro-symbolic Reinforcement Learning (2026.acl-long)

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Wenxiang Zheng, Guo Tang, Shixin Jiang, Liangyu Huo, Xiyuan Zhang, Jian Xie, Ming Liu

Information Retrieval

Challenge:	Retrieval-Augmented Generation (RAG) is widely used to ground large language models in external knowledge and improve factual accuracy.
Approach:	They propose a framework that integrates neuro-symbolic verification with reinforcement learning to optimize logical consistency.
Outcome:	The proposed framework outperforms strong RAG baselines on hotpotQA, ASQA, and TriviaQA.

SumSurvey: An Abstractive Dataset of Scientific Survey Papers for Long Document Summarization (2024.findings-acl)

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Ran Liu, Ming Liu, Min Yu, He Zhang, Jianguo Jiang, Gang Li, Weiqing Huang

Text Generation

Challenge:	a growing need for long document summarization datasets with 16k input is causing problems.
Approach:	They propose to use a dataset to analyze salient information in long document summarizations.
Outcome:	The proposed dataset outperforms existing models and LLMs in the distribution form of salient information and the distribution of salinal information is an indicator of quality.

A Constituency Parsing Tree based Method for Relation Extraction from Abstracts of Scholarly Publications (D19-53)

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Ming Jiang, Jana Diesner

Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder (2020.acl-main)

Challenge:	Existing methods for relation extraction rely on lexical patterns and dependency templates.
Approach:	They propose a rule-based method for extracting entity networks from scientific literature . they use syntactic features of constituent parsing trees to extract and construct graphs .
Outcome:	The proposed method outperforms state-of-the-art methods in several cases.

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Daya Guo, Duyu Tang, Nan Duan, Jian Yin, Daxin Jiang, Ming Zhou

Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks (D19-1)

Challenge:	Existing approaches for inferential text generation ignore context that is not explicitly provided . Existing models ignore background knowledge that provides crucial evidence to generate inferences .
Approach:	They propose an approach that automatically finds evidence for an event from a large text corpus and leverages it to guide the generation of inferential texts.
Outcome:	The proposed model generates inferential texts from a large text corpus and uses evidence to guide it.

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Haoyang Huang, Yaobo Liang, Nan Duan, Ming Gong, Linjun Shou, Daxin Jiang, Ming Zhou

CoSQA: 20,000+ Web Queries for Code Search and Question Answering (2021.acl-long)

Challenge:	Existing models that can handle cross-lingual tasks with limited or no training data are insensitive to different languages.
Approach:	They propose to use Unicoder to train models in one language and apply it to other languages.
Outcome:	Experiments show that Unicoder learns the mappings among different languages from more perspectives.

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Junjie Huang, Duyu Tang, Linjun Shou, Ming Gong, Ke Xu, Daxin Jiang, Ming Zhou, Nan Duan

Information Retrieval

Challenge:	Using deep neural networks to find codes is difficult . we present a dataset that includes 20,604 labels for natural language queries and codes .
Approach:	They introduce a contrastive learning method to enhance text-code matching . they find that CoSQA improves the accuracy of code question answering by 5.1% .
Outcome:	The proposed method improves the accuracy of code question answering by 5.1% and improves by 10.5% on a CodeBERT model.

CipherBank: Exploring the Boundary of LLM Reasoning Capabilities through Cryptography Challenge (2025.findings-acl)

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Yu Li, Qizhi Pei, Mengyuan Sun, Honglin Lin, Chenlin Ming, Xin Gao, Jiang Wu, Conghui He, Lijun Wu

A Semi-supervised Scalable Unified Framework for E-commerce Query Classification (2025.acl-industry)

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities, but their capabilities in cryptographic decryption tasks remain underexplored.
Approach:	They propose a benchmark to evaluate the reasoning capabilities of large language models in cryptographic decryption tasks.
Outcome:	The proposed benchmark examines the reasoning capabilities of large language models in cryptographic decryption tasks.

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Chunyuan Yuan, Chong Zhang, Zhen Fang, Ming Pang, Xue Jiang, Changping Peng, Zhangang Lin, Ching Law

Information Retrieval

Challenge:	Existing query classification methods rely on posterior click behavior to construct training samples, resulting in insufficient prior information for modeling.
Approach:	They propose a semi-supervised scaleable unified framework that integrates enhanced modules to unify query classification tasks.
Outcome:	The proposed framework outperforms the state-of-the-art models in offline and online A/B experiments.

Aligning Large Language Models with Implicit Preferences from User-Generated Content (2025.acl-long)

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Zhaoxuan Tan, Zheng Li, Tianyi Liu, Haodong Wang, Hyokun Yun, Ming Zeng, Pei Chen, Zhihan Zhang, Yifan Gao, Ruijie Wang, Priyanka Nigam, Bing Yin, Meng Jiang

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

Challenge:	Existing preference learning methods rely heavily on curated data from humans or advanced LLMs, which is costly and difficult to scale.
Approach:	They propose a framework that leverages implicit preferences in unlabeled user-generated content to generate preference data.
Outcome:	The proposed framework transforms user-generated content into user queries and generates responses from the policy model.

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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

Dialogue Systems & Conversational Agents

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)

Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval (2022.emnlp-main)

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Houxing Ren, Linjun Shou, Ning Wu, Ming Gong, Daxin Jiang

Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling (2022.naacl-main)

Challenge:	Existing methods to distill knowledge from cross-encoder re-ranker to dual-encoding retriever are lacking in the cross-lingual setting.
Approach:	They propose to use a query generator as the teacher in the cross-lingual setting to distill knowledge to a dual-encoder retrieval model.
Outcome:	The proposed method outperforms state-of-the-art methods on two benchmark datasets.

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Nuo Chen, Linjun Shou, Ming Gong, Jian Pei, Daxin Jiang

Reaction Miner: An Integrated System for Chemical Reaction Extraction from Textual Data (2023.emnlp-demo)

Challenge:	Existing methods to train cross-lingual pre-trained language models have shown great success in cross-linguistic sequence labeling tasks.
Approach:	They propose a cross-lingual language informative span masking task to eliminate the objective gap between pre-training and fine-tuning stages.
Outcome:	The proposed method surpasses the state-of-the-art methods on multiple benchmarks even with limited pre-training data.

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Ming Zhong, Siru Ouyang, Yizhu Jiao, Priyanka Kargupta, Leo Luo, Yanzhen Shen, Bobby Zhou, Xianrui Zhong, Xuan Liu, Hongxiang Li, Jinfeng Xiao, Minhao Jiang, Vivian Hu, Xuan Wang, Heng Ji, Martin Burke, Huimin Zhao, Jiawei Han

Towards Robust Few-Shot Relation Classification: Incorporating Relation Description with Agreement (2025.findings-emnlp)

Challenge:	Reaction Miner is a system designed to extract chemical reactions from raw scientific PDFs.
Approach:	They propose a system that extracts chemical reactions directly from raw scientific PDFs.
Outcome:	The proposed system can extract chemical reactions from raw scientific PDFs.

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Mengting Hu, Jianfeng Wu, Ming Jiang, Yalan Xie, Zhunheng Wang, Rui Ying, Xiaoyi Liu, Ruixuan Xu, Hang Gao, Renhong Cheng

PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts (2025.findings-acl)

Challenge:	Existing approaches to recognize relational relationships with a few support samples are limited for unlimited queries.
Approach:	They propose a simple but effective framework that uses relation descriptions as external knowledge to enhance the model’s comprehension of the relation semantics.
Outcome:	The proposed framework outperforms strong baselines while being robust against various NOTA rates.

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Ming Zhang, Yuhui Wang, Yujiong Shen, Tingyi Yang, Changhao Jiang, Yilong Wu, Shihan Dou, Qinhao Chen, Zhiheng Xi, Zhihao Zhang, Yi Dong, Zhen Wang, Zhihui Fei, Mingyang Wan, Tao Liang, Guojun Ma, Qi Zhang, Tao Gui, Xuanjing Huang

Dialogue Systems & Conversational Agents

Challenge:	Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, but they struggle to solve strictly constrained dialogue tasks.
Approach:	They construct a dataset that contains 12,705 high-quality Chinese dialogue instructions from 440 flowcharts containing 5,055 process nodes.
Outcome:	The proposed model outperforms GPT-4o models on backward transitions and outperformed GPT-42 models on the same dataset.

Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension (2020.acl-main)

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Fei Yuan, Linjun Shou, Xuanyu Bai, Ming Gong, Yaobo Liang, Nan Duan, Yan Fu, Daxin Jiang

Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text (2022.findings-acl)

Challenge:	Existing approaches to improve machine reading comprehension performance on low resource languages are limited due to the lack of sufficient training data.
Approach:	They propose to use a mixed MRC task to translate the question to other languages and build cross-lingual question-passage pairs.
Outcome:	The proposed task improves on two cross-lingual MRC datasets.

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Siyuan Wang, Wanjun Zhong, Duyu Tang, Zhongyu Wei, Zhihao Fan, Daxin Jiang, Ming Zhou, Nan Duan

A Survey of RAG-Reasoning Systems in Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods for logical reasoning of text focus on contextual semantics while struggling to explicitly model the logical inference process.
Approach:	They propose a logic-driven context extension framework and a data-driven augmentation algorithm that uses contrastive learning to better capture logical information.
Outcome:	The proposed framework outperforms existing methods on two benchmark datasets, ReClor and LogiQA.

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Yangning Li, Weizhi Zhang, Yuyao Yang, Wei-Chieh Huang, Yaozu Wu, Junyu Luo, Yuanchen Bei, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Chunkit Chan, Yankai Chen, Zhongfen Deng, Yinghui Li, Hai-Tao Zheng, Dongyuan Li, Renhe Jiang, Ming Zhang, Yangqiu Song, Philip S. Yu

EGSS: Entropy-guided Stepwise Scaling for Reliable Software Engineering (2026.acl-long)

Challenge:	a survey of RAG-based reasoning-based approaches shows that it is not effective for multi-step inferences.
Approach:	They map how advanced reasoning optimizes each stage of RAG . they show how retrieved knowledge supply missing premises and expand context for complex inference .
Outcome:	The proposed frameworks achieve state-of-the-art across knowledge-intensive benchmarks.

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Chenhui Mao, Yuanting Lei, Zhixiang Wei, Ming Liang, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li

Green & Sustainable NLP

Challenge:	Entropy-Guided Stepwise Scaling (EGSS) is a novel TTS framework for software engineering tasks.
Approach:	They propose an entropy-guided stepwise scaling framework that balances efficiency and effectiveness through entropic-guide encoding and robust test-suite augmentation.
Outcome:	EGSS boosts performance by 5–10% across all evaluated models, and reduces inference-time token usage by over 28% . compared to existing methods, EGS reduces token usage and reduce inference time by over 20% .

No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension (2020.findings-emnlp)

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Xuguang Wang, Linjun Shou, Ming Gong, Nan Duan, Daxin Jiang

Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion (2024.acl-long)

Challenge:	Natural Questions (NQ) benchmark sets new challenges for machine reading comprehension.
Approach:	They propose a novel approach to handle all answer types systematically using a two-step training procedure.
Outcome:	The proposed approach achieved the top 1 on both long and short answer leaderboards with F1 scores of 77.2 and 64.1.

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Rui Ying, Mengting Hu, Jianfeng Wu, Yalan Xie, Xiaoyi Liu, Zhunheng Wang, Ming Jiang, Hang Gao, Linlin Zhang, Renhong Cheng

TRUST: Towards Robust Social Bot Detection via Uncertainty-Guided Pseudo-Labeling and Graph Structure Purification (2026.findings-acl)

Challenge:	Current methods embed factual knowledge into continuous vector space and apply geometric operations to learn potential patterns in temporal knowledge graphs.
Approach:	They propose a temporal knowledge graph completion method that uses two geometric operations to learn missing facts in temporal graphs.
Outcome:	The proposed method significantly outperforms existing temporal knowledge graph embedding models.

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Ruixuan Xu, Mengting Hu, Zhunheng Wang, Ming Jiang, Rui Ying, Zhen Zhang, Hang Gao, Shuaipeng Liu, Renhong Cheng

LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models (2026.acl-long)

Challenge:	Existing graph-based detection models are vulnerable to deceptive message propagation, where bots deliberately interact with legitimate users.
Approach:	They propose a framework to mitigate deceptive message propagation by node-level uncertainty estimation and graph structure purification.
Outcome:	The proposed framework improves on three benchmark datasets and six GNN backbones on real-world social bots.

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Ming Zhang, Yujiong Shen, Jingyi Deng, Yuhui Wang, Huayu Sha, Kexin Tan, Qiyuan Peng, Yue Zhang, Junzhe Wang, Shichun Liu, Yueyuan Huang, Jingqi Tong, Changhao Jiang, Yilong Wu, Zhihao Zhang, Mingqi Wu, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang

Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding (2025.findings-naacl)

Challenge:	Existing evaluation of Large Language Models on static benchmarks is vulnerable to data contamination and leaderboard overfitting.
Approach:	LLMEval-Fair framework provides a framework for dynamic evaluation of Large Language Models . evaluators use a proprietary bank of 220k graduate-level questions to analyze model data .
Outcome:	LLMEval-Fair provides robust and credible evaluation framework for Large Language Models . it provides a strong empirical validation for the dynamic evaluation paradigm .

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Xingjian Diao, Chunhui Zhang, Weiyi Wu, Zhongyu Ouyang, Peijun Qing, Ming Cheng, Soroush Vosoughi, Jiang Gui

From Specific-MLLMs to Omni-MLLMs: A Survey on MLLMs Aligned with Multi-modalities (2025.findings-acl)

Challenge:	Multimodal foundation models have demonstrated significant success in tasks such as visual captioning, question answering, and image-text retrieval.
Approach:	They propose a specialized cognitive module, temporal working memory, which selectively retains task-relevant information across temporal dimensions.
Outcome:	The module retains task-relevant information across temporal dimensions, ensuring that critical details are preserved throughout the processing of video and audio content.

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Shixin Jiang, Jiafeng Liang, Jiyuan Wang, Xuan Dong, Heng Chang, Weijiang Yu, Jinhua Du, Ming Liu, Bing Qin

Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval (2022.findings-emnlp)

Challenge:	MLLMs are able to integrate multiple modalities into a single model to tackle complex tasks in real-world scenarios.
Approach:	They propose a comprehensive survey of Omni-MLLMs to address the challenges and opportunities of multimodal modeling.
Outcome:	The proposed model can integrate multiple modalities into a single model and provide novel perspectives.

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Houxing Ren, Linjun Shou, Jian Pei, Ning Wu, Ming Gong, Daxin Jiang

Temperature-Centric Investigation of Speculative Decoding with Knowledge Distillation (2024.findings-emnlp)

Challenge:	Recent multilingual pre-trained models perform poorly on multilingual retrieval tasks due to lack of multilingual training data.
Approach:	They propose to mine and generate self-supervised training data based on large-scale unlabeled corpus and introduce query generator to generate more queries in target languages for unlabed passages.
Outcome:	The proposed method performs better than baselines on a Mr. TYDI dataset and an industrial dataset from a commercial search engine.

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Siru Ouyang, Shuohang Wang, Minhao Jiang, Ming Zhong, Donghan Yu, Jiawei Han, Yelong Shen

Benchmarking Machine Translation with Cultural Awareness (2024.findings-emnlp)

Challenge:	Speculative decoding is a novel method to expedite inference in autoregressive (large) language models.
Approach:	They propose to use a smaller model as a draft model to speculate a block of tokens, which the target model then evaluates for acceptance.
Outcome:	The proposed method can be used to accelerate inference in autoregressive (large) language models by using smaller models as draft models to speculate tokens for multiple inference steps.

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Binwei Yao, Ming Jiang, Tara Bobinac, Diyi Yang, Junjie Hu

Machine Translation

Challenge:	Existing studies on terminology translation focus on breaking language barriers rather than cultural barriers.
Approach:	They propose a parallel corpus enriched with CSI annotations in 6 language pairs for investigating Cultural-Aware Machine Translation.
Outcome:	The proposed corpus is enriched with CSI annotations in 6 languages and measures translation quality.

ReactIE: Enhancing Chemical Reaction Extraction with Weak Supervision (2023.findings-acl)

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Ming Zhong, Siru Ouyang, Minhao Jiang, Vivian Hu, Yizhu Jiao, Xuan Wang, Jiawei Han

Learning Sparsity for Effective and Efficient Music Performance Question Answering (2025.acl-short)

Challenge:	Structured chemical reaction information is a vital tool for chemists engaged in laboratory work and advanced endeavors such as computer-aided drug design.
Approach:	They propose a method which utilizes frequent patterns within the text as linguistic cues to identify specific characteristics of chemical reactions.
Outcome:	The proposed model outperforms baselines and outperformed existing models.

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Xingjian Diao, Tianzhen Yang, Chunhui Zhang, Weiyi Wu, Ming Cheng, Jiang Gui

A Graph Representation of Semi-structured Data for Web Question Answering (2020.coling-main)

Challenge:	Existing Music AVQA methods rely on dense and unoptimized representations, leading to inefficiencies in the isolation of key information, reduction of redundancy, and prioritization of critical samples.
Approach:	They propose a sparse learning framework specifically designed for Music AVQA to address these challenges.
Outcome:	The proposed framework reduces training time by 28.32% while maintaining accuracy while maintaining state-of-the-art performance on the Music AVQA datasets.

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Xingyao Zhang, Linjun Shou, Jian Pei, Ming Gong, Lijie Wen, Daxin Jiang

Structured Data in NLP

Challenge:	Existing studies treat semi-structured data as flat documents with pieces of text . semi-structural data is more effective to represent rich relational information . question answering is an important feature in most search engines .
Approach:	They propose a graph representation of Web tables and lists based on categorization of components and their relations . they also develop reasoning techniques on the graph model for the question answering task .
Outcome:	The proposed graph improves F1 score by 3.90 points over the state-of-the-art baselines on real datasets.

Efficient Transformer Parameter Reuse via Zero-Token Mechanism (2026.findings-acl)

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Guanghao Li, Wenhao Jiang, Li Shen, Ming Tang, Chun Yuan

EMTIR-GRPO: Efficient Multi-Tool Augmented Large Language Models via Reinforcement Learning (2026.findings-acl)

Challenge:	Existing approaches to scaling up parameter counts are impractical for users with limited computational resources.
Approach:	They propose a decoupled parameter cycling strategy that employs a head-tail decoupling strategy to decouple the first (head) and last (tail) layers from the parameter cycling process.
Outcome:	The proposed approach achieves superior performance under strict parameter constraints and significantly reduces computational overhead via early exits.

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Shixin Jiang, Zhihao Zhu, Jiafeng Liang, Yang Wu, Ming Liu, Bing Qin

SciEvent: Benchmarking Multi-domain Scientific Event Extraction (2025.emnlp-main)

Challenge:	Tool-integrated reasoning (TIR) enables large language models to invoke external tools for tasks beyond their internal capacity but often suffers from tool overuse.
Approach:	They propose an algorithm that uses a composite reward to model tool costs and tool efficiency.
Outcome:	The proposed algorithm models heterogeneous tool costs and encourages more cost-effective tool-use strategies.

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Bofu Dong, Pritesh Shah, Sumedh Sonawane, Tiyasha Banerjee, Erin Brady, Xinya Du, Ming Jiang

ECom-Bench: Can LLM Agent Resolve Real-World E-commerce Customer Support Issues? (2025.emnlp-industry)

Challenge:	Existing work on scientific information extraction relies on entity-relation extraction in narrow domains . current models struggle in domains such as sociology and humanities .
Approach:	They propose a multi-domain benchmark for scientific abstract annotations using a unified event extraction schema.
Outcome:	The proposed benchmark includes 500 abstracts across five research domains with manual annotations of event segments, triggers, and fine-grained arguments.

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Haoxin Wang, Xianhan Peng, Huang Cheng, Yizhe Huang, Ming Gong, Chenghan Yang, Yang Liu, Jiang Lin

Challenge:	ECom-Bench is a benchmark framework for evaluating LLM agent with multimodal capabilities in e-commerce customer support domain.
Approach:	They introduce a benchmark framework for evaluating LLM agent with multimodal capabilities in the e-commerce customer support domain.
Outcome:	The proposed benchmark features dynamic user simulation based on persona information from real e-commerce customer interactions and a realistic task dataset derived from authentic ecommerce dialogues.

Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding (2022.emnlp-main)

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Shining Liang, Linjun Shou, Jian Pei, Ming Gong, Wanli Zuo, Xianglin Zuo, Daxin Jiang

Multi-View Document Representation Learning for Open-Domain Dense Retrieval (2022.acl-long)

Challenge:	Existing approaches to translate spoken language understanding into low-resource languages are limited to implicit alignment and disregard the inherent semantic structure in SLU.
Approach:	They propose to model utterance-slot-word structure by a multi-level contrastive learning framework . they also propose a label-aware joint model leveraging label semantics to enhance alignment .
Outcome:	The proposed model improves performance on two zero-shot cross-lingual datasets.

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Shunyu Zhang, Yaobo Liang, Ming Gong, Daxin Jiang, Nan Duan

Representation Learning

Challenge:	Existing methods for dense retrieval are hard to match with multiple views.
Approach:	They propose a multi-view document representation learning framework to generate multiple embeddings through viewers to represent documents and enforce them to align with different queries.
Outcome:	The proposed method outperforms recent works and achieves state-of-the-art results.

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

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

Music Audio-Visual Question Answering Requires Specialized Multimodal Designs (2026.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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Wenhao You, Xingjian Diao, Wenjun Huang, Chunhui Zhang, Keyi Kong, Weiyi Wu, Chiyu Ma, Zhongyu Ouyang, Tingxuan Wu, Ming Cheng, Soroush Vosoughi, Jiang Gui

Generate & Rank: A Multi-task Framework for Math Word Problems (2021.findings-emnlp)

Challenge:	Music audio-visual question answering presents unique challenges with dense audio-visual content, intricate temporal dynamics, and the need for domain-specific knowledge.
Approach:	They analyze Music AVQA datasets and analyze their results to identify key design patterns . they propose concrete future directions for incorporating musical priors .
Outcome:	The proposed architectures are critical for success in Music AVQA, the authors argue . they suggest concrete future directions for incorporating musical priors .

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Jianhao Shen, Yichun Yin, Lin Li, Lifeng Shang, Xin Jiang, Ming Zhang, Qun Liu

GLGE: A New General Language Generation Evaluation Benchmark (2021.findings-acl)

Challenge:	Existing studies formalize MWP as a generation task but mathematical expressions are prone to minor mistakes.
Approach:	They propose a ranking task for math word problem (MWP) that learns from its own mistakes and distinguishes between correct and incorrect expressions.
Outcome:	The proposed model outperforms baselines on the classical Math23k dataset and is 7% higher than the state-of-the-art.

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Dayiheng Liu, Yu Yan, Yeyun Gong, Weizhen Qi, Hang Zhang, Jian Jiao, Weizhu Chen, Jie Fu, Linjun Shou, Ming Gong, Pengcheng Wang, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Ruofei Zhang, Winnie Wu, Ming Zhou, Nan Duan

Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)

Challenge:	Multi-task benchmarks focus on a range of Natural Language Understanding (NLU) tasks without considering the Natural Language Generation (NLG) models.
Approach:	They propose a multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks.
Outcome:	The proposed benchmarks are based on GLUE and Su-perGLUE for English and several other languages.

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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David Clifton

Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA (2026.acl-industry)

Challenge:	Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options.
Approach:	They construct a benchmark to better understand large language models in the clinic . they use existing datasets to evaluate LLMs in clinical situations .
Outcome:	The proposed model outperforms human experts in multiple medical tasks.

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Wenwei Li, Ming Xu, Tianle Xia, Lingxiang Hu, Yiding Sun, Linfang Shang, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

Structured Data in NLP

Challenge:	Existing methods for QA in industrial environments are inherently relational and often updated.
Approach:	They propose a framework that optimizes retrieval and generation through two components: Graph-aware Retrieval and evidence-constrained reinforcement learning.
Outcome:	Experiments on an internal advertising QA dataset show consistent gains across expert-judged dimensions including accuracy, completeness, safety, and URL validity.

CodeBERT: A Pre-Trained Model for Programming and Natural Languages (2020.findings-emnlp)

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Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou

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

Challenge:	Large pre-trained models have improved performance on a variety of natural language processing tasks.
Approach:	They develop a bimodal pre-trained model for programming language (PL) and natural language (NL) it incorporates a hybrid objective function that detects replaced tokens from generators.
Outcome:	The proposed model performs better on two NL-PL applications by fine-tuning model parameters.

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

Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation (2020.coling-main)

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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Junhao Liu, Linjun Shou, Jian Pei, Ming Gong, Min Yang, Daxin Jiang

LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation (2025.findings-emnlp)

Challenge:	Cross-lingual Machine Reading Comprehension (CLMRC) is a challenging problem due to the lack of large-scale annotated datasets in low-source languages, such as Arabic, Hindi, and Vietnamese.
Approach:	They propose a novel approach to augment cross-lingual machine reading comprehension by combining knowledge from multiple language branch models into a single model for all target languages.
Outcome:	Extensive experiments on two CLMRC benchmarks show the proposed method is effective and robust to data noises.

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Ming Zhang, Yujiong Shen, Zelin Li, Huayu Sha, Binze Hu, Yuhui Wang, Chenhao Huang, Shichun Liu, Jingqi Tong, Changhao Jiang, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang

BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation (2022.naacl-main)

Challenge:	Current medical benchmarks have limitations in question design, data sources and evaluation methods.
Approach:	They propose a new benchmark covering five core medical areas . it includes 2,996 questions created from real-world electronic health records .
Outcome:	The proposed model covers five core medical areas and includes 2,996 questions created from real-world electronic health records and expert-designed clinical scenarios.

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Yuchen Jiang, Tianyu Liu, Shuming Ma, Dongdong Zhang, Jian Yang, Haoyang Huang, Rico Sennrich, Ryan Cotterell, Mrinmaya Sachan, Ming Zhou

From Heads to Neurons: Causal Attribution and Steering in Multi-Task Vision–Language Models (2026.findings-acl)

Challenge:	Standard evaluation metrics, e.g., BLEU, TER and METEOR, focus on the quality of translations at the sentence level and do not consider discourse-level features.
Approach:	They propose to use a metric to take discourse coherence into consideration by categorizing discourse-related spans and calculating the similarity-based F1 measure of categorized spans.
Outcome:	The proposed metric possesses better selectivity and interpretability at the document-level, and is more sensitive to document- level nuances.

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Qidong Wang, Junjie Hu, Ming Jiang

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

Challenge:	Existing models focus on single tasks, limiting comparability of neuron importance . ranking strategies overlook how task-dependent information pathways shape write-in effects of feed-forward network (FFN) neurons.
Approach:	They propose a gradient-free framework for task-aware neuron attribution and steering in multi-task vision-language models.
Outcome:	The proposed framework outperforms existing methods in identifying task-critical neurons and improves model performance after steering.

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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

Syntax-Enhanced Pre-trained Model (2021.acl-long)

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.

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Zenan Xu, Daya Guo, Duyu Tang, Qinliang Su, Linjun Shou, Ming Gong, Wanjun Zhong, Xiaojun Quan, Daxin Jiang, Nan Duan

Syntactic Text Processing

Challenge:	Existing methods that use syntax of text in pre-training and fine-tuning suffer from discrepancy between the two stages.
Approach:	They propose a model that utilizes the syntactic structure of text in pre-training and fine-tuning stages.
Outcome:	The proposed model achieves state-of-the-art on six public benchmark datasets.

Learning Musical Representations for Music Performance Question Answering (2024.findings-emnlp)

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Xingjian Diao, Chunhui Zhang, Tingxuan Wu, Ming Cheng, Zhongyu Ouyang, Weiyi Wu, Jiang Gui

Structural Contrastive Pretraining for Cross-Lingual Comprehension (2023.findings-acl)

Challenge:	Existing methods for audio-visual learning fail to consider the distinctive characteristics of instruments and music.
Approach:	They propose to integrate multimodal interactions within the context of music data and annotate and release rhythmic and music sources in the current music datasets to enable the model to learn music characteristics.
Outcome:	The proposed model can learn music characteristics from the current music datasets and align its predictions with the temporal dimension.

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Nuo Chen, Linjun Shou, Tengtao Song, Ming Gong, Jian Pei, Jianhui Chang, Daxin Jiang, Jia Li

V-SEAM: Visual Semantic Editing and Attention Modulating for Causal Interpretability of Vision-Language Models (2025.emnlp-main)

Challenge:	Existing methods to train multilingual language models using pretraining tasks like mask language modeling have yielded promising results on a wide range of downstream tasks.
Approach:	They propose a new task to align the structural words in a parallel sentence, enhancing models’ ability to comprehend cross-lingual representations.
Outcome:	The proposed task improves model's ability to comprehend cross-lingual representations by increasing the frequency of negative pairings.

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Qidong Wang, Junjie Hu, Ming Jiang

Explainability & Interpretability in NLP

Challenge:	Existing work on causal interpretability focuses on large language models (LLMs) but internal mechanisms of vision-language models remain underexplored, authors say .
Approach:	They introduce a framework that combines visual and semantic manipulations for causal interpretation of vision-language models.
Outcome:	The proposed framework shows improved performance for LLAVA and InstructBLIP on three diverse benchmarks.

NeuronBlocks: Building Your NLP DNN Models Like Playing Lego (D19-3)

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Ming Gong, Linjun Shou, Wutao Lin, Zhijie Sang, Quanjia Yan, Ze Yang, Feixiang Cheng, Daxin Jiang

Challenge:	Deep Neural Networks (DNN) have been widely employed in industry to address various natural language processing tasks.
Approach:	They propose an NLP toolkit that encapsulates neural network modules as building blocks to construct various DNN models with complex architecture.
Outcome:	The proposed toolkit can build, train, and test various DNN models with complex architecture.

Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency (2023.findings-emnlp)

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Zilin Xiao, Linjun Shou, Xingyao Zhang, Jie Wu, Ming Gong, Daxin Jiang

Joint Type Inference on Entities and Relations via Graph Convolutional Networks (P19-1)

Challenge:	Existing entity disambiguation methods struggle to capture explicit discourse-level dependencies, resulting in incoherent predictions at the abstract level.
Approach:	They propose an unsupervised variational autoencoder to extract latent topic vectors of context sentences to enhance coherence of entity predictions.
Outcome:	The proposed system achieves state-of-the-art on popular ED benchmarks with an average improvement of 1.3 F1 points.

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Changzhi Sun, Yeyun Gong, Yuanbin Wu, Ming Gong, Daxin Jiang, Man Lan, Shiliang Sun, Nan Duan

Structured Data in NLP

Challenge:	a novel graph convolutional network (GCN) is proposed for the task of joint entity relation extraction.
Approach:	They propose a graph convolutional network running on an entity-relation bipartite graph . they propose combining two different methods to perform joint entity relation extraction .
Outcome:	The proposed model outperforms existing joint models in entity performance and is competitive with the state-of-the-art in relation performance.

Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training (2026.acl-industry)

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Tianle Xia, Ming Xu, Lingxiang Hu, Yiding Sun, Wenwei Li, Linfang Shang, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network (2020.acl-main)

Challenge:	Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, yet traditional singleround retrieval struggles with complex multistep reasoning.
Approach:	They propose a framework that introduces path-centric reward shaping for agentic RAG training.
Outcome:	The proposed framework improves on existing methods with an average accuracy gain of 7.7 points.

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Wanjun Zhong, Duyu Tang, Zhangyin Feng, Nan Duan, Ming Zhou, Ming Gong, Linjun Shou, Daxin Jiang, Jiahai Wang, Jian Yin

Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding (2021.emnlp-main)

Challenge:	Existing methods for fact checking textual statements are not yet available.
Approach:	They propose a neural network approach capable of leveraging logical operations for fact checking . they use a textual statement and semi-structured tables to generate a program from it .
Outcome:	The proposed approach achieves state-of-the-art performance on TABFACT dataset . it derives a program (a.k.a. logical form) of the statement in semantic parsing manner .

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Yingmei Guo, Linjun Shou, Jian Pei, Ming Gong, Mingxing Xu, Zhiyong Wu, Daxin Jiang

Enhancing Multimodal Named Entity Recognition through Adaptive Mixup Image Augmentation (2025.coling-main)

Challenge:	Existing approaches to scale out spoken language understanding to low-resource languages are noisy.
Approach:	They propose a method for mitigating noise in augmented data by training models with augmented datasets.
Outcome:	The proposed method outperforms state-of-the-art methods on two benchmark datasets.

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Bo Xu, Haiqi Jiang, Jie Wei, Hongyu Jing, Ming Du, Hui Song, Hongya Wang, Yanghua Xiao

TIGEr: Text-to-Image Grounding for Image Caption Evaluation (D19-1)

Challenge:	Current named entity recognition methods struggle with text-image mismatch problem due to a lack of visual context.
Approach:	They propose an adaptive mixup image augmentation method that generates augmented images based on matching score between text and image .
Outcome:	The proposed method can be integrated into existing models and demonstrate consistent performance improvements.

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Ming Jiang, Qiuyuan Huang, Lei Zhang, Xin Wang, Pengchuan Zhang, Zhe Gan, Jana Diesner, Jianfeng Gao

Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension (2020.acl-main)

Challenge:	Existing metrics based on text-level comparisons fail to assess the quality of captions produced by machines.
Approach:	They propose to use a machine-learned text-image grounding model to measure the accuracy of machine-generated captions and their correlation with human judgments.
Outcome:	The proposed metric has higher consistency with human judgments and is more accurate than existing metrics.

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Bo Zheng, Haoyang Wen, Yaobo Liang, Nan Duan, Wanxiang Che, Daxin Jiang, Ming Zhou, Ting Liu

ProtoVQA: An Adaptable Prototypical Framework for Explainable Fine-Grained Visual Question Answering (2025.emnlp-main)

Challenge:	Existing approaches to machine reading comprehension treat documents at their hierarchical nature, ignoring their dependencies.
Approach:	They propose a machine reading comprehension benchmark with two-grained answers . they use graph attention networks to model documents at their hierarchical nature .
Outcome:	The proposed framework outperforms existing systems at long and short answer criteria.

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Xingjian Diao, Weiyi Wu, Keyi Kong, Peijun Qing, Xinwen Xu, Ming Cheng, Soroush Vosoughi, Jiang Gui

Question Answering

Challenge:	Visual Question Answering (VQA) is increasingly used in diverse applications where models must provide accurate answers and explanations that humans can easily understand and verify.
Approach:	They propose a unified prototypical framework that learns question-aware prototypes that serve as reasoning anchors and applies spatially constrained matching to ensure that the selected evidence is coherent and semantically relevant.
Outcome:	The proposed framework yields faithful, fine-grained explanations while maintaining competitive accuracy.

REO-Relevance, Extraness, Omission: A Fine-grained Evaluation for Image Captioning (D19-1)

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Ming Jiang, Junjie Hu, Qiuyuan Huang, Lei Zhang, Jana Diesner, Jianfeng Gao

Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection (2020.emnlp-main)

Challenge:	Existing metrics for image captioning evaluation provide an overall quality score, which is difficult to infer specific description errors.
Approach:	They propose a fine-grained evaluation method REO for automatically measuring the performance of image captioning systems.
Outcome:	The proposed method achieves higher consistency with human judgments and provides more intuitive evaluation results than other metrics.

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Ruize Wang, Duyu Tang, Nan Duan, Wanjun Zhong, Zhongyu Wei, Xuanjing Huang, Daxin Jiang, Ming Zhou

Challenge:	Existing methods for fine-grained propaganda detection are not based on input-output data, but instead use declarative knowledge to detect propagandistic text fragments.
Approach:	They propose a method to inject declarative knowledge of fine-grained propaganda techniques into training data to get better representations of propagandistic texts.
Outcome:	The proposed method achieves superior performance on a large dataset for propaganda detection.

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

TRIPS: Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection (2022.emnlp-main)

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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Chaoya Jiang, Haiyang Xu, Chenliang Li, Ming Yan, Wei Ye, Shikun Zhang, Bin Bi, Songfang Huang

Visual Data in NLP

Challenge:	Existing vision-and-language pre-training models suffer from long visual sequences . experimental results show that TRIPS gains a speedup of 40% over previous similar VLP models .
Approach:	They propose an efficient vision-and-language pre-training model with text-relevant image patch selection, TRIPS, which reduces the visual sequence progressively with a text-guided patch-selection layer in the visual backbone for efficient training and inference.
Outcome:	The proposed model can speed up training and inference by 40% over previous models.

Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA (2022.findings-emnlp)

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Junjie Huang, Wanjun Zhong, Qian Liu, Ming Gong, Daxin Jiang, Nan Duan

Investigating and Enhancing the Robustness of Large Multimodal Models Against Temporal Inconsistency (2025.acl-long)

Challenge:	Existing methods to retrieve evidences from corpus are difficult due to table-text discrepancy and data sparsity problem.
Approach:	They propose an optimized OpenQA Table-Text Retriever to retrieve tabular and textual evidences from tabular resources.
Outcome:	The proposed OpenQA Table-Text Retriever significantly outperforms existing methods on QA tasks.

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Jiafeng Liang, Shixin Jiang, Xuan Dong, Ning Wang, Zheng Chu, Hui Su, Jinlan Fu, Ming Liu, See-Kiong Ng, Bing Qin

Robustness in NLP

Challenge:	Large Multimodal Models (LMMs) have demonstrated impressive performance on general video comprehension benchmarks, but their robustness needs to be thoroughly investigated for broader applications.
Approach:	They propose a temporal robustness benchmark which introduces temporal inconsistency perturbations separately at the visual and textual modalities to assess the robustness of models.
Outcome:	The proposed method improves the model’s robustness and reliability in temporal analysis.

CPopQA: Ranking Cultural Concept Popularity by LLMs (2024.naacl-short)

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Ming Jiang, Mansi Joshi

The Digital Dunning-Kruger Effect: Decoupling Hallucinations via Geometric Hidden-state Observation for Semantic Truthfulness (2026.acl-long)

Challenge:	Existing studies on the knowledge capacity of large language models (LLMs) focus on knowledge explicitly learned from pretraining data or implicitly inferable from similar contexts.
Approach:	They propose a few-shot question-answering task that examines LLMs’ statistical ranking abilities for long-tail cultural concepts.
Outcome:	The proposed task examines LLMs’ statistical ranking abilities for long-tail cultural concepts, particularly in the United States and the United Kingdom.

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Yueheng Mao, Min Yu, Gengwang Li, Jianguo Jiang, Gang Li, Meng Zhang, Zhen Xu, Weiqing Huang, Ming Liu

Instructed Language Models with Retrievers Are Powerful Entity Linkers (2023.emnlp-main)

Challenge:	Large Language Models (LLMs) often generate overconfident yet factually incorrect hallucinations.
Approach:	They propose a black-box-based framework that captures stubborn hallucinations by integrating internal geometric dynamics with output probability distributions.
Outcome:	The proposed framework outperforms white-box methods and reduces computational overhead by over 90%.

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Zilin Xiao, Ming Gong, Jie Wu, Xingyao Zhang, Linjun Shou, Daxin Jiang

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

Challenge:	Generative approaches powered by large language models have demonstrated emergent abilities in tasks that require complex reasoning abilities.
Approach:	They propose a sequence-to-sequence training objective with instruction-tuning that enables casual language models to perform entity linking over knowledge bases.
Outcome:	The proposed framework outperforms existing approaches with +6.8 F1 points gain on average and huge advantage in training data efficiency and compute consumption.

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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

Infrared-LLaVA: Enhancing Understanding of Infrared Images in Multi-Modal Large Language Models (2024.findings-emnlp)

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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Shixin Jiang, Zerui Chen, Jiafeng Liang, Yanyan Zhao, Ming Liu, Bing Qin

ECoK: Emotional Commonsense Knowledge Graph for Mining Emotional Gold (2024.findings-acl)

Challenge:	Existing methods for infrared modeling ignore supervisory signals of infra-modality-specific attributes, which may lead to biased understanding of in-frarea images.
Approach:	They propose a multi-agent generation system which transfers knowledge from visible images to generate infrared image-text pairs and infra-instructional data.
Outcome:	The proposed system generates infrared image-text pairs and infra-response data and is able to answer common infreas tasks with the proposed model.

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Zhunheng Wang, Xiaoyi Liu, Mengting Hu, Rui Ying, Ming Jiang, Jianfeng Wu, Yalan Xie, Hang Gao, Renhong Cheng