Papers by Zhao Jin
Evaluating Answer Leakage Robustness of LLM Tutors against Adversarial Student Attacks (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are increasingly used in education, yet their default usefulness conflicts with pedagogical principles. |
| Approach: | They propose an adversarial student agent that they fine-tune to jailbreak LLM-based tutors and propose a benchmark to evaluate tutor robustness. |
| Outcome: | The proposed model fine-tunes to jailbreak LLM-based tutors, and shows that they perform well under adversarial student attacks. |
Prior Knowledge and Memory Enriched Transformer for Sign Language Translation (2022.findings-acl)
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| Challenge: | Existing methods for sign language translation do not explore all of them . visual and textual understanding and additional prior knowledge learning are challenging . |
| Approach: | They propose a method which integrates auxiliary information into vanilla transformer for SLT . they use visual-textual context information and additional auxiliary knowledge of a word . |
| Outcome: | The proposed method improves the understanding of sign language videos with visual and textual understanding and additional prior knowledge learning. |
Agent-RewardBench: Towards a Unified Benchmark for Reward Modeling across Perception, Planning, and Safety in Real-World Multimodal Agents (2025.acl-long)
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| Challenge: | Multimodal Large Language Models (MLLMs) are developing but lack external feedback . there is no clear on how to select reward models for agents . |
| Approach: | They propose a benchmark to evaluate agent reward modeling ability in MLLMs . they use multiple dimensions and real-world agent scenarios evaluation . |
| Outcome: | The proposed benchmark evaluates agent performance in multimodal large language models . it covers perception, planning, and safety with 7 scenarios and is highly difficult and high-quality . |
OpenSR: Open-Modality Speech Recognition via Maintaining Multi-Modality Alignment (2023.acl-long)
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| Challenge: | Speech Recognition often gets stuck in the lack of new domain utterances when training a model of new-domain speech. |
| Approach: | They propose a training system Open-modality Speech Recognition that enables zero-shot modality transfer . they use multi-modal alignment in phoneme space to maintain multi-modality alignment . |
| Outcome: | The proposed system achieves zero-shot modality transfer compared to existing methods . it achieves state-of-the-art performance on audio-visual speech recognition and lip-reading with 2.7% and 25.0%, respectively. |
Semantic-conditioned Dual Adaptation for Cross-domain Query-based Visual Segmentation (2023.findings-acl)
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| Challenge: | Existing approaches to visual segmentation from language queries require expensive labeling and degradation when deployed to an unseen domain. |
| Approach: | They propose a task to adapt a visual segmentation model from a labeled domain to an unseen domain. |
| Outcome: | The proposed framework achieves precise feature- and relation-invariant across domains via universal semantic structure. |
WavRAG: Audio-Integrated Retrieval Augmented Generation for Spoken Dialogue Models (2025.acl-long)
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| Challenge: | Existing RAG frameworks rely on Automatic Speech Recognition to process speech input, which discards crucial audio information and increases computational overhead. |
| Approach: | They propose a retrieval augmented generation framework with native, end-to-end audio support that integrates audio and text into a unified knowledge representation. |
| Outcome: | The proposed framework can perform 10x faster than current pipelines while delivering 10x acceleration. |
Media Attitude Detection via Framing Analysis with Events and their Relations (2024.emnlp-main)
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| Challenge: | a recent study examined the effects of media framing on public perception and understanding of news articles. |
| Approach: | They propose to extract framing devices employed by media to assess their role in framating the narrative. |
| Outcome: | The proposed method surpasses baseline models and offers a more detailed and explainable analysis of media framing effects. |
The Impact of Reasoning Step Length on Large Language Models (2024.findings-acl)
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| Challenge: | Long reasoning steps in LLMs improve reasoning abilities, but the correlation between their effectiveness and the length of reasoning steps remains largely unknown. |
| Approach: | They conducted experiments that expand and compress the rationale reasoning steps within CoT demonstrations while keeping all other factors constant. |
| Outcome: | The results show that lengthening the reasoning steps in prompts significantly enhances LLMs’ reasoning abilities across multiple datasets. |
Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do (2026.acl-long)
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| Challenge: | Existing open-source models often yield only marginal overall improvements, possibly due to an overemphasis on mathematical reasoning at the expense of broader capabilities. |
| Approach: | They evaluate 12 multimodal tasks using 14 non-reasoning models and 8 reasoning models. |
| Outcome: | The proposed method is effective in multimodal reasoning tasks, the authors show . they show that it lacks the ability to maintain deep visual introspection throughout the reasoning process. |
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding (2022.acl-long)
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Mengze Li, Tianbao Wang, Haoyu Zhang, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Wenming Tan, Jin Wang, Peng Wang, Shiliang Pu, Fei Wu
| Challenge: | Existing methods for grounding video frames with dense annotations require enormous amount of human effort. |
| Approach: | They propose to ground natural language in video frames with only one frame labeled . they propose an end-to-end model that eliminates interference of irrelevant frames . |
| Outcome: | The proposed model can ground natural language in all video frames with only one frame labeled . the proposed model eliminates interference of irrelevant frames based on branch search and cropping techniques . |
Revealing Personality Traits: A New Benchmark Dataset for Explainable Personality Recognition on Dialogues (2024.emnlp-main)
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| Challenge: | Current research treats personality recognition as a classification task, failing to reveal the supporting evidence for the recognized personality. |
| Approach: | They propose a task that aims to reveal the reasoning process as supporting evidence of the personality trait. |
| Outcome: | The proposed task reveals the reasoning process as supporting evidence of the personality trait. |
Tug-of-War between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models (2024.lrec-main)
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| 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) . |
HacRED: A Large-Scale Relation Extraction Dataset Toward Hard Cases in Practical Applications (2021.findings-acl)
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Qiao Cheng, Juntao Liu, Xiaoye Qu, Jin Zhao, Jiaqing Liang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, Yanghua Xiao
| Challenge: | Relation extraction (RE) is an essential topic in natural language processing and has attracted extensive attention. |
| Approach: | They propose a case-oriented construction framework to build a hard case relation extraction dataset with 65,225 relational facts annotated from 9,231 documents. |
| Outcome: | The proposed model achieves a high 96% F1 score on data quality and is far lower than humans. |
BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover’s Distance (2020.emnlp-main)
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| Challenge: | Pre-trained language models have been proposed and applied to many NLP tasks, yielding state-of-the-art performance, but high storage and computational costs obstruct them to be effectively deployed on resource-constrained devices and real-time applications. |
| Approach: | They propose a BERT distillation method which allows each intermediate student layer to learn from any intermediate teacher layers. |
| Outcome: | The proposed method can learn from different teacher layers adaptively for different NLP tasks. |
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)
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Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yuqi Zhu, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li, Bin Gu, Mengfei Yang
| Challenge: | Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs). |
| Approach: | They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories. |
| Outcome: | The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks. |
CogKTR: A Knowledge-Enhanced Text Representation Toolkit for Natural Language Understanding (2022.emnlp-demos)
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Zhuoran Jin, Tianyi Men, Hongbang Yuan, Yuyang Zhou, Pengfei Cao, Yubo Chen, Zhipeng Xue, Kang Liu, Jun Zhao
| Challenge: | Existing knowledge-enhanced methods are limited to knowledge-intensive tasks. |
| Approach: | They propose a knowledge-enhanced text representation toolkit for natural language understanding . it combines knowledge acquisition, knowledge representation, knowledge injection and knowledge application . |
| Outcome: | The proposed toolkit supports knowledge acquisition, knowledge representation, knowledge injection, and knowledge application. |
A Troublemaker with Contagious Jailbreak Makes Chaos in Honest Towns (2025.acl-long)
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| Challenge: | Existing research focuses on single-agent attacks and shared memory attacks, but real-world scenarios often involve independent memory. |
| Approach: | They propose a large-scale, multi-agent, multitopology attack evaluation framework that exploits the memory of an agent to make it more vulnerable to jailbreak attacks. |
| Outcome: | The proposed framework improves on the troublemaker makes chaos in Honest Town task with 23.51%, 18.95%, and 52.93% improvements in line, star topologies, and 100-agent settings. |
TAVT: Towards Transferable Audio-Visual Text Generation (2023.acl-long)
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| Challenge: | Existing transfer learning techniques focus on uni-modal analysis and lack consideration of multi-modal content and cross-modal relation. |
| Approach: | They propose a transferable audio-visual text generation framework that incorporates two components: Audio-Visual Meta-Mapper and Dual Counterfactual Contrastive Learning. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods across multiple domains and modal settings. |
Can Language Models Serve as Temporal Knowledge Bases? (2022.findings-emnlp)
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| Challenge: | Existing studies have only considered language models as knowledge bases in a static setting . memorizing conflicting information is still challenging for LMs and hinders memorization of other unrelated one-to-one relationships. |
| Approach: | They propose two requirements for treating language models as temporal knowledge bases . they propose a dataset which is aimed at probing temporally-scoped knowledge . |
| Outcome: | The proposed model can store conflicting information and use stored knowledge for temporal knowledge queries. |
Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models (2024.findings-acl)
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Zhuoran Jin, Pengfei Cao, Hongbang Yuan, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, Jun Zhao
| 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. |
Bridging the Pose-Semantic Gap: A Cascade Framework for Text-Based Person Anomaly Search (2026.findings-acl)
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| Challenge: | Existing methods for person anomaly search fail to address the complexities of real-world security, authors say . Existing approaches fail to detect subtle semantic distinctions, authors argue . |
| Approach: | They propose a framework that decouples retrieval into two stages . structure-aware coarse retrieval and detective squad interaction are proposed . |
| Outcome: | The proposed framework achieves state-of-the-art performance by balancing efficiency and semantic reasoning. |
R³A: Reinforced Reasoning for Relevance Assessment for RAG in User-Generated Content Platforms (2026.acl-industry)
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| Challenge: | Existing approaches to query–document relevance assessment are limited . ambiguous user intent and asymmetric relevance are challenges for RAG platforms . |
| Approach: | They propose a decomposed reasoning model for relevance assessment that decomposes query intent into intent inference and evidence grounding. |
| Outcome: | The proposed model outperforms strong baselines on offline benchmarks and achieves significant gains in large-scale online A/B testing. |
Building a Broad Infrastructure for Uniform Meaning Representations (2024.lrec-main)
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Julia Bonn, Matthew J. Buchholz, Jayeol Chun, Andrew Cowell, William Croft, Lukas Denk, Sijia Ge, Jan Hajič, Kenneth Lai, James H. Martin, Skatje Myers, Alexis Palmer, Martha Palmer, Claire Benet Post, James Pustejovsky, Kristine Stenzel, Haibo Sun, Zdeňka Urešová, Rosa Vallejos, Jens E. L. Van Gysel, Meagan Vigus, Nianwen Xue, Jin Zhao
| Challenge: | This paper reports the first release of the UMR data set for six languages . it includes annotations for six different languages that vary greatly in terms of their linguistic properties and resource availability. |
| Approach: | They report the first release of the UMR data set for six languages . they describe on-going efforts to enlarge the data set and extend it to other languages - including Navajo, Navájo, and Sanapaná . |
| Outcome: | The first release of the UMR data set includes annotations for six languages . the language dataset is available for free and can be extended to other languages if needed . |
Weakly-Supervised Spoken Video Grounding via Semantic Interaction Learning (2023.acl-long)
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| Challenge: | Recent work on spoken video grounding challenges extracting semantic information from speech . previous studies focused on textual queries, but recent work focuses on spoken queries . |
| Approach: | They propose a framework for weakly-supervised spoken video grounding to represent cross-modal semantics without expensive temporal annotations. |
| Outcome: | The proposed framework is more efficient than existing methods. |
MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation (2021.acl-long)
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| Challenge: | Emotion recognition in conversation is a crucial component in affective dialogue systems, which helps the system understand users’ emotions and generate empathetic responses. |
| Approach: | They propose a multimodal fused graph convolutional network model which leverages multimodal dependencies and speaker information to model inter-speaker and intra-speech dependency. |
| Outcome: | The proposed model outperforms other SOTA methods on two public benchmark datasets, IEMOCAP and MELD. |
Focus on Your Question! Interpreting and Mitigating Toxic CoT Problems in Commonsense Reasoning (2024.acl-long)
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| Challenge: | Large language models exhibit high-level commonsense reasoning abilities, especially with enhancement methods like Chain-of-Thought (CoT). |
| Approach: | They propose a chain-of-thought-like method to elicit models' potential abilities to generate rationales and answers that are based on attribution tracing and causal tracers to probe the internal working mechanism of the LLM. |
| Outcome: | The proposed method eliminates Toxic CoT problems and improves the model’s overall commonsense reasoning performance by 5.5%. |
LLM-Driven Knowledge Injection Advances Zero-Shot and Cross-Target Stance Detection (2024.naacl-short)
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| Challenge: | Existing methods for stance detection focus on background information and not on the accompanying input texts. |
| Approach: | They propose to prompt Large Language Models to explicitly extract the relationship between paired text and unseen target as contextual knowledge and inject it into a generation model BART to exploit the rich contexts and semantics. |
| Outcome: | The proposed model is able to detect stance labels in zero-shot and cross-target scenarios. |
HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) face memory challenges due to the high cost of backpropagation. |
| Approach: | They propose a zeroth-order (ZO) optimization that matches memory usage to inference . they propose scalable and memory-efficient zeroth order (ZE) optimizer that integrates annealed A-GNB gradients with diagonal Hessian estimation and layer-wise clipping as a second-order pre-conditioner. |
| Outcome: | The proposed algorithm outperforms state-of-the-art methods with an average speedup of 20 over MeZO on RoBERTa-large and OPT-1.3B. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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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
| 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. |
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science (2025.findings-emnlp)
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An Luo, Xun Xian, Jin Du, Fangqiao Tian, Ganghua Wang, Ming Zhong, Shengchun Zhao, Xuan Bi, Zirui Liu, Jiawei Zhou, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding
| Challenge: | Large language models (LLMs) have advanced the automation of data science workflows, yet it remains unclear whether they can critically leverage external domain knowledge as human data scientists do in practice. |
| Approach: | They propose a benchmark to evaluate how large language models handle external domain knowledge in tabular prediction tasks. |
| Outcome: | The proposed model evaluates whether it can critically leverage external domain knowledge as human data scientists do in practice. |
MccSTN: Multi-Scale Contrast and Fine-Grained Feature Fusion Networks for Subject-driven Style Transfer (2024.lrec-main)
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| Challenge: | Stylistic style transfer is an important part of the image processing field . due to the low semantic similarity between the original image and the style image, many fine-grained style features are discarded. |
| Approach: | They propose a new style representation and transfer framework that can be adapted to existing image style transfers. |
| Outcome: | The proposed framework can be adapted to existing image style transfers. |
Neural Attention-Aware Hierarchical Topic Model (2021.emnlp-main)
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| Challenge: | Neural topic models (NTMs) use deep neural networks to learn topic information. |
| Approach: | They propose a variational autoencoder model that reconstructs sentence and document word counts using bag-of-words embeddings and pre-trained semantic embedders. |
| Outcome: | The proposed model lowers reconstruction errors at sentence and document levels and finds more coherent topics from real-world datasets. |
SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis (2025.findings-naacl)
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Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Wang Changxin, Zhifeng Gao, Hongshuai Wang, Li Yongge, Mujie Lin, Shuwen Yang, Jiankun Wang, Mingjun Xu, Jin Huang, Xi Fang, Jiaxi Zhuang, Yuqi Yin, Yaqi Li, Changhong Chen, Zheng Cheng, Zifeng Zhao, Linfeng Zhang, Guolin Ke
| Challenge: | Existing benchmarks fail to adequately evaluate the proficiency of Large Language Models (LLMs) Existing standards do not cover the skills needed to evaluate LLMs in scientific literature analysis. |
| Approach: | They propose a benchmark to evaluate the proficiency of large language models in scientific literature analysis. |
| Outcome: | SciAssess evaluates 11 LLMs on multiple tasks across scientific fields. |
Factuality Assessment as Modal Dependency Parsing (2021.acl-long)
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| Challenge: | a critical step towards factuality assessment is to determine the factuality of events in text. |
| Approach: | They propose a modal dependency parsing task that assesses the factuality of events in text . they crowdsource a large-scale data set annotated with modal dependence structures . |
| Outcome: | The proposed model outperforms the pipeline model in factuality assessment . the proposed model is based on a crowdsourced dataset . |
MULFE: A Multi-Level Benchmark for Free Text Model Editing (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have impressive capabilities in comprehending human language and vast parametric knowledge obtained from large corpora. |
| Approach: | They propose a multi-level benchmark for free text model editing to bridge the gap . they categorize probe queries into three levels of generalization . |
| Outcome: | The proposed method improves the generalization performance of large langugae models. |
FinRAGBench-V: A Benchmark for Multimodal RAG with Visual Citation in the Financial Domain (2025.emnlp-main)
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| Challenge: | Existing RAG research focuses on textual data, overlooking rich visual content in financial documents. |
| Approach: | They propose a visual RAG benchmark tailored for finance that integrates multimodal data and provides visual citation to ensure traceability. |
| Outcome: | The proposed visual RAG benchmark integrates multimodal data and provides visual citation to ensure traceability. |
CogKGE: A Knowledge Graph Embedding Toolkit and Benchmark for Representing Multi-source and Heterogeneous Knowledge (2022.acl-demo)
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Zhuoran Jin, Tianyi Men, Hongbang Yuan, Zhitao He, Dianbo Sui, Chenhao Wang, Zhipeng Xue, Yubo Chen, Jun Zhao
| Challenge: | Existing methods focus on entity-centric knowledge, but CogKGE supports heterogeneous knowledge. |
| Approach: | They propose a knowledge graph embedding toolkit to represent multi-source and heterogeneous knowledge. |
| Outcome: | The proposed toolkit provides a unified programming framework for KGE tasks and a series of knowledge representations for downstream tasks. |
MIND: From Passive Mimicry to Active Reasoning through Capability-Aware Multi-Perspective CoT Distillation (2026.acl-long)
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Jin Cui, Jiaqi Guo, Jiepeng Zhou, Ruixuan Yang, Jiayi Lu, Jiajun Xu, Jiangcheng Song, Boran Zhao, Pengju Ren
| Challenge: | Existing approaches restrict students to following a single golden rationale and treat different reasoning paths independently, causing suboptimal performance. |
| Approach: | They propose a capability-adaptive framework that transitions distillation from passive mimicry to active cognitive construction and employ a feedback-driven inertia calibration mechanism to align supervision with the student’s current adaptability. |
| Outcome: | Experiments show that the proposed framework achieves state-of-the-art performance on both in-distribution and out-of distribution benchmarks. |
End-to-End Conversational Search for Online Shopping with Utterance Transfer (2021.emnlp-main)
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Liqiang Xiao, Jun Ma, Xin Luna Dong, Pascual Martínez-Gómez, Nasser Zalmout, Chenwei Zhang, Tong Zhao, Hao He, Yaohui Jin
| Challenge: | a new study proposes a conversational search system that integrates product attributes and dialog with search . but it faces two real world challenges: imperfect product schema/knowledge and lack of training dialog data . |
| Approach: | They propose an end-to-end conversational search system that integrates search with text . they propose an utterance transfer approach that generates dialogue utterations from other domains . |
| Outcome: | The proposed system outperforms the best tested baseline in a conversational search dataset for online shopping. |
Multimodal Prompt Learning with Missing Modalities for Sentiment Analysis and Emotion Recognition (2024.acl-long)
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| Challenge: | Existing methods for multimodal sentiment analysis often fail due to equipment failure, data corruption, privacy issues and the like. |
| Approach: | They propose a multimodal Transformer framework using prompt learning to address the issue of missing modalities. |
| Outcome: | The proposed framework outperforms existing methods significantly across evaluation metrics. |
EVIT: Event-Oriented Instruction Tuning for Event Reasoning (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have made significant advances in event reasoning . however, smaller instruction-tuned models do not consistently demonstrate exceptional proficiency . |
| Approach: | They propose an event-oriented instruction tuning technique to train a large language model . they propose a structure named event quadruple which contains the structure and semantics of events . |
| Outcome: | The proposed model achieves competitive performances on event reasoning tasks. |
Whispers that Shake Foundations: Analyzing and Mitigating False Premise Hallucinations in Large Language Models (2024.emnlp-main)
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| Challenge: | Large language models generate hallucinated text when confronted with false premise questions . authors propose a method to mitigate false premises hallucinosity . |
| Approach: | They propose a method to constrain false premise attention heads during the model inference process. |
| Outcome: | The proposed method improves performance by constraining false premise attention heads . it yields a notable increase of nearly 20% of model performance . |
Video Dialog via Progressive Inference and Cross-Transformer (D19-1)
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| Challenge: | Existing visual dialog methods use RNN to encode the dialog history as a vector representation . a new method for video dialog is proposed, which progressively updates query information based on dialog history and video content until the agent think the information is sufficient and unambiguous. |
| Approach: | They propose a method which progressively updates query information based on dialog history and video content until the agent thinks it is sufficient and unambiguous. |
| Outcome: | The proposed method can be used to infer video dialog answers on large-scale datasets. |
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
| 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. |
Reasoning over Precedents Alongside Statutes: Case-Augmented Deliberative Alignment for LLM Safety (2026.acl-long)
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Can Jin, Rui Wu, Tong Che, Qixin Zhang, Hongwu Peng, Jiahui Zhao, Zhenting Wang, Wenqi Wei, Ligong Han, Zhao Zhang, Yuan Cao, Ruixiang Tang, Dimitris N. Metaxas
| Challenge: | OpenAI introduces deliberative alignment (DA) to enhance safety of its o-series models, but effectiveness of this approach in open-source LLMs is understudied. |
| Approach: | They propose a case-augmented deliberative alignment method for large language models . they propose to use reinforcement learning on self-generated safety reasoning chains . |
| Outcome: | The proposed method avoids narrowly enumerated rules and allows broader adaptability. |
LongLeader: A Comprehensive Leaderboard for Large Language Models in Long-context Scenarios (2025.naacl-long)
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Pei Chen, Hongye Jin, Cheng-Che Lee, Rulin Shao, Jingfeng Yang, Mingyu Zhao, Zhaoyu Zhang, Qin Lu, Kaiwen Men, Ning Xie, Huasheng Li, Bing Yin, Han Li, Lingyun Wang
| Challenge: | LongLeader aims to assess different LLMs' long-context comprehension abilities . long-constext comprehension is a key bottleneck for many use cases . |
| Approach: | They propose a leaderboard to assess different LLMs' long-context comprehension abilities . they offer open-source access to the benchmarks and maintain a dedicated website . |
| Outcome: | The proposed model assesses different LLMs on selected benchmarks and provides open-source access to the benchmarks. |
Seeing the Same Story Differently: Framing‐Divergent Event Coreference for Computational Framing Analysis (2025.emnlp-main)
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| Challenge: | a new task aims to capture subtle differences in how news articles frame events . a central challenge is capturing how same real-world event can evolve into sharply divergent narratives . |
| Approach: | They propose a task that identifies pairs of event mentions referring to the same underlying occurrence but differing in framing across documents. |
| Outcome: | The proposed method enables scalable, interpretable analysis of how media frame the same events differently. |
Masked Measurement Prediction: Learning to Jointly Predict Quantities and Units from Textual Context (2022.findings-naacl)
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| Challenge: | Current benchmarks do not evaluate numeracy of pretraining language models on measurements. |
| Approach: | They propose a new task where a model learns to reconstruct a number with its associated unit given masked text. |
| Outcome: | The proposed model significantly underperforms pre-trained model with baselines and ablations. |
Beyond Benchmarks: Building a Richer Cross-Document Event Coreference Dataset with Decontextualization (2025.naacl-long)
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| Challenge: | Existing datasets for Cross-Document Event Coreference (CDEC) are small and lacking diversity. |
| Approach: | They propose a new approach leveraging large language models to decontextualize event mentions by simplifying the document-level annotation task to sentence pairs with enriched context. |
| Outcome: | The proposed approach improves the quality of the dataset and generalizability of the model. |
Knowledge-Enhanced Self-Supervised Prototypical Network for Few-Shot Event Detection (2022.findings-emnlp)
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| Challenge: | Existing methods for few-shot event detection are inaccurate and lack a prototype representation module. |
| Approach: | They propose a Knowledge-Enhanced self-supervised prototypical network for few-shot event detection . it adopts hybrid rules which align event types to FrameNet and introduces knowledge to obtain more instances . |
| Outcome: | The proposed network improves few-shot event detection performance on three benchmark datasets. |
SCCS: Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment (2023.findings-acl)
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Jielin Qiu, Jiacheng Zhu, Mengdi Xu, Franck Dernoncourt, Trung Bui, Zhaowen Wang, Bo Li, Ding Zhao, Hailin Jin
| Challenge: | Existing methods for multimodal summarization ignore the structure and semantics of the whole video and article. |
| Approach: | They propose a semantic-consistent cross-domain summarization model that extracts features from video and article and uses fusion methods to select representative one. |
| Outcome: | The proposed model produces high-quality multimodal summaries on three MSMO datasets. |
Language Resource Efficient Learning for Captioning (2021.findings-emnlp)
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| Challenge: | XE loss and SC loss are both considered to be performance degradations for captioning tasks. |
| Approach: | They propose to generalize the single pairwise comparison in SC loss and use multiple generalized pairwise compares to reduce noise in baseline. |
| Outcome: | The proposed method outperforms state-of-the-art models on a video caption dataset using only half of the language resources. |
Find-the-Common: A Benchmark for Explaining Visual Patterns from Images (2024.lrec-main)
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| Challenge: | Recent advances in Instruction-fine-tuned Vision and Language Models (IVLMs) have prompted some studies to analyze the reasoning capabilities of IVLMs. |
| Approach: | They introduce a vision and language task for Inductive Visual Reasoning that uses common attributes across visual scenes to find common answers. |
| Outcome: | The proposed model can archive with 48% accuracy on the FTC, compared with state-of-the-art models. |
Is the Attention Matrix Really the Key to Self-Attention in Multivariate Long-Term Time Series Forecasting? (2026.acl-long)
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| Challenge: | In multivariate long-term time series forecasting, it is widely believed that the effectiveness of self-attention arises from its attention matrix. |
| Approach: | They propose a multi-branch MLP that isolates the ‘multi-brain mapping with element-wise operation’ structure from the Transformer and shows that it achieves competitive performance. |
| Outcome: | The proposed model outperforms three classic and three latest Transformer models and shows that it achieves competitive performance. |
T2A-Feedback: Improving Basic Capabilities of Text-to-Audio Generation via Fine-grained AI Feedback (2025.acl-long)
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Zehan Wang, Ke Lei, Chen Zhu, Jiawei Huang, Sashuai Zhou, Luping Liu, Xize Cheng, Shengpeng Ji, Zhenhui Ye, Tao Jin, Zhou Zhao
| Challenge: | Text-to-audio (T2A) models still struggle to satisfy human preferences for prompt-following and acoustic quality when generating complex multi-event audio. |
| Approach: | They propose to use AI feedback learning to enhance basic capabilities of text-to-audio models . they use a large audio preference dataset to evaluate the model's capabilities . |
| Outcome: | The proposed model improves in simple and complex scenarios with AI feedback learning. |
Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers? (2025.coling-main)
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Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang
| Challenge: | Large language models have shown remarkable performances across a wide range of tasks, but mechanisms by which they encode tasks of varying complexity remain poorly understood. |
| Approach: | They propose to explore the possibility that LLMs process concepts in different layers . they propose to categorize concepts based on their level of abstraction . |
| Outcome: | The proposed model can process complex concepts in shallow layers, the authors show . the proposed model could be used to prob complex tasks in shallow ones . |
View-R1: Asymmetric Policy Optimization for Difficulty-Aware Multimodal Reinforcement Learning (2026.findings-acl)
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| Challenge: | Multimodal Large Language Models (MLLMs) are powerful at integrating diverse data but struggle with complex reasoning. |
| Approach: | They propose a method which separates responses into positive and negative groups to stabilize training and preserve knowledge. |
| Outcome: | The proposed model View-R1 achieves a 10.55% improvement in reasoning and outperforms larger models while maintaining and improving performance on general tasks. |
Neural Graph Matching Networks for Chinese Short Text Matching (2020.acl-main)
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| Challenge: | Chinese word segmentation can be erroneous, ambiguous or inconsistent, causing performance problems. |
| Approach: | They propose a sentence matching framework that uses paired word lattices as input instead of a character sequence. |
| Outcome: | The proposed framework outperforms the state-of-the-art short text matching models on two Chinese datasets. |
Detection, Diagnosis, and Explanation: A Benchmark for Chinese Medial Hallucination Evaluation (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have made significant progress in recent years, but their practical use is hindered by their tendency to generate hallucinations. |
| Approach: | They propose to use ICD-10 and MeSH to evaluate LLMs' ability to detect medical hallucinations and make accurate diagnoses in noisy environments. |
| Outcome: | The proposed benchmark can be used to evaluate LLMs’ ability to detect medical hallucinations, make accurate diagnoses in noisy conditions, and provide plausible explanations. |
Web Sitemap Knowledge Can Enhance Autonomous Browsing (2026.findings-acl)
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Yuyao Zhang, Hongyu Lu, Jiajie Jin, Hongjin Qian, Shiyu Li, Zhao Yang, Yutao Zhu, Ji-Rong Wen, Zhicheng Dou
| Challenge: | Existing web agents suffer from limited robustness, efficiency and task success due to lack of structural understanding of websites and lack of browsing priors in pre-trained models. |
| Approach: | They propose an agent-oriented sitemap protocol that integrates structured website knowledge into web agents. |
| Outcome: | The proposed agent-oriented sitemap improves robustness, efficiency and effectiveness without extra training. |
Revisit Self-Debugging with Self-Generated Tests for Code Generation (2025.acl-long)
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Xiancai Chen, Zhengwei Tao, Kechi Zhang, Changzhi Zhou, Xinyu Zhang, Wanli Gu, Yuanpeng He, Mengdi Zhang, Xunliang Cai, Haiyan Zhao, Zhi Jin
| Challenge: | Large language models (LLMs) have made significant advances in code generation, but they still face challenges when tackling complex programming tasks beyond their basic capabilities. |
| Approach: | They propose to integrate self-generated tests into the code generation process . they propose to use post-execution and in-exection self-debugging to mitigate test bias . |
| Outcome: | The proposed method improves the performance of large language models in code generation tasks by leveraging execution feedback from tests. |
Sign2Vis: Automated Data Visualization from Sign Language (2025.findings-acl)
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| Challenge: | Existing methods to translate natural language descriptions into visualization queries focus on spoken languages, not sign languages. |
| Approach: | They propose a sign language interface that enables the DHH community to engage more fully with data analysis. |
| Outcome: | The proposed interface can be used by the deaf and hard-of-hearing community. |
OpticE: A Coherence Theory-Based Model for Link Prediction (2022.coling-1)
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| Challenge: | Knowledge representation learning is a key step required for link prediction tasks with knowledge graphs (KGs). |
| Approach: | They propose a new embedding approach based on the physical phenomenon of optical interference to reduce the semantic ambiguity in KGs. |
| Outcome: | The proposed model can compete with existing methods on KG benchmarks. |
Contrastive Token-Wise Meta-Learning for Unseen Performer Visual Temporal-Aligned Translation (2023.findings-acl)
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| Challenge: | a novel generalization framework for visual temporal-aligned translation is proposed to transfer recognition skills to unseen performers . ambiguity in the visual sequence can hinder current methods for visual language translation . |
| Approach: | They propose a generalizable framework to transfer recognition skills to unseen performers . they use visual temporal-aligned translation to generate multiple words autoregressively . |
| Outcome: | The proposed framework is generalized to transfer recognition skills to unseen performers . it is compared with existing methods on lipreading and fingerspelling datasets . |
Comparing Neighbors Together Makes it Easy: Jointly Comparing Multiple Candidates for Efficient and Effective Retrieval (2024.emnlp-main)
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| Challenge: | Experimental results show that using only bi-encoders as an intermediate reranker can improve top-1 accuracy with negligible slowdown (7%). |
| Approach: | They propose a framework that compares a query and multiple embeddings of similar candidates through shallow self-attention layers, delivering rich representations contextualized to each other. |
| Outcome: | The proposed framework compares a query and multiple embeddings of similar candidates through shallow self-attention layers, delivering rich representations contextualized to each other. |
AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension (2024.acl-long)
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Qian Yang, Jin Xu, Wenrui Liu, Yunfei Chu, Ziyue Jiang, Xiaohuan Zhou, Yichong Leng, Yuanjun Lv, Zhou Zhao, Chang Zhou, Jingren Zhou
| Challenge: | Existing benchmarks for audio-centric interaction have impeded advancements in this field . AIR-Bench evaluates LALMs' ability to understand audio signals and interact with humans . |
| Approach: | They propose a benchmark to evaluate the ability of large audio-language models to understand audio signals . they use 19 tasks with approximately 19k single-choice questions to examine single-task ability . |
| Outcome: | The proposed framework evaluates the ability of large audio-language models to understand audio signals and interact with humans in the textual format. |
CTFN: Hierarchical Learning for Multimodal Sentiment Analysis Using Coupled-Translation Fusion Network (2021.acl-long)
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| Challenge: | Existing methods for multimodal sentiment analysis require all modalities as input, thus are sensitive to missing modality at predicting time. |
| Approach: | They propose to model bi-direction interplay via couple learning and exploit multiple bi-directional translations to exploit multimodal fusion embeddings. |
| Outcome: | The proposed framework achieves state-of-the-art or often competitive performance on two multimodal benchmarks with extensive ablation studies. |
Reframing Responsibility: Framing-Aware Event Causality Identification (2026.acl-long)
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| Challenge: | Causal explanations in political narratives are often framed and contested. |
| Approach: | They propose a framing-aware extension of ECI that models causal explanations as structured claims including responsibility targets, evaluative frams, source type, and epistemic modality. |
| Outcome: | The proposed model enables quantitative analysis of divergent causal attribution across narratives. |
TransFace: Unit-Based Audio-Visual Speech Synthesizer for Talking Head Translation (2024.findings-acl)
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Xize Cheng, Rongjie Huang, Linjun Li, Zehan Wang, Tao Jin, Aoxiong Yin, Chen Feiyang, Xinyu Duan, Baoxing Huai, Zhou Zhao
| Challenge: | Existing methods for talking head translation rely on cascading, resulting in delays and cascadic errors. |
| Approach: | They propose a model for talking head translation, TransFace, which can translate audio-visual speech into audio-visual speech in other languages. |
| Outcome: | The proposed model can translate audio-visual speech into audio-visual speech in other languages. |
Unlocking the Future: Exploring Look-Ahead Planning Mechanistic Interpretability in Large Language Models (2024.emnlp-main)
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| Challenge: | Recent studies have shown that large language models may possess preliminary planning capabilities. |
| Approach: | They examine the look-ahead planning mechanism in large language models from the perspectives of information flow and internal representations. |
| Outcome: | The proposed model can decode the decision from the output of MHSA in the middle layers at the last token. |
Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention (P18-2)
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| Challenge: | Existing methods for event detection use sentence-level contextual information. |
| Approach: | They propose a document embedding enhanced bi-RNN model to detect events in sentences . they use hierarchical and supervised attention based RNN to learn document embeds . |
| Outcome: | The proposed model compares with state-of-the-art models on a ACE-2005 dataset. |
Rethinking the Multimodal Correlation of Multimodal Sequential Learning via Generalizable Attentional Results Alignment (2024.acl-long)
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| Challenge: | Existing studies have focused on the alignment of multimodal sequential learning using transformers. |
| Approach: | They propose a constrained scheme to align the multiple attentional results from both local and global perspectives. |
| Outcome: | The proposed scheme could align the multiple attentional results from both local and global perspectives, making the information capture more efficient. |
M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database (2022.acl-long)
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| Challenge: | Existing data resources to support multimodal affective analysis in dialogues are limited in scale and diversity. |
| Approach: | They propose a multimodal multi-scene multi-label Emotional Dialogue dataset, M3ED, which contains 990 dyadic emotional dialogues from 56 different TV series. |
| Outcome: | The proposed dataset contains 990 dyadic emotional dialogues from 56 different TV series, a total of 9,082 turns and 24,449 utterances. |
AgentReview: Exploring Peer Review Dynamics with LLM Agents (2024.emnlp-main)
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| Challenge: | Existing methods of peer review analysis do not address multivariate nature of the process, account for latent variables, and are constrained by privacy concerns due to the sensitive nature of data. |
| Approach: | They propose a large language model based peer review simulation framework which effectively disentangles the impacts of multiple latent factors and addresses the privacy issue. |
| Outcome: | The proposed framework disentangles the impacts of multiple latent factors and addresses privacy concerns. |
Class-Incremental Few-Shot Event Detection (2024.lrec-main)
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| Challenge: | Existing methods to deal with new class of events with only a few labeled instances are challenging . old knowledge forgetting and new class overfitting are two problems in this task. |
| Approach: | They propose a task called class-incremental few-shot event detection to solve old knowledge forgetting and new class overfitting problems. |
| Outcome: | The proposed method reduces old knowledge forgetting and new class overfitting problems on two benchmark datasets. |
Don’t Be Misled by Style: A Style-Adaptive Reranker for Capturing Effective Knowledge in Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing rerankers are mainly trained on well-edited texts, but stylistic features can be misled by reranked models. |
| Approach: | They propose a style-augmented multi-task framework that prioritizes effective knowledge over stylistic perturbations by using an LLM to derive passage-level supervision on whether a passage helps or harms answer correctness. |
| Outcome: | Extensive experiments show that SARK improves generation performance across multiple LLMs under mixed-style conditions. |
Integrating Physician Diagnostic Logic into Large Language Models: Preference Learning from Process Feedback (2024.findings-acl)
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| Challenge: | Existing studies have shown that large language models can enhance response richness and coherence, but there is a pressing need to bolster the model’s capacity for diagnostic logic to ensure patient safety. |
| Approach: | They propose an approach termed preference learning from process feedback (PLPF) that integrates the doctor’s diagnostic logic into LLMs. |
| Outcome: | The proposed approach improves the diagnostic accuracy of the baseline model in medical conversations by 17.6%, surpassing the performance of traditional approaches. |
TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis (2025.findings-acl)
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Yu Zhang, Wenxiang Guo, Changhao Pan, Dongyu Yao, Zhiyuan Zhu, Ziyue Jiang, Yuhan Wang, Tao Jin, Zhou Zhao
| Challenge: | Existing zero-shot singing voice synthesis models depend on phoneme and note boundary annotations, limiting their robustness and producing poor transitions between phonemes and notes. |
| Approach: | They propose a multi-task multilingual zero-shot SVS model with style transfer and style control based on various prompts. |
| Outcome: | Experimental results show that TCSinger 2 outperforms baseline models in subjective and objective metrics across multiple related tasks. |
AliGATr: Graph-based layout generation for form understanding (2024.findings-emnlp)
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| Challenge: | State of the art forms understanding models often rely on poorly calibrated output probabilities and low performance on relation extraction tasks. |
| Approach: | They propose a graph-based model that uses a generative objective to represent complex grid-like layouts that are often found in forms. |
| Outcome: | The proposed model performs better on the KIE and RE tasks and is more accurate than existing models. |
Where is this coming from? Making groundedness count in the evaluation of Document VQA models (2025.findings-naacl)
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| Challenge: | Document Visual Question Answering (VQA) models have come close to or matching human performance on some benchmarks. |
| Approach: | They propose a method that accounts for the semantic and multimodal groundedness of a model’s outputs and can be parameterized so that users can configure the score according to their preferences. |
| Outcome: | The proposed method produces scores that are a better indicator of a model’s robustness and tends to give higher rewards to better-calibrated answers. |
Meeseeks: A Feedback-Driven, Iterative Self-Correction Benchmark evaluating LLMs’ Instruction Following Capability (2026.findings-acl)
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Jiaming Wang, Yunke Zhao, Peng Ding, Jun Kuang, Yibin Shen, Zhe Tang, Yilin Jin, ZongYu Wang, Xiaoyu Li, Xuezhi Cao
| Challenge: | Existing models lack the ability to adhere to instructions, resulting in suboptimal performance. |
| Approach: | They propose an automated iterative instruction-following benchmark with integrated feedback mechanism. |
| Outcome: | The proposed benchmark identifies erroneous components in model responses and provides feedback accurately. |
Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding (2021.naacl-main)
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| Challenge: | Existing continual learning approaches suffer from low accuracy and performance fluctuation when the distributions of old and new data are significantly different. |
| Approach: | They propose a hyperparameter-free continual learning model for text data that can stably produce high performance under various environments. |
| Outcome: | The proposed model outperforms the best state-of-the-art method by 20% in average accuracy and each component contributes effectively to overall performance. |
Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning (2024.lrec-main)
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| Challenge: | Existing studies focus on sentence-level ECI with high-resource languages, leaving document-level DECI with low-resourced languages under-explored. |
| Approach: | They propose a Heterogeneous Graph Interaction Model with Multi-granularity Contrastive Transfer Learning for zero-shot cross-lingual ECI. |
| Outcome: | The proposed model outperforms the state-of-the-art model on monolingual and multilingual scenarios by 9.4% and 8.2% of average F1 score. |
Protein Large Language Models: A Comprehensive Survey (2025.findings-emnlp)
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Yijia Xiao, Wanjia Zhao, Junkai Zhang, Yiqiao Jin, Han Zhang, Zhicheng Ren, Renliang Sun, Haixin Wang, Guancheng Wan, Pan Lu, Xiao Luo, Yu Zhang, James Zou, Yizhou Sun, Wei Wang
| Challenge: | Existing studies focus on specific aspects or applications, but this study provides a comprehensive overview of Protein-specific large language models. |
| Approach: | This paper proposes a structured taxonomy of state-of-the-art ProteinLLMs . they analyze how they leverage large-scale protein sequence data for improved accuracy . |
| Outcome: | The proposed model covers their architectures, training datasets, evaluation metrics, and diverse applications. |
Uni-Dubbing: Zero-Shot Speech Synthesis from Visual Articulation (2024.acl-long)
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Songju Lei, Xize Cheng, Mengjiao Lyu, Jianqiao Hu, Jintao Tan, Runlin Liu, Lingyu Xiong, Tao Jin, Xiandong Li, Zhou Zhao
| Challenge: | Multimodal speech synthesis is a key challenge due to the scarcity of datasets that pair audio with corresponding video. |
| Approach: | They propose a method that incorporates modality alignment during the pre-training phase on multimodal datasets and freezes the video modality extraction component and the encoder module within the pretrained weights. |
| Outcome: | The proposed method achieves a reduced word error rate (WER) of 31.73%, surpassing the previous best of 33.9% with single-modality audio. |
Rethinking Positional Encoding in Tree Transformer for Code Representation (2022.emnlp-main)
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| Challenge: | Recent works have proposed novel tree Transformers to capture the syntactic structure in source code. |
| Approach: | They propose a novel tree Transformer encoding node positions based on a description method for tree structures to incorporate inductive bias into Transformer. |
| Outcome: | The proposed model outperforms baselines on code summarization and completion tasks across two languages, and it is able to perform better on both local and global paradigms. |
Exploring Fine-Grained Human Motion Video Captioning (2025.coling-main)
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Bingchan Zhao, Xinyi Liu, Zhuocheng Yu, Tongchen Yang, Yifan Song, Mingyu Jin, Sujian Li, Yizhou Wang
| Challenge: | Existing video captioning models fail to capture nuanced semantics of videos . existing models generate coarse descriptions of human motions, resulting in poor quality . |
| Approach: | They construct a fine-grained human motion video captioning dataset named BoFiT and a model that generates fine-grain descriptions of human motions via prompting. |
| Outcome: | The proposed model outperforms existing models on comprehensive metrics. |
CogIE: An Information Extraction Toolkit for Bridging Texts and CogNet (2021.acl-demo)
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| Challenge: | CogNet is a knowledge base that integrates three types of knowledge: linguistic knowledge, world knowledge and commonsense knowledge. |
| Approach: | They propose an information extraction toolkit called CogIE that is a bridge connecting raw texts and CogNet. |
| Outcome: | The proposed toolkit can ground raw texts to CogNet and leverage different types of knowledge to enrich extracted results. |
Prompt-Singer: Controllable Singing-Voice-Synthesis with Natural Language Prompt (2024.naacl-long)
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Yongqi Wang, Ruofan Hu, Rongjie Huang, Zhiqing Hong, Ruiqi Li, Wenrui Liu, Fuming You, Tao Jin, Zhou Zhao
| Challenge: | Recent singing-voice-synthesis methods lack ability to control style attributes of synthesized singing. |
| Approach: | They propose a singing-voice-synthesis method that enables attribute controlling on singer gender, vocal range and volume with natural language. |
| Outcome: | The proposed method achieves favorable control ability and audio quality. |
MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification (2022.coling-1)
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| Challenge: | Existing few-shot text classification methods lack labeled data in many scenarios. |
| Approach: | They propose a meta learning framework that obtains different learning rates for different tasks and neural network layers to enable the meta learner to quickly adapt to new training data. |
| Outcome: | The proposed framework can obtain different learning rates for different tasks and neural network layers so as to enable the meta learner to quickly adapt to new tasks. |
Pushing the Limits of LLM Tool Calling via Experiential Knowledge Integration and Activation (2026.findings-acl)
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| Challenge: | Existing approaches treat tool use as a problem of prompt design, API documents specification, or supervised or unsupervised alignment. |
| Approach: | They propose a knowledge-augmented tool execution framework that integrates experiential knowledge with reasoning-width-expanded inference and knowledge-aware training. |
| Outcome: | The proposed framework improves on BFCL-V3 and AppWorld on three model scales. |
H-MAS: Hierarchical Multi-Agent Scheduling for Multi-Tenant LLM Serving (2026.findings-acl)
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Yuhan Liu, Cong Xu, Qi Jia, Yihua Wang, Feiyu Chen, Liang Jin, Lu Liu, Yaqian Zhao, Yuting Ding, Xiang Li
| Challenge: | Multi-tenant Model-as-a-Service (MaaS) workloads exhibit non-stationarity across multiple time scales . existing request schedulers often rely on a fixed policy that remains unchanged at runtime . |
| Approach: | They propose a hierarchical multi-agent scheduler that operates in a layered closed loop . they propose to maintain 1.2–3.0 higher Goodput than SGLang and vLLM . |
| Outcome: | Experiments show that H-MAS achieves 1.2–3.0 higher Goodput than SGLang and vLLM . it maintains more stable QoS under diverse request lengths and heterogeneous SLO targets . |
ESCoT: Towards Interpretable Emotional Support Dialogue Systems (2024.acl-long)
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| Challenge: | Emotion-focused and strategy-driven chain-of-thought (ESCoT) is a new paradigm for emotional support dialogues. |
| Approach: | They propose an emotional support response generation scheme to improve interpretability . they generate a dataset and develop a model to generate dialogue responses with better interpretability. |
| Outcome: | The proposed scheme can generate dialogue responses with better interpretability. |
MPVStance: Mitigating Hallucinations in Stance Detection with Multi-Perspective Verification (2025.acl-long)
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| Challenge: | despite advances in large language models, challenges persist due to hallucination-models generating inaccurate content. |
| Approach: | They propose a framework that integrates multi-perspective verification with Retrieval-Augmented Generation to address these challenges. |
| Outcome: | The proposed method outperforms existing models on the SemEval-2016 and VAST datasets. |
Self-Improvement Programming for Temporal Knowledge Graph Question Answering (2024.lrec-main)
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| Challenge: | Existing methods implicitly model time constraints by learning time-aware embeddings of questions and candidate answers, which is far from understanding the question comprehensively. |
| Approach: | They propose a temporal-based temporal programming method that leverages the in-context learning ability of Large Language Models to understand combinatory time constraints in questions. |
| Outcome: | The proposed method outperforms existing methods on multiTQ and CronQuestions datasets and is highly efficient on multi-level questions. |
Emotion Recognition in Conversation via Dynamic Personality (2024.lrec-main)
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| Challenge: | Existing approaches to ERC focus on conversational contexts, but focus on static personality. |
| Approach: | They propose a model that considers the dynamic personality of speakers during conversations. |
| Outcome: | The proposed model outperforms existing models on three benchmark conversational datasets. |
KoCo-Bench: Can Large Language Models Leverage Domain Knowledge in Software Development? (2026.acl-long)
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Xue Jiang, Ge Li, Jiaru Qian, Xianjie Shi, Chenjie Li, Hao Zhu, Ziyu Wang, Jielun Zhang, Zeyu Zhao, Kechi Zhang, Jia Li, Wenpin Jiao, Zhi Jin, Yihong Dong
| Challenge: | Existing domain-specific code benchmarks focus on assessing what knowledge LLMs possess rather than how they acquire and apply new knowledge. |
| Approach: | They propose a benchmark to evaluate domain specialization methods in real-world software development. |
| Outcome: | KOCO-bench is a new benchmark for evaluating domain specialization methods in real-world software development. |
Large Language Models Can Be Contextual Privacy Protection Learners (2024.emnlp-main)
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Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable linguistic comprehension and generation capability, but when applied to specialized industries, they face challenges such as hallucination, insufficient domain knowledge, and failing to incorporate the latest domain knowledge. |
| Approach: | They propose a paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy. |
| Outcome: | The proposed model protects private data while enhancing the model's knowledge. |
RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment (2025.findings-acl)
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Zhuoran Jin, Hongbang Yuan, Tianyi Men, Pengfei Cao, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, Jun Zhao
| 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. |
UMR-Writer: A Web Application for Annotating Uniform Meaning Representations (2021.emnlp-demo)
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| Challenge: | Uniform Meaning Representations (UMRs) are graph-based semantic representations that can be used to annotate text. |
| Approach: | They present a web-based application for annotating Uniform Meaning Representations (UMR) they propose to use a graph-based cross-linguistically applicable semantic representation to annotate sentences and documents. |
| Outcome: | The proposed tool is based on a graph-based, cross-linguistically applicable semantic representation that can be used to annotate text. |
DTELS: Towards Dynamic Granularity of Timeline Summarization (2025.naacl-long)
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| Challenge: | Existing timeline summarizations lack flexibility to meet diverse granularity needs . a fine-grained timeline showing the technical details is preferred for news topics . |
| Approach: | They propose a new paradigm to construct adaptive timelines based on user instructions or requirements. |
| Outcome: | The proposed timelines are informative and granularly consistent, but they struggle to generate consistent timelines. |
Towards Explainable Diagnosis: A Self-learned Explanatory Knowledge Base Approach (2026.acl-long)
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| Challenge: | Large language models (LLMs) have great potential to facilitate explainable diagnosis, but their effectiveness is often constrained by insufficient diagnostic expertise. |
| Approach: | They propose a unified LLM-based framework for faithful and explainable diagnosis that builds a high-quality diagnostic knowledge base through a record-driven explanation learning paradigm. |
| Outcome: | The proposed framework outperforms baselines on the DiReCT and JAMA benchmarks and improves the explanation completeness metric from 64.5% to 76.9% over the best existing methods. |
Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning (2026.acl-long)
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| Challenge: | Multimodal web agents are cost-efficient and privacy-preserving, but suffer from weak planning and limited cross-website generalization. |
| Approach: | They propose a method which autonomously explores environments to discover experiences and utilizes hindsight experience to synthesize strictly aligned, high-level training data. |
| Outcome: | The proposed method outperforms Qwen2.5-VL-32B model on real-world benchmarks and demonstrates that mastering low-level atomic skills does not guarantee high-level planning competence. |
Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models (L18-1)
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| Challenge: | Existing methods for speaker modeling are based on hand-crafted statistics and ad hoc to a certain application. |
| Approach: | They propose to use speaker classification as a surrogate task for general speaker modeling and collect massive data to facilitate research in this direction. |
| Outcome: | The proposed models outperform the existing models and are feasible with speaker identity information. |
InstructoR: Instructing Unsupervised Conversational Dense Retrieval with Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods for conversational retrieval only fine-tune on limited supervised data, making it difficult for the retriever to fully grasp the entire conversation. |
| Approach: | They propose a method to instruct unsupervised conversational dense retrieval with large language models (LLMs) they use supervised data to discover the user's query intent from the conversation context . |
| Outcome: | The proposed method can bring significant improvements across various ad-hoc retrievers, surpassing the current state-of-the-art method. |
Alignment Precedes Fusion: Open-Vocabulary Named Entity Recognition as Context-Type Semantic Matching (2023.findings-emnlp)
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| Challenge: | Continual learning and zero-shot learning approaches have not been adopted to scale to novel-emerging types. |
| Approach: | They propose a method to recognize entities in novel types by their textual names or descriptions. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on three challenging OVNER benchmarks by 9.7%, 9.5%, and 1.8% F1-score of novel types. |
Establishing Trustworthy LLM Evaluation via Shortcut Neuron Analysis (2025.acl-long)
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| Challenge: | Recent studies have focused on building dynamic benchmarks to address data contamination issues. |
| Approach: | They propose a method for identifying shortcut neurons through comparative and causal analysis to suppress shortcut neurons. |
| Outcome: | The proposed method overestimates contaminated models and is highly generalizable across benchmarks and hyperparameter settings. |
Rectifying the Emotional Flow: Aligning Priors and Dynamic Guidance for High-Arousal Text-to-Speech (2026.acl-long)
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| Challenge: | Existing systems suffer from linguistic collapse when pursuing high intensity or fail to meet target emotional levels. |
| Approach: | They propose an inference framework that introduces a neutral prosody bias and a uniform Classifier-Free Guidance that distorts the acoustic manifold, leading to artifacts. |
| Outcome: | The proposed framework achieves superior linguistic accuracy and expressiveness without model retraining. |
SEAG: Structure-Aware Event Causality Generation (2023.findings-acl)
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Zhengwei Tao, Zhi Jin, Xiaoying Bai, Haiyan Zhao, Chengfeng Dou, Yongqiang Zhao, Fang Wang, Chongyang Tao
| Challenge: | Current methods for extracting event causality are limited by the lack of cross-task dependencies and may cause error propagation. |
| Approach: | They propose an approach for Structure-Aware Event Causality Generation (SEAG) they generate the ECG structure using a pre-trained language model and perform structural discriminative training alongside auto-regressive generation. |
| Outcome: | The proposed method is effective in extracting event causality from text. |
Missing Modality Imagination Network for Emotion Recognition with Uncertain Missing Modalities (2021.acl-long)
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| Challenge: | Existing multimodal fusion models trained on full-modality samples fail when partial modalities are missing. |
| Approach: | They propose a model to deal with the uncertain missing modality problem by learning robust joint multimodal representations that can predict the representation of any missing modal given available modalities under different missing-modality conditions. |
| Outcome: | The proposed model significantly improves performance under uncertain missing-modality testing conditions and full-modalities ideal testing conditions. |
A Good Neighbor, A Found Treasure: Mining Treasured Neighbors for Knowledge Graph Entity Typing (2022.emnlp-main)
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| Challenge: | Existing methods to infer missing types for knowledge graphs only leverage one-hop neighbor information of the central entity, ignoring multi-hop neighbors that can provide valuable clues for inference. |
| Approach: | They propose a method to infer missing types for knowledge graph entities by using neighbor information and co-occurrence relations between types. |
| Outcome: | The proposed method significantly outperforms existing state-of-the-art methods on two widely used datasets. |
AgentsCourt: Building Judicial Decision-Making Agents with Court Debate Simulation and Legal Knowledge Augmentation (2024.findings-emnlp)
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Zhitao He, Pengfei Cao, Chenhao Wang, Zhuoran Jin, Yubo Chen, Jiexin Xu, Huaijun Li, Kang Liu, Jun Zhao
| Challenge: | Recent advances in deep learning have significantly impacted the legal domain. |
| Approach: | They propose a multi-agent framework for judicial decision-making that simulates the court trial process . they propose 420 Chinese judgment documents to support their framework and build a large-scale legal knowledge base . |
| Outcome: | The proposed framework outperforms existing methods in various aspects, especially in generating legal articles. |
DialogueEIN: Emotion Interaction Network for Dialogue Affective Analysis (2022.coling-1)
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| Challenge: | Emotion Recognition in Conversation (ERC) has attracted increasing research attention in recent years. |
| Approach: | They propose to model the emotional interactions between speakers to simulate the emotional inertia, emotional stimulus, global and local emotional evolution in dialogues. |
| Outcome: | The proposed model can achieve superior performance compared to state-of-the-art methods on four ERC benchmark datasets, IEMOCAP, MELD, EmoryNLP and DailyDialog. |
Your Inference Request Will Become a Black Box: Confidential Inference for Cloud-based Large Language Models (2026.acl-long)
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Chung-ju Huang, Huiqiang Zhao, Yuanpeng He, Lijian Li, Wenpin Jiao, Zhi Jin, Peixuan Chen, Leye Wang
| Challenge: | Existing approaches fail to ensure privacy, maintain model performance, and preserve computational efficiency simultaneously. |
| Approach: | They propose a confidential inference framework that partitions the LLM pipeline between a client-verified Confidential Virtual Machine (CVM) and the public cloud to protect client data without compromising the cloud’s model intellectual property or inference quality. |
| Outcome: | The proposed framework can defend against state-of-the-art token inference attacks while preserving model privacy, performance, and efficiency. |
LANTERN in the Event Stream: Training-Free Temporal Knowledge Graph Forecasting by Balancing Inertia and Shifts (2026.findings-acl)
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| Challenge: | Temporal knowledge graph forecasting (TKGF) uses long-window strengthscores and short-windowed novelty scores to predict missing entities in future queries. |
| Approach: | They propose a training-freeprompting framework that uses two perspectives of history to predict missing entities in future queries. |
| Outcome: | The proposed framework outperforms the state-of-the-art baselineAnRe framework in ICEWS14, ICEW05-15, and GDELT. |
A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment (2020.coling-main)
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| Challenge: | Existing methods for cross-lingual entity alignment ignore useful pre-aligned links between two KGs. |
| Approach: | They propose a novel method that jointly learns embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments. |
| Outcome: | The proposed method achieves remarkable performance gains on three benchmark cross-lingual entity alignment datasets. |
Data-Efficiently Learn Large Language Model for Universal 3D Scene Perception (2025.findings-naacl)
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| Challenge: | Existing methods for 3D scene understanding are limited to specific downstream tasks, hindering their practicality in real-world applications. |
| Approach: | They propose a 3D visual perceptual ability and advanced reasoning capabilities for 3D scenes by aligning 3D representations into the feature space of advanced LLMs. |
| Outcome: | The proposed system achieves a 82.2% relative score compared with state-of-the-art methods with limited data. |
UniEvent: Unified Generative Model with Multi-Dimensional Prefix for Zero-Shot Event-Relational Reasoning (2023.acl-long)
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| Challenge: | Reasoning about events and their relations is an indispensable ability to fulfill various event-centric or common-sense reasoning tasks. |
| Approach: | They propose a multi-task learning framework that organizes event relational reasoning tasks into a coordinate system with multiple axes, representing inter-event relations and reasoning formulations. |
| Outcome: | The proposed framework achieves state-of-the-art or competitive performance on zero-shot and supervised reasoning tasks. |
A Comparative Study of Explicit and Implicit Gender Biases in Large Language Models via Self-evaluation (2024.lrec-main)
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| Challenge: | Existing studies on the explicit and implicit biases in large language models (LLMs) focus on either explicit or implicit bias. |
| Approach: | They propose a self-evaluation-based two-stage measurement of explicit and implicit biases within large language models grounded in social psychology. |
| Outcome: | The proposed model is based on two stages of self-evaluation on state-of-the-art LLMs to measure explicit bias toward social targets, where bias is less likely to be self-recognized by the LLM. |
PlugMed: Improving Specificity in Patient-Centered Medical Dialogue Generation using In-Context Learning (2023.findings-emnlp)
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| Challenge: | In-context learning is a key task in health conversational assistants, but it is difficult to guarantee the specificity of the responses. |
| Approach: | They propose a plug-and-play medical dialogue system that provides a patient-centered medical interpretation service to users who are less knowledgeable about medical knowledge. |
| Outcome: | The proposed model improves the specificity of the patient-centered medical dialogues by providing them with real dialogues from similar patients as prompts. |
Evaluating Personalized Tool-Augmented LLMs from the Perspectives of Personalization and Proactivity (2025.acl-long)
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| Challenge: | Personalized tool utilization is essential for aligning large language models (LLMs) with user preference in interaction scenarios with various tools. |
| Approach: | They propose a key-point-based LLM evaluation method that mitigates biases by manually annotating key points for each test case and providing them to LLM as the reference. |
| Outcome: | The proposed method mitigates biases in the LLM-as-a-judge system by manually annotating key points for each test case and providing them to LLM as the reference. |