Papers by Qing Li
Enhance Robustness of Language Models against Variation Attack through Graph Integration (2024.lrec-main)
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| Challenge: | Pre-trained language models (PLMs) are used in many NLP applications but their vulnerability to adversarial attacks can lead to false or misleading information being distributed. |
| Approach: | They propose a method to incorporate a Chinese character variation graph into pre-trained language models to increase their robustness against character variation attacks in Chinese content. |
| Outcome: | The proposed method outperforms existing language models in combating adversarial attacks in Chinese content. |
Controllable Abstractive Sentence Summarization with Guiding Entities (2020.coling-main)
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| Challenge: | Existing text summarization models lack guiding entities to ensure that entities are present in summaries. |
| Approach: | They propose a controllable abstractive sentence summarization model which generates summaries with guiding entities. |
| Outcome: | The proposed model outperforms the state-of-the-art models in evaluation scores and informativeness metrics. |
Generalizable Cross-Lingual Cognitive Distortion Detection with Standardized Annotations and Multi-Task Learning (2025.findings-acl)
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| Challenge: | Existing studies on cognitive distortion have limited generalizability and performance of models in large-scale and cross-linguistic contexts. |
| Approach: | They propose a multi-task learning model based on teacher student architecture solution which improves generalization performance. |
| Outcome: | The proposed model improves generalizability and interpretability of the proposed model. |
From Allies to Adversaries: Manipulating LLM Tool-Calling through Adversarial Injection (2025.naacl-long)
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| Challenge: | Toolcalling has changed Large Language Model (LLM) applications by integrating external tools, but it also introduces new security vulnerabilities, particularly in the tool scheduling mechanisms of LLM, which have not been extensively studied. |
| Approach: | They propose a framework that exploits vulnerabilities in Large Language Models through adversarial tool injection to execute privacy theft, launch denial-of-service attacks, and manipulate business competition. |
| Outcome: | The proposed framework exploits vulnerabilities in LLM tool-calling systems through adversarial tool injection. |
Recurrent Attention Networks for Long-text Modeling (2023.findings-acl)
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| Challenge: | Existing approaches to encoding long documents using self-attention have been limited by quadratic computational complexities and limited application in long text processing. |
| Approach: | They propose a long-document encoding model that allows the recurrent operation of self-attention. |
| Outcome: | The proposed model extracts global semantics in token-level and document-level representations, making it inherently compatible with both sequential and sequential tasks. |
Repairing Catastrophic-Neglect in Text-to-Image Diffusion Models via Attention-Guided Feature Enhancement (2024.findings-emnlp)
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| Challenge: | Text-to-Image Diffusion models generate high-quality images from textual descriptions, but they often produce images that do not fully align with the input prompts, resulting in semantic inconsistencies. |
| Approach: | They propose an automated repair approach to address catastrophic-neglect in T2I DMs. |
| Outcome: | The proposed model achieves 10.1%-16.3% higher Correct Rate in image generation compared to baselines. |
Chart-MRAG: Benchmarking Multimodal Retrieval Augmented Generation on Chart-based Documents (2026.acl-long)
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null Ymyang, Jiang Zhong, Li Jin, Xiao Sun, Jingwang Huang, null Gaojinpeng, Qing Liu, Yang Bai, Jingyuan Zhang, Rui Jiang, Qin Lei, Kaiwen Wei
| Challenge: | Existing benchmarks focus on simple image-text interactions, overlooking complex visual formats like charts. |
| Approach: | They propose a semi-automatic framework for generating evaluation samples through multi-modal keypoint extraction, knowledge graph construction, and qa pair synthesis. |
| Outcome: | The proposed framework generates 4,738 question-answering pairs across 8 domains from real-world documents. |
Task-oriented Domain-specific Meta-Embedding for Text Classification (2020.emnlp-main)
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| Challenge: | Existing methods neglect domain-specific knowledge and use the same word embedding for each word in all domain-specified datasets. |
| Approach: | They propose a method to incorporate domain-specific and task-oriented information into meta-embeddings by combining pre-trained word embeddings. |
| Outcome: | The proposed method performs well on four text classification datasets and shows that it is compatible with existing methods. |
Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs (2025.acl-long)
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| Challenge: | Recent studies have focused on prompt engineering to extract sentence embeddings from large language models (LLMs) but these models are mostly decoder-only and the earlier tokens in the sentence cannot attend to the latter, resulting in biased encoding of sentence information and cascading effects on the final decoded token. |
| Approach: | They propose a plug-and-play and training-free technique that prepends each layer’s decoded sentence embedding to the beginning of the sentence in the next layer’ s input. |
| Outcome: | The proposed technique can significantly improve the performance of existing prompt-based sentence embedding methods across different LLMs while incurring negligible additional inference cost. |
AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework (2024.lrec-main)
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| Challenge: | Currently, ML&DL methods fail to provide reasons for stock trend predictions, lacking interpretability and reasoning processes. large language models (LLMs) suffer from hallucinations and are unable to keep up with the latest information. |
| Approach: | They develop a method to train large language models to handle financial analysis tasks . they use AlphaFin datasets to compare performance with traditional methods . |
| Outcome: | The proposed method improves stock trend prediction and financial question answering tasks. |
Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning (2022.findings-emnlp)
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| Challenge: | Existing approaches to transfer a pretrained language model include fine-tuning all the parameters in the language model and adapting all its subsets. |
| Approach: | They propose to select layers based on the variability of their hidden states given a task-specific corpus. |
| Outcome: | The proposed model reduces the computational cost of transfer learning methods without sacrificing performance. |
Generative Text-to-Image Retrieval via Hierarchical Identifiers and Semantic Internalization (2026.findings-acl)
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| Challenge: | Existing text-to-image retrieval methods suffer from limited semantic discriminability, alignment bias, and closed-set restrictions. |
| Approach: | They propose a framework for semantic internalization for Generative Multimodal Alignment . they construct multi-granularity hierarchical identifiers to ensure unique, semantically consistent image representations . |
| Outcome: | The proposed framework outperforms state-of-the-art frameworks on Flickr30K and MS-COCO datasets . it achieves average Recall@1, Recall @5, and Recall_10 improvements of 10.65%, 8.50%, and 7.00% . |
CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval (2026.acl-long)
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Jiahui Geng, Fengyu Cai, Shaobo Cui, Qing Li, Liangwei Chen, Chenyang Lyu, Haonan Li, Derui Zhu, Alexander Pretschner, Heinz Koeppl, Fakhri Karray
| Challenge: | Existing benchmarks focus on functional relevance while neglecting code quality. |
| Approach: | They propose a multilingual benchmark to evaluate quality-aware code retrieval . they include fine-grained quality annotations over 42,725 queries and 134,907 code snippets . |
| Outcome: | The proposed benchmarks show that state-of-the-art models fail to separate buggy or insecure code from robust counterparts. |
Emotion Classification by Jointly Learning to Lexiconize and Classify (2020.coling-main)
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| Challenge: | Existing approaches to identify emotions in short text are limited and lack coverage and inaccuracies when applied to informal short text. |
| Approach: | They propose a novel emotional network to jointly learn sentence emotions and construct emotion lexicons which are dynamically adapted to a given context. |
| Outcome: | The proposed model outperforms several approaches proposed in previous studies and achieves new state-of-the-art on the benchmark Twitter dataset. |
MATO: A Model-Agnostic Training Optimization for Aspect Sentiment Triplet Extraction (2025.naacl-long)
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| Challenge: | Existing models with strong in-house performance may struggle to generalize to diverse expressions. |
| Approach: | They propose a model-agnostic t**raining method to improve ASTE model inference . they propose to compute the violation rate (VR) on each element of one triplet . |
| Outcome: | The proposed method can improve aspect sentiment triplet extraction models consistent with expected results facing triplet element diversity. |
DRAMA: Dynamic Multi-Granularity Graph Estimate Retrieval over Tabular and Textual Question Answering (2024.lrec-main)
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| Challenge: | TableTextQA tasks require tabular and textual data, gaining increasing attention . however, row-based approaches suffer from limitations such as lack of interaction between rows . |
| Approach: | They propose a method that incorporates an interaction mechanism among multiple rows . Empirical results demonstrate that the proposed method is effective . |
| Outcome: | Empirical results show that the proposed model is effective on tabFact and HybridQA datasets. |
Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models (2024.findings-naacl)
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| Challenge: | Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions. |
| Approach: | They propose a new approach that uses text embeddings to obtain basis vectors by matrix decomposition and constructs a space for representing all prompts. |
| Outcome: | The proposed approach significantly outperforms state-of-the-art prompt paradigms on ten public reasoning benchmarks. |
C2KD: Cross-layer and Cross-head Knowledge Distillation for Small Language Model-based Recommendation (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) show promise but their size and high inference costs limit deployment on resource-constrained devices. |
| Approach: | They propose a framework to transfer task-relevant knowledge from two complementary dimensions to Large Language Models (LLMs) Large Language models (LLMS) have demonstrated great potential in sequential recommendation tasks . |
| Outcome: | Extensive experiments across diverse model families show that the proposed framework achieves competitive performance compared to LLMs. |
Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training (2025.naacl-long)
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Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Kewei Cheng, Sanket Lokegaonkar, Yifan Gao, Qing Ping, Tianyi Liu, Binxuan Huang, Zheng Li, Zhengyang Wang, Pei Chen, Ruijie Wang, Rongzhi Zhang, Nasser Zalmout, Priyanka Nigam, Bing Yin, Chao Zhang
| Challenge: | Existing LLMs often rely on complex prompting or extensive fine-tuning to introduce new capabilities while preserving strong generalizability. |
| Approach: | They propose a large-scale pre-training corpus to enhance LLM agents' capabilities . they use 103B agent-specific data encompassing 76,537 APIs . |
| Outcome: | The proposed training corpus outperforms open-source LLMs and commercial LLM agents on three agent benchmarks. |
LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following (2025.coling-main)
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| Challenge: | E-commerce authoring requires engaging, diverse, and targeted content . Large language models lack memorization of domain-specific features in e-commerce applications . |
| Approach: | They propose a unified e-commerce authoring models that address contextual preferences of customers, sellers, and platforms . they propose to integrate interleaved features presented by participating objects into the models to empower authoring applications with comprehensive scenario understanding . |
| Outcome: | The proposed models achieve state-of-the-art evaluation performance and exhibit the advantage in zero-shot practical applications. |
Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions (D18-1)
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| Challenge: | Existing approaches to visual question answering represent images using pre-trained CNNs . but they rarely provide any insight, apart from the answer, into the VQA process . |
| Approach: | They propose to break up the end-to-end VQA into two steps: explaining and reasoning . they first extract attributes and generate descriptions as explanations for an image . a reasoning module utilizes these explanations in place of the image to infer an answer . |
| Outcome: | The proposed system achieves comparable performance with baselines, but with added benefits of explanability and the ability to improve with higher quality explanations. |
MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models (2026.findings-acl)
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Kailin Jiang, Ning Jiang, Yuntao Du, Yuchen Ren, Yuchen Li, Yifan Gao, Jinhe Bi, Yunpu Ma, Bin Li, Lei Liu, Qing Li
| Challenge: | Existing benchmarks for Large Multimodal Models (LMMs) are constrained by static representations, inadequately evaluating their ability to understand time-sensitive knowledge. |
| Approach: | They propose a benchmark containing 2,104 time-sensitive knowledge samples spanning six knowledge types to evaluate temporal awareness along 6 key dimensions and 11 challenging tasks. |
| Outcome: | The proposed benchmark measures temporal awareness along 6 key dimensions and 11 tasks, while most open-source LMMs still lack time understanding ability. |
Beyond Similarity: A Gradient-based Graph Method for Instruction Tuning Data Selection (2025.acl-long)
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Yang Zhao, Li Du, Xiao Ding, Yangou Ouyang, Hepeng Wang, Kai Xiong, Jinglong Gao, Zhouhao Sun, Dongliang Xu, Qing Yang, Dongchen Li, Bing Qin, Ting Liu
| Challenge: | Existing methods for selecting training data from general datasets fail to account for the joint distribution of instructions, resulting in inefficient learning and suboptimal knowledge transfer. |
| Approach: | They propose a method that constructs a mixed gradient-based instruction graph to capture the joint distribution and interdependencies among instructions. |
| Outcome: | The proposed method outperforms existing methods on domain adaptation tasks and in complex, data-scarce scenarios. |
When Efficiency Becomes a Vulnerability: Computational Cost Attacks on WebAgents (2026.acl-long)
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| Challenge: | Existing WebAgents suffer from computational cost attacks due to long reasoning processes and excessive computational cost. |
| Approach: | They propose a framework that generates adversarial prompts and a reinforcement learning-enhanced selector to identify the most effective perturbations. |
| Outcome: | The proposed framework exploits large language models to generate diverse adversarial prompts and a reinforcement learning–enhanced selector to identify the most effective perturbations. |
Incorporating Global Information in Local Attention for Knowledge Representation Learning (2021.findings-acl)
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| Challenge: | Graph Attention Networks (GATs) are a promising model that takes advantage of localized attention mechanism to perform knowledge representation learning (KRL) on graph-structure data. |
| Approach: | They propose to incorporate global information into the GAT family of models by using an attention-based global random walk algorithm. |
| Outcome: | Experimental results on KG entity prediction against the state-of-the-arts demonstrate the effectiveness of the proposed model. |
ConRPG: Paraphrase Generation using Contexts as Regularizer (2021.emnlp-main)
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| Challenge: | Existing methods for paraphrase generation lack reliable supervision signals. |
| Approach: | They propose an unsupervised paradigm for paraphrase generation based on contextual language models, candidate filtering and paraphrase model training based upon the selected candidates. |
| Outcome: | The proposed paradigm outperforms existing paraphrase generation methods in supervised and unsupervised setups. |
Is “My Favorite New Movie” My Favorite Movie? Probing the Understanding of Recursive Noun Phrases (2022.naacl-main)
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| Challenge: | Recursive noun phrases have interesting semantic properties, yet it is unknown whether language models have such knowledge. |
| Approach: | They propose a dataset of three textual inference tasks targeting recursive noun phrases . they show that such knowledge is learnable with appropriate data . |
| Outcome: | The proposed model achieves strong zero-shot performance on an extrinsic Harm Detection task. |
FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging (2025.acl-long)
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Zichen Tang, Haihong E, Ziyan Ma, Haoyang He, Jiacheng Liu, Zhongjun Yang, Zihua Rong, Rongjin Li, Kun Ji, Qing Huang, Xinyang Hu, Yang Liu, Qianhe Zheng
| Challenge: | Compared to existing benchmarks, FinanceReasoning provides three key advancements: (1) credibility; (2) comprehensiveness; (3) numerical precision; (4) complexity; (5) complexity; and (6) complexity. |
| Approach: | They propose a benchmark to evaluate the reasoning capabilities of large reasoning models (LRMs) in financial numerical reasoning problems. |
| Outcome: | The proposed benchmark exceeds existing benchmarks in 67.8% of financial concepts and formulas and is credible, comprehensive, and challenging. |
GenDecider: Integrating “None of the Candidates” Judgments in Zero-Shot Entity Linking Re-ranking (2024.naacl-short)
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| Challenge: | Existing methods for zero-shot reranking assume the correct entity is always among the retrieved candidates. |
| Approach: | They propose a novel re-ranking approach for Zero-Shot Entity Linking . they use the Llama model to detect scenarios where the correct entity is not retrieved . |
| Outcome: | The proposed approach significantly improves disambiguation and accuracy on the ZESHEL dataset. |
MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media (2025.emnlp-main)
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Wei Zhai, Nan Bai, Qing Zhao, Jianqiang Li, Fan Wang, Hongzhi Qi, Meng Jiang, Xiaoqin Wang, Bing Xiang Yang, Guanghui Fu
| Challenge: | Social media is a key platform for emotional expression, yet deep learning lacks flexibility and interpretability. |
| Approach: | They propose to use Chinese social media to train interpretable mental health instruction datasets to test models' ability to explain their decisions. |
| Outcome: | The proposed models outperform deep learning and LLMs on three mental health downstream tasks and demonstrate their potential for clinical applications. |
Reasoning under Uncertainty: Efficient LLM Inference via Unsupervised Confidence Dilution and Convergent Adaptive Sampling (2025.emnlp-main)
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Zhenning Shi, Yijia Zhu, Yi Xie, Junhan Shi, Guorui Xie, Haotian Zhang, Yong Jiang, Congcong Miao, Qing Li
| Challenge: | Large language models suffer from overconfidence and computational inefficiency due to fixed computation budgets and miscalibrated confidence estimates. |
| Approach: | They propose a framework for computationally efficient, trustworthy reasoning under uncertainty using Diversity-Aware Self-Signal Dilution and Convergent Adaptive Weighted Sampling techniques. |
| Outcome: | The proposed framework reduces inference cost by 70% while maintaining accuracy levels while reducing inference costs. |
Visual Goal-Step Inference using wikiHow (2021.emnlp-main)
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| Challenge: | Past work in NLP examined the task of goal-step inference for textual goals . wikiHow dataset shows that goal-step inference is challenging for state-of-the-art models . |
| Approach: | They propose a task where a model is given a textual goal and must choose which of four images represents a plausible step towards that goal. |
| Outcome: | The proposed task is challenging for state-of-the-art multimodal models and can be transferred to other datasets. |
Instance-Guided Prompt Learning for Few-Shot Text Matching (2022.findings-emnlp)
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| Challenge: | Few-shot text matching is a more practical technique to determine whether two texts are semantically identical. |
| Approach: | They propose a pluggable prompt learning method for few-shot text matching . they use the semantics of instances to regulate the effects of the gate on the prompt tokens . |
| Outcome: | The proposed method outperforms baselines on MRPC and QQP. |
IgSEG: Image-guided Story Ending Generation (2021.findings-acl)
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| Challenge: | Existing tasks such as story ending generation generate text-based story endings, but visual storytelling generates photo-streams-based stories. |
| Approach: | They propose a task called Image-guided Story Ending Generation (IgSEG) given a multi-sentence story plot and an ending-related image, they propose MGCL to solve these challenges. |
| Outcome: | The proposed model outperforms baselines on automatic and human evaluation. |
Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language (2025.acl-long)
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Bo Zeng, Chenyang Lyu, Sinuo Liu, Mingyan Zeng, Minghao Wu, Xuanfan Ni, Tianqi Shi, Yu Zhao, Yefeng Liu, Chenyu Zhu, Ruizhe Li, Jiahui Geng, Qing Li, Yu Tong, Longyue Wang, Weihua Luo, Kaifu Zhang
| Challenge: | Existing datasets for instruction-following are monolingual and centered on English . existing data are unable to capture linguistic and cultural subtle differences . |
| Approach: | They propose an extension of IFEval to a localized multilingual version called Marco-Bench-MIF . their benchmark addresses linguistic constraints and cultural references via translation and verification . |
| Outcome: | The proposed extension of IFEval to a localized multilingual version covers 30 languages with varying levels of localization. |
LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Design (2026.findings-acl)
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| Challenge: | Lipid nanoparticles (LNPs) are among the most clinically mature platforms for nucleic acid delivery, yet designing lipids that are effective and biologically safe remains a major bottleneck. |
| Approach: | They propose a safety-aware multi-agent LLM framework for lipid discovery that enforces toxicity as a prerequisite for efficiency prediction. |
| Outcome: | The proposed framework achieves an average improvement in mRNA transfection efficiency prediction across multiple foundation models. |
ReRec: Reasoning-Augmented LLM-based Recommendation Assistant via Reinforcement Fine-tuning (2026.acl-long)
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| Challenge: | Existing reasoning-augmented systems that handle complex queries are lacking . we present a framework that enhances LLM-based recommendation assistants . |
| Approach: | They propose a reinforcement fine-tuning framework that enhances LLM-based recommendation . they use a dual-graph Enhanced Reward Shaping framework to integrate recommendation metrics . |
| Outcome: | The proposed framework outperforms state-of-the-art recommendations and preserves core abilities. |
Intent Detection with WikiHow (2020.aacl-main)
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| Challenge: | Existing approaches to intent detection have limited data annotated for new domains or languages. |
| Approach: | They propose to train a set of pretraining intent detection models on wikiHow which can predict a broad range of intended goals from many actions. |
| Outcome: | The proposed models achieve state-of-the-art results on the Snips dataset, the Schema-Guided Dialogue dataset, and all 3 languages of the Facebook multilingual dialog datasets. |
CADReview: Automatically Reviewing CAD Programs with Error Detection and Correction (2025.acl-long)
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| Challenge: | Computer-aided design (CAD) is crucial in prototyping 3D objects through geometric instructions. |
| Approach: | They propose a CAD review task to automatically detect and correct potential errors . they propose CAD program repairer framework to provide helpful feedback on error correction . |
| Outcome: | The proposed framework outperforms existing MLLMs in detecting errors and providing feedback on error correction. |
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)
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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji
| Challenge: | We present a new information extraction system that can construct temporal event graphs from news documents. |
| Approach: | They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction . |
| Outcome: | The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities. |
UniLR: Unleashing the Power of LLMs on Multiple Legal Tasks with a Unified Legal Retriever (2025.acl-long)
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Ang Li, Yiquan Wu, Yifei Liu, Ming Cai, Lizhi Qing, Shihang Wang, Yangyang Kang, Chengyuan Liu, Fei Wu, Kun Kuang
| Challenge: | Existing retrieval methods are designed for general domains, struggling with legal knowledge, or tailored for specific legal tasks, unable to handle diverse legal knowledge types. |
| Approach: | They propose a novel retrieval method that integrates specialized knowledge into LLMs. |
| Outcome: | The proposed method can perform multiple legal retrieval tasks for LLMs. |
Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit Clues (2024.findings-acl)
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| Challenge: | Existing jailbreak attacks primarily utilize scenario camouflage techniques, however their explicit mention of malicious intent will be easily recognized and defended by LLMs. |
| Approach: | They propose an indirect jailbreak attack approach, Puzzler, which can bypass LLM’s defensive strategies and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query. |
| Outcome: | The proposed approach can bypass the LLM’s defensive strategies and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query. |
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)
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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
| Challenge: | Existing methods for event prediction are incomplete and noisy. |
| Approach: | They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework . |
| Outcome: | The proposed framework can be applied to a wide variety of newsworthy scenarios. |
Multi-Granularity History and Entity Similarity Learning for Temporal Knowledge Graph Reasoning (2024.emnlp-main)
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| Challenge: | Existing models for Temporal Knowledge Graph reasoning capture repetitive history, ignoring the entity's multi-hop neighbour history which can provide valuable background knowledge for TKG reasoning. |
| Approach: | They propose a multi-granularity history and entity similarity learning model which captures the similarity between entities. |
| Outcome: | The proposed model can predict unknown facts based on historical information, but most existing models ignore multi-hop neighbour history which can provide valuable background knowledge for TKG reasoning. |
Neural Mixed Counting Models for Dispersed Topic Discovery (2020.acl-main)
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| Challenge: | Existing methods for inference of parameter parameters are time-consuming and difficult to use. |
| Approach: | They propose two efficient neural mixed counting models that use the negative binomial distribution as the prior for dispersed topic discovery. |
| Outcome: | The proposed models outperform state-of-the-art models in terms of perplexity and topic coherence on real-world datasets. |
Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading Comprehension (2021.findings-emnlp)
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| Challenge: | unified Aspect-based Sentiment Analysis (ABSA) aims to couple aspect terms with their corresponding opinion terms, which might make it easier to predict sentiment polarities. |
| Approach: | They propose a new paradigm to pair aspect terms with their corresponding opinion terms . they propose to use a machine learning paradigm to solve the unified ABSA task . |
| Outcome: | The proposed framework can solve the ABSA task without any additional data annotation or transformation. |
Enhancing Text-to-SQL Capabilities of Large Language Models through Tailored Promptings (2024.lrec-main)
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| Challenge: | Large language models with prompting have achieved encouraging results on many natural language processing tasks due to the absence of task-tailored promptings. |
| Approach: | They propose three promptings specifically designed for Text-to-SQL: SL-prompt, CC-promped, and SL+CC prompt. |
| Outcome: | The proposed promptings achieve execution accuracy of 86.2% and test-suite accuracy of 76% . the granularity of schema linking and the order of clause generation have great impact on performance, which are considered little in previous research. |
Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation (2020.acl-main)
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| Challenge: | Aspect term extraction is a task to extract aspect terms from review texts as opinion targets for sentiment analysis. |
| Approach: | They propose a conditional generation task for augmentation of aspect term extraction . they use a sequence-to-sequence method that generates a new sentence . results confirm that their method alleviates the data scarcity problem significantly . |
| Outcome: | The proposed method reduces the data scarcity problem significantly and boosts current models. |
Activation Steering Decoding: Mitigating Hallucination in Large Vision-Language Models through Bidirectional Hidden State Intervention (2025.acl-long)
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| Challenge: | Large Vision Language Models (LVLMs) suffer from hallucination where generated textual descriptions fail to align accurately with visual semantics. |
| Approach: | They propose a training-free approach that mitigates hallucination through targeted intervention in the model’s intermediate activations by identifying directional patterns of hallucinism in the activation space using a small calibration set. |
| Outcome: | The proposed approach reduces hallucination across multiple benchmarks while maintaining performance on general visual understanding tasks. |
UniGeM: Unifying Data Selection and Mixing via Geometric Exploration and Mining (2026.findings-acl)
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Changhao Wang, null Yunfeiyu, Xinhao Yao, Jiaolong Yang, Lu Yu, Junpeng Fang, Chaobo Li, Riccardo Cantoro, Qing Cui, Jun Zhou
| Challenge: | Large Language Models (LLMs) scaling is limited by data quality and domain mixing and instance selection are two separate problems. |
| Approach: | They propose a framework that unifies mixing and selection without training proxy models or relying on external reference datasets. |
| Outcome: | The proposed framework achieves 2.0 data efficiency over a random baseline and further improves overall performance compared to SOTA methods in reasoning-heavy evaluations and multilingual generalization. |
Mimicking the Familiar: Dynamic Command Generation for Information Theft Attacks in LLM Tool-Learning System (2025.acl-long)
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| Challenge: | Existing approaches to attack Large Language Model (LLM) tool-learning systems are black-box oriented and rely on static commands that cannot adapt flexibly to the changes in user queries and the invocation chain. |
| Approach: | They propose a dynamic attack comment generation approach for information theft attacks in LLM tool-learning systems that mimics the familiar by inferring the information utilized by upstream tools. |
| Outcome: | The proposed approach outperforms baselines with +13.2% ASRTheft and can be generalized to new tool-learning systems to expose their information leakage risks. |
Datamart-Agent: LLM-Driven Game-Theoretic Agent for Data Marketplace Modeling (2026.findings-acl)
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| Challenge: | Existing studies on data marketplaces model static equilibria and complete information, which limits their realism. |
| Approach: | They propose an LLM-driven game-theoretic agent that makes equilibrium-consistent decisions in analytically tractable data marketplaces with evolving and incomplete-information. |
| Outcome: | The proposed framework matches equilibrium-consistent decision execution in a static data marketplace with a dynamic game tree memory and mechanism-guided reflection without updating parameters. |
CARE-CR: Context-Aware Routing and Expert Fusion for Multi-Preference Cognitive Restructuring (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) offer promising avenues for automated cognitive restructuring in mental health settings, but current approaches lack the adaptability to balance conflicting therapeutic dimensions, such as empathy and rationality. |
| Approach: | They propose a decoupled optimization framework that implements a dimension-guided Monte Carlo tree search to train expert policies specialized for distinct therapeutic attributes rather than relying on a monolithic alignment strategy. |
| Outcome: | The proposed framework achieves consistent improvements over baselines across multiple evaluation dimensions, including diagnostic accuracy, contextual appropriateness, task effectiveness, and overall helpfulness, while enabling controllable cognitive restructuring generation. |
A Unified Sequence Labeling Model for Emotion Cause Pair Extraction (2020.coling-main)
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| Challenge: | Existing methods for emotion-cause pair extraction cannot distinguish emotion-caused pairs from each other . Existing approaches may suffer from possible cascading errors . |
| Approach: | They propose to assign emotion type labels to emotion and cause clauses so that they can be easily distinguished. |
| Outcome: | The proposed method can extract multiple emotion-cause pairs in an end-to-end fashion. |
A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification (2020.coling-main)
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| Challenge: | Existing supervised and distantly supervised RC models ignore the emergence of novel relations in open environment. |
| Approach: | They propose a two-phase prototypical network with prototype attention alignment and triplet loss to dynamically recognize the novel relations with a few support instances without catastrophic forgetting. |
| Outcome: | Experiments show that the proposed model performs better on deep learning and few-shot learning . it can recognize the novel relations with a few support instances without catastrophic forgetting . |
Think in Latent Thoughts: A New Paradigm for Gloss-Free Sign Language Translation (2026.acl-long)
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| Challenge: | Existing approaches to sign language translation (SLT) assume video segments are directly mappable to spoken-language words. |
| Approach: | They propose a reasoning-driven SLT framework that uses an ordered sequence of latent thoughts as an explicit middle layer between video and generated text. |
| Outcome: | The proposed model improves coherence and faithfulness over existing gloss-free methods. |
CoEvo: Coevolution of LLM and Retrieval Model for Domain-Specific Information Retrieval (2025.emnlp-main)
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Ang Li, Yiquan Wu, Yinghao Hu, Lizhi Qing, Shihang Wang, Chengyuan Liu, Tao Wu, Adam Jatowt, Ming Cai, Fei Wu, Kun Kuang
| Challenge: | Recent methods to enhance queries by generating intermediary elements can degrade retrieval performance . combining LLMs and retrievers can be difficult, resulting in unreliable or irrelevant intermediaries . |
| Approach: | They propose a framework that facilitates the coevolution of large language models and retrieval models. |
| Outcome: | The proposed framework facilitates the coevolution of LLMs and retrieval models. |
CoRec: An Easy Approach for Coordination Recognition (2023.emnlp-main)
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| Challenge: | Existing syntactic parsers are slow and suffer from errors, especially for long and complicated sentences. |
| Approach: | They propose a pipeline model COordination RECognizer with coordinator identifier and conjunct boundary detector. |
| Outcome: | The proposed model improves the yield of state-of-the-art Open IE models by reducing errors and slow processing time. |
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems (2024.emnlp-industry)
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Yilun Kong, Jingqing Ruan, YiHong Chen, Bin Zhang, Tianpeng Bao, Shi Shiwei, Du Qing, Xiaoru Hu, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao, Xueqian Wang
| Challenge: | Large language models have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools. |
| Approach: | They propose a framework to enhance the task planning and tool usage abilities of LLMs in industrial systems. |
| Outcome: | The proposed framework enhances the task planning and tool usage abilities of LLM-based agents in industrial systems. |
StructFact: Reasoning Factual Knowledge from Structured Data with Large Language Models (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have made significant strides in natural language processing by leveraging their ability to comprehend and reason with factual knowledge. |
| Approach: | They propose a benchmark to evaluate the ability of large language models to reason with structured data for knowledge-intensive tasks. |
| Outcome: | Extensive tests on 10 common LLMs show that they struggle with heterogeneity of structured data during reasoning. |
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)
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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song
| Challenge: | Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage. |
| Approach: | They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. |
| Outcome: | The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality. |
Advancing the Robustness of Large Language Models through Self-Denoised Smoothing (2024.naacl-short)
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Jiabao Ji, Bairu Hou, Zhen Zhang, Guanhua Zhang, Wenqi Fan, Qing Li, Yang Zhang, Gaowen Liu, Sijia Liu, Shiyu Chang
| Challenge: | Existing adversarial attacks can cause LLMs to make wrong predictions on downstream tasks or generate harmful content misaligned with human values. |
| Approach: | They propose to use randomized smoothing to add noise to the input and then make predictions based on these denoised versions. |
| Outcome: | The proposed method surpasses existing methods in both empirical and certified robustness in defending against adversarial perturbations for both downstream tasks and human alignments (i.e., jailbreak attacks). |
Releasing the Capacity of GANs in Non-Autoregressive Image Captioning (2024.lrec-main)
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| Challenge: | Existing non-autoregressive (NAR) models suffer from their inherent multi-modality problem. |
| Approach: | They propose an Adversarial Non-autoregressive Transformer for Image Captioning that improves model performance by modifying model structure to be compatible with contrastive learning. |
| Outcome: | The proposed model achieves 26.72 times faster than the autoregressive model on the MSCOCO dataset. |
AMR-TST: Abstract Meaning Representation-based Text Style Transfer (2023.findings-acl)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation that can enhance natural language generation (NLG) by providing a logical semantic input. |
| Approach: | They propose an AMR-based text style transfer technique that converts source text to an AML graph and generates transferred text based on the AMR graph modified by a TST policy named style rewriting. |
| Outcome: | The proposed method achieves state-of-the-art results compared with baseline models in automatic and human evaluations. |
META-LORA: Memory-Efficient Sample Reweighting for Fine-Tuning Large Language Models (2025.coling-main)
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| Challenge: | Supervised fine-tuning (SFT) is widely adopted for tailoring large language models (LLMs) to specific downstream tasks. |
| Approach: | They propose a memory-efficient method for automatic sample reweighting that learns to re-weight fine-tuning samples by minimizing the loss on a small, high-quality validation set. |
| Outcome: | Meta-LoRA learns to reweight fine-tuning samples by minimizing the loss on a small, high-quality validation set through an end-to-end bi-level optimization framework based on meta-learning. |
Exploring Continual Learning for Code Generation Models (2023.acl-short)
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Prateek Yadav, Qing Sun, Hantian Ding, Xiaopeng Li, Dejiao Zhang, Ming Tan, Parminder Bhatia, Xiaofei Ma, Ramesh Nallapati, Murali Krishna Ramanathan, Mohit Bansal, Bing Xiang
| Challenge: | Large-scale code generation models such as Copilot and CodeT5 are expensive to train and re-train. |
| Approach: | They propose a benchmark for Continual Learning (CL) that covers a wide range of tasks with different input and output programming languages. |
| Outcome: | The proposed method improves on Prompt Pooling with Teacher Forcing, which suffers catastrophic forgetting due to stark distribution shifts in coding tasks. |
Conditional Causal Relationships between Emotions and Causes in Texts (2020.emnlp-main)
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| Challenge: | Existing studies on the causal relationships between emotions and causes focus on extracting causally related clauses from documents, but none considers whether context clauses are indispensable for extracted clauses to be causally linked. |
| Approach: | They propose a task to determine whether an input pair of emotion and cause has a valid causal relationship under different contexts. |
| Outcome: | The proposed task identifies whether an input pair of emotion and cause has a valid causal relationship under different contexts and then fine-tunes the prediction results based on the characteristics of the input clauses. |
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering (2020.acl-main)
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| Challenge: | Existing graph-based methods focus only on relations between objects in an image and neglect the importance of syntactic dependency relations between words. |
| Approach: | They propose a dual channel graph convolutional network to capture relations between objects in an image and syntactic dependency relations between words in a question. |
| Outcome: | The proposed model achieves comparable performance with the state-of-the-art approaches. |
Investigating Context Faithfulness in Large Language Models: The Roles of Memory Strength and Evidence Style (2025.findings-acl)
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| Challenge: | Retrieval-augmented generation improves Large Language Models (LLMs) by integrating external information into the response generation process. |
| Approach: | They investigate the impact of memory strength and evidence presentation on LLMs’ receptiveness to external evidence by measuring the divergence in LLM responses to different paraphrases of the same question. |
| Outcome: | The proposed method improves Large Language Models (LLMs) by integrating external information into the response generation process. |
Neural Topic Modeling via Contextual and Graph Information Fusion (2025.emnlp-main)
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| Challenge: | Existing topic models generate uninformative and incoherent topics that hinder interpretable insights from managing textual data. |
| Approach: | They propose to incorporate contextual and graph information to improve the variational autoencoder framework by combining contextual and bag-of-words information. |
| Outcome: | The proposed framework generates more coherent and diverse topics on three benchmark datasets and achieves strong performance on automatic and manual evaluations. |
Exposing Numeracy Gaps: A Benchmark to Evaluate Fundamental Numerical Abilities in Large Language Models (2025.findings-acl)
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Haoyang Li, Xuejia Chen, Zhanchao Xu, Darian Li, Nicole Hu, Fei Teng, Yiming Li, Luyu Qiu, Chen Jason Zhang, Li Qing, Lei Chen
| Challenge: | Existing benchmarks focus on linguistic competence or structured mathematical problem-solving, neglecting fundamental numerical reasoning required in real-world scenarios. |
| Approach: | They propose a benchmark to evaluate numerical capabilities for large language models . they use a dataset to assess number recognition, arithmetic operations, contextual retrieval, comparison, summary, and multi-step reasoning. |
| Outcome: | The proposed benchmark evaluates six fundamental numerical capabilities: number recognition, arithmetic operations, contextual retrieval, comparison, summary, and multi-step reasoning. |
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information (2021.acl-long)
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| Challenge: | ChineseBERT model incorporates glyph and pinyin information of Chinese characters into pretraining . proposed model achieves new performance boost over baseline models with fewer training steps . |
| Approach: | They propose a ChineseBERT model that incorporates glyph and pinyin information into pretraining . the glyph embedding is obtained based on different fonts of a character, and the pinyink embeddment characterizes the pronunciation of Chinese characters. |
| Outcome: | The proposed model achieves new performance boosts over baseline models with fewer training steps. |
AutoGUI: Scaling GUI Grounding with Automatic Functionality Annotations from LLMs (2025.acl-long)
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| Challenge: | Existing datasets for UI-VLMs contain large-scale context-free element annotations or contextualized functional descriptions for elements at a small scale. |
| Approach: | They propose an auto-annotation pipeline that generates massive UI element functionality annotations based on UI content changes induced by interacting with the elements. |
| Outcome: | The proposed pipeline improves accuracy and scales well with human evaluation of a high-quality AutoGUI-704k dataset. |
Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data (2022.acl-long)
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| Challenge: | Existing work has treated procedures as shallow structures without modeling the parent-child relation. |
| Approach: | They propose to construct an open-domain hierarchical knowledge-base (KB) of procedures based on wikiHow . they link steps in an article to other articles with similar goals, recursively building the KB . |
| Outcome: | The proposed method significantly outperforms baselines according to automatic evaluation, human judgment, and application to downstream tasks such as instructional video retrieval. |
Fin-STAR: Structure-as-Semantics to Resolve Implicitness in Financial Retrieval (2026.findings-acl)
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Yu Zou, Yan Chen, Lida He, Qi Zhou, Xiaorui Zhou, Aixi Zhong, Yi Wang, Wei Li, Qingyu Wang, Jiatao Li, Wei Gong, Jialei Zeng, Jingmei Zhao, Ke Jiang, Qing Li
| Challenge: | Existing Retrieval-Augmented Generation systems treat structure as a physical navigational skeleton rather than intrinsic semantic knowledge. |
| Approach: | They propose a framework that redefining hierarchy as intrinsic semantics and uses snippets to enrich hierarchical lineage. |
| Outcome: | The proposed framework outperforms state-of-the-art hierarchical and graph-based benchmarks on FinTierQA Gold. |
One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems (2025.findings-emnlp)
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Zhiyuan Chang, Mingyang Li, Xiaojun Jia, Junjie Wang, Yuekai Huang, Ziyou Jiang, Yang Liu, Qing Wang
| Challenge: | Existing knowledge poisoning attacks against RAG systems require multiple poisoned documents or can only function effectively on simplistic queries. |
| Approach: | They propose a more realistic knowledge poisoning attack that poisons only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements. |
| Outcome: | The proposed attack achieves success by poisoning only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements. |
Fine-Grained Features-based Code Search for Precise Query-Code Matching (2025.coling-main)
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| Challenge: | Existing methods to locate code snippets from databases represent the semantics of code and query by averaging the features of each token and word. |
| Approach: | They propose a fine-grained code search model that consists of a cross-modal encoder, mapping layer and classification layer to capture fine-granular interactions between code and query. |
| Outcome: | The proposed model significantly outperforms existing methods across multiple programming language datasets. |
Making Flexible Use of Subtasks: A Multiplex Interaction Network for Unified Aspect-based Sentiment Analysis (2021.findings-acl)
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| Challenge: | Existing studies aim to integrate multiple sub-tasks into a unified ABSA model but suffer from major disadvantages . |
| Approach: | They propose a multi-task learning approach to make use of sub-tasks for a unified ABSA. |
| Outcome: | The proposed model can work well when some sub-tasks are absent, and the interactive relations among subtasks not adequate. |
FoldMoE: Efficient Long Sequence MoE Training via Attention-MoE Pipelining (2025.acl-long)
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| Challenge: | Existing approaches to training LLMs with Mixture-of-Experts (MoE) architecture on long sequences are limited by the insufficient computation. |
| Approach: | They propose a MoE training system that enables token-level overlapping across entire Transformer blocks through novel attention-MoE pipelining. |
| Outcome: | The proposed system achieves 1.49x and 2.72x speedup over state-of-the-art token-level overlapping and non-overlapping baselines on GPT-MoE models with sequences up to 32K tokens. |
PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics (2024.findings-naacl)
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| Challenge: | Existing studies have recognized hallucination as a notable concern in large autoregressive language models (LLMs). |
| Approach: | They propose a polygraph for large language models that detects "hallucination" they demonstrate that hallucination can be detected by tractable probabilistic models . |
| Outcome: | The proposed model outperforms state-of-the-art methods on open-source LLMs by 20% on TruthfulQA benchmarks. |
Explicit and Implicit Data Augmentation for Social Event Detection (2025.acl-long)
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| Challenge: | Social event detection relies on labeled data, but annotation is costly and labor-intensive. |
| Approach: | They propose a plug-and-play dual augmentation framework that combines explicit text-based and implicit feature-space augmentation to enhance data diversity and model robustness. |
| Outcome: | The proposed framework outperforms the best baseline model by 17.67% on the Twitter2012 dataset and 15.57% on the twitter2018 dataset in terms of the average F1 score. |
VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration (2025.findings-acl)
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Jiahui Geng, Qing Li, Zongxiong Chen, Yuxia Wang, Derui Zhu, Zhuohan Xie, Chenyang Lyu, Xiuying Chen, Preslav Nakov, Fakhri Karray
| Challenge: | Existing safety calibration methods focus on model undersafety, where the model responds to hazardous queries, while neglecting oversafetiness, where models refuse to answer safe queries. |
| Approach: | They propose safety calibration which addresses both undersafety and oversafetiness by comparing model responses to a novel dataset of 3,600 image-text pairs. |
| Outcome: | The proposed methods have been used to evaluate safety calibration across image-centric and text-centric scenarios. |
All Changes May Have Invariant Principles: Improving Ever-Shifting Harmful Meme Detection via Design Concept Reproduction (2026.acl-long)
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Ziyou Jiang, Mingyang Li, Junjie Wang, Yuekai Huang, Jie Huang, Zhiyuan Chang, Zhaoyang Li, Qing Wang
| Challenge: | Existing methods for harmful meme detection only learn the combination of harmful elements and lack understanding of these implicit expressions. |
| Approach: | They propose a method that detects harmful memes by replicating the design concept of malicious users. |
| Outcome: | The proposed method achieves the highest accuracy with 81.1% and has slight accuracy decreases when generalized to type-shifting and temporal-evolving memes. |
Benchmarking for Domain-Specific LLMs: A Case Study on Academia and Beyond (2025.findings-emnlp)
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| Challenge: | Comp-Comp is an iterative benchmarking framework grounded in the principles of comprehensiveness and compactness. |
| Approach: | They propose a benchmark framework that incorporates the principle of comprehensiveness and compactness. |
| Outcome: | The proposed framework is domain-agnostic and adaptable to a wide range of specialized fields. |
Towards a More Generalized Approach in Open Relation Extraction (2025.acl-long)
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| Challenge: | Existing OpenRE methods assume unlabeled data is a mixture of known and novel instances. |
| Approach: | They propose a generalized OpenRE setting that considers unlabeled data as a mixture of known and novel instances. |
| Outcome: | The proposed framework outperforms baselines in relation classification and clustering on three benchmark datasets. |
HD-NDEs: Neural Differential Equations for Hallucination Detection in LLMs (2025.acl-long)
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| Challenge: | Hallucination is a significant challenge for large language models, but current methods struggle when non-factual information arises in the early or mid-sequence of outputs, reducing their reliability. |
| Approach: | They propose a method that captures the full dynamics of large language models by using neural differential equations to assess the truthfulness of statements. |
| Outcome: | The proposed method achieves 14% improvement in AUC-ROC on the True-False dataset compared to state-of-the-art methods. |
Know Thy Enemy: Securing LLMs Against Prompt Injection via Diverse Data Synthesis and Instruction-Level Chain-of-Thought Learning (2026.findings-acl)
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Zhiyuan Chang, Mingyang Li, Yuekai Huang, Ziyou Jiang, Xiaojun Jia, Qian Xiong, Junjie Wang, Zhaoyang Li, Qing Wang
| Challenge: | Large language model (LLM)-integrated applications face security vulnerabilities from prompt injection (PI) attacks. |
| Approach: | They propose a model enhancement method that synthesizes diverse training data and employs instruction-level chain-of-thought fine-tuning to enable LLMs to effectively identify and reject malicious instructions regardless of their source or position in the context. |
| Outcome: | The proposed method outperforms baselines in three critical dimensions while maintaining utility performance without degradation. |
Where Did It Go Wrong? Capability-Oriented Failure Attribution for Vision-and-Language Navigation Agents (2026.findings-acl)
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| Challenge: | Existing testing methods are system-level and provide limited insight into which capability deficiencies cause task failures. |
| Approach: | They propose a capability-oriented testing approach that enables failure detection and attribution by seed selection and mutation. |
| Outcome: | The proposed method detects more failure cases and pinpoints capability-level deficiencies than state-of-the-art baselines, providing more interpretable and actionable guidance for improving embodied agents. |
Exploring Non-Autoregressive Text Style Transfer (2021.emnlp-main)
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| Challenge: | Existing methods for text style transfer use autoregressive decoding, but they are slow and low parallelizability. |
| Approach: | They propose a base NAR model by directly adapting the common training scheme from its AutoRegressive counterpart. |
| Outcome: | The proposed model sacrifices performance due to lack of conditional dependence between output tokens . knowledge distillation, contrastive learning, and iterative decoding are employed to improve the model . |
Chinese MentalBERT: Domain-Adaptive Pre-training on Social Media for Chinese Mental Health Text Analysis (2024.findings-acl)
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| Challenge: | Existing models for language analysis are inadequate for specialized domains like psychology. |
| Approach: | They have enriched a Chinese social media database with psychological lexicons to enhance its applicability to psychological text analysis. |
| Outcome: | The proposed model performed better on six public datasets and provided relevant predictions given the masked sentences. |
SAGE: Synergistic Adaptive Gating of Experts for Hateful Video Detection (2026.acl-long)
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| Challenge: | Existing methods for hateful video detection rely on multimodal feature fusion . existing methods rely only on blind feature mixing, which leads to feature dilution . |
| Approach: | They propose a framework that shifts from blind feature mixing to decision-level arbitration . it instantiates disentangled experts to rigorously preserve modality-specific semantics . |
| Outcome: | The proposed framework outperforms state-of-the-art methods on HateMM and MultiHateClip benchmarks. |
Improving Factual Consistency in Abstractive Summarization with Sentence Structure Pruning (2024.lrec-main)
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Dingxin Hu, Xuanyu Zhang, Xingyue Zhang, Yiyang Li, Dongsheng Chen, Marina Litvak, Natalia Vanetik, Qing Yang, Dongliang Xu, Yanquan Zhou, Lei Li, Yuze Li, Yingqi Zhu
| Challenge: | Abstractive summarization models suffer from factual inconsistency problem . post-editing methods focus on replacing suspicious entities, failing to modify incorrect content hidden in sentence structures. |
| Approach: | They propose to use sentence pruning operation to correct possible errors . they propose to apply sentence pruning operations to the syntactic dependency tree . |
| Outcome: | The proposed method improves factual consistency on the FRANK dataset compared with baselines . it is model-independent and can serve as the final step in ensuring factual consistentness. |
Reasoning about Goals, Steps, and Temporal Ordering with WikiHow (2020.emnlp-main)
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| Challenge: | Existing datasets focus on relation between procedural events, but little attention has been paid to relation between events. |
| Approach: | They propose a set of reasoning tasks targeting goal-step relations and step-step temporal relations based on wikiHow articles . their automatically-generated training set allows models to transfer to out-of-domain tasks requiring knowledge of procedural events . |
| Outcome: | The proposed dataset improves on SWAG, Snips, and Story Cloze Test in zero- and few-shot settings. |
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing (2022.acl-long)
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Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei
| Challenge: | Existing work shows that pre-trained models can improve in various natural language processing tasks. |
| Approach: | They propose a unified-modal encoder-decoder framework that pre-trains speech-text representations using large-scale unlabeled speech and text data. |
| Outcome: | The proposed framework is superior to existing models on speech-to-text processing tasks. |
SlideCoder: Layout-aware RAG-enhanced Hierarchical Slide Generation from Design (2025.emnlp-main)
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Wenxin Tang, Jingyu Xiao, Wenxuan Jiang, Xi Xiao, Yuhang Wang, Xuxin Tang, Qing Li, Yuehe Ma, Junliang Liu, Shisong Tang, Michael R. Lyu
| Challenge: | Existing natural language-based LLM generation methods struggle to capture visual and structural nuances of slide designs. |
| Approach: | They propose a layout-aware framework for generating editable slides from reference images . they propose python code that translates NL instructions into Python code to construct each slide . |
| Outcome: | The proposed framework outperforms state-of-the-art models by up to 40.5 points . it also outperformed open-source models with improved reverse-engineered data. |
Rethinking Multimodal Entity and Relation Extraction from a Translation Point of View (2023.acl-long)
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| Challenge: | Special attention is paid to the cross-modal misalignment in text-image datasets which may mislead the learning. |
| Approach: | They propose a multimodal back-translation method which uses diffusion-based generative models for pseudo-paralleled pairs and a divergence estimator to construct a high-resource corpora as a bridge for low-ressource learners. |
| Outcome: | The proposed method outperforms 14 state-of-the-art methods in both entity and relation extraction tasks. |
EFSA: Towards Event-Level Financial Sentiment Analysis (2024.acl-long)
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| Challenge: | a large-scale Chinese dataset contains 12,160 news articles and 13,725 quintuples . a four-hop Chain-of-Thought LLM-based approach is devised for this task . |
| Approach: | They propose to extend financial sentiment analysis to event-level since events usually serve as the subject of the sentiment in financial text. |
| Outcome: | The proposed method can reach the current state-of-the-art on a large-scale Chinese dataset. |
Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models (2024.acl-long)
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Zhengxin Zhang, Dan Zhao, Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, Qing Li, Yong Jiang, Zhihao Jia
| Challenge: | Existing methods to finetun large language models (LLMs) only update a small number of trainable parameters, or attempt to reduce the memory footprint during the training phase of the finetune process. |
| Approach: | They propose quantized side tuing (QST) which quantizes an LLM’s model weights into 4-bit to reduce the memory footprint of the original weights. |
| Outcome: | The proposed method reduces the memory footprint of the model weights, optimizer states, and intermediate activations while reducing the memory requirements. |
Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs (2023.emnlp-main)
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| Challenge: | Recent studies on relation representation learning focus on contrastive learning strategies, but these studies overlook important aspects. |
| Approach: | They propose to use within-sentence pairs augmentation and cross-sentent pairs extraction to increase diversity of positive pairs and strengthen the discriminative power of contrastive learning. |
| Outcome: | The proposed task increases diversity of positive pairs and strengthens discriminative power . it overcomes limitations of traditional Relation Extraction tasks, which require manual annotations . |
GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs (2022.emnlp-main)
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| Challenge: | Existing methods for embedding knowledge graphs are difficult due to complicated query structures and incomplete graph data. |
| Approach: | They propose a probabilistic embedding model for encoding entities and queries to answer different types of FOL queries on KGs. |
| Outcome: | The proposed model outperforms state-of-the-art models on public benchmarks on three large logical query datasets. |
Reference-free Hallucination Detection for Large Vision-Language Models (2024.findings-emnlp)
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| Challenge: | Large vision-language models exhibit excellent ability in language understanding, question answering, and conversations of visual inputs, but they are prone to producing hallucinations. |
| Approach: | They propose to use supervised uncertainty quantification methods to detect hallucinations in large vision-language models. |
| Outcome: | The proposed methods outperform the others in detecting hallucinations on four representative LVLMs across two different tasks. |
Collaborative Learning of Bidirectional Decoders for Unsupervised Text Style Transfer (2021.emnlp-main)
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| Challenge: | Existing methods for unsupervised text style transfer struggle to achieve high style conversion rate and low content loss. |
| Approach: | They propose a collaborative learning framework for unsupervised text style transfer using a pair of bidirectional decoders. |
| Outcome: | The proposed framework achieves strong empirical results on style compatibility and content preservation. |
Removal of Hallucination on Hallucination: Debate-Augmented RAG (2025.acl-long)
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| Challenge: | erroneous or biased retrieval can mislead generation, compounding hallucinations. |
| Approach: | They propose a framework that integrates multi-agent debates into retrieval and generation stages to improve retrieval reliability. |
| Outcome: | The proposed framework improves retrieval reliability, reduces hallucinations and significantly improves overall factual accuracy. |
Descriptive Prompt Paraphrasing for Target-Oriented Multimodal Sentiment Classification (2023.findings-emnlp)
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| Challenge: | Current researches mainly work on either of two types of targets in a decentralized manner. |
| Approach: | They propose a model to perform sentiment polarity on a target jointly considering its corresponding multiple modalities including text, image, and others. |
| Outcome: | The proposed model performs well on four datasets spanning the above two target types and is prompt-based language modelling. |
GLProtein: Global-and-Local Structure Aware Protein Representation Learning (2025.findings-emnlp)
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| Challenge: | Despite advances in protein sequence analysis, there remains potential for further exploration in integrating protein structural information. |
| Approach: | They propose a framework that integrates global structural similarity and local amino acid details to enhance protein pre-training. |
| Outcome: | The proposed framework outperforms existing methods in several bioinformatics tasks. |
SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding (2026.findings-acl)
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Yuqi Yang, Weiqi Wang, Baixuan Xu, Wei Fan, Qing Zong, Chunkit Chan, Zheye Deng, Xin Liu, Yifan Gao, Changlong Yu, Chen Luo, Yang Li, Zheng Li, Qingyu Yin, Bing Yin, Yangqiu Song
| Challenge: | Existing models fail to capture and model customer intention effectively because of insufficient information exploitation and only apparent information like descriptions and titles are used. |
| Approach: | They propose to exploit existing session data to capture and model intention in E-commerce product purchase sessions using a multimodal benchmark. |
| Outcome: | The proposed framework can bridge the gap between intention understanding in simplified research cases like co-buy intention and more complex yet practical scenarios like session history. |
ContrastKV: Robust KV Cache Eviction via Contrastive Signal Fusion for Multi-Query Generalization (2026.acl-long)
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| Challenge: | Existing query-agnostic approaches rely on a single proxy query, leading to fragile eviction decisions under high evict ratios. |
| Approach: | They propose a query-agnostic KV cache eviction algorithm that exploits complementary semantic and non-semantic signals. |
| Outcome: | Experiments show that the proposed algorithm outperforms state-of-the-art methods while retaining up to 92% accuracy with only 20% of the KV cache budget. |
QueryAttack: Jailbreaking Aligned Large Language Models Using Structured Non-natural Query Language (2025.findings-acl)
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Qingsong Zou, Jingyu Xiao, Qing Li, Zhi Yan, Yuhang Wang, Li Xu, Wenxuan Wang, Kuofeng Gao, Ruoyu Li, Yong Jiang
| Challenge: | Existing methods to bypass security defenses of large language models (LLMs) are not effective, but QueryAttack can be jailbroken. |
| Approach: | They propose a framework to examine generalizability of safety alignment by translating malicious queries into structured non-natural query languages. |
| Outcome: | The proposed framework can achieve high attack success rates and jailbreak various defense methods on mainstream LLMs. |
Unsolvable Problem Detection: Robust Understanding Evaluation for Large Multimodal Models (2025.acl-long)
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Atsuyuki Miyai, Jingkang Yang, Jingyang Zhang, Yifei Ming, Qing Yu, Go Irie, Yixuan Li, Hai Helen Li, Ziwei Liu, Kiyoharu Aizawa
| Challenge: | Multiple-choice question answering (MCQA) is widely used to assess the understanding capability of Large Multimodal Models (LMMs). |
| Approach: | They propose a task to evaluate the robust understanding capability of Large Multimodal Models (LMMs) they introduce a benchmark to assess performance across various ability dimensions . |
| Outcome: | The proposed model can withhold answers when encountering unsolvable problems of MCQA, proving it understands the answer. |
Gradient-Adaptive Policy Optimization: Towards Multi-Objective Alignment of Large Language Models (2025.acl-long)
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| Challenge: | Reinforcement learning from human feedback (RLHF) has emerged as a powerful technique for aligning large language models (LLMs) with human preferences. |
| Approach: | They propose a novel algorithm that uses multiple-gradient descent to optimize LLMs with diverse preferences to maximize trade-offs between objectives. |
| Outcome: | The proposed approach incorporates user preferences across different objectives and achieves Pareto solutions that better align with the user’s specific needs. |
CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language Models (2026.findings-acl)
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| Challenge: | Existing security models rely on open-ended communication, but the collaborative process itself can be exploited and disrupted. |
| Approach: | They propose a new threat class, called Denial-of-Collaboration, which corrupts collaborative structure and transforms communication topology into self-sabotage. |
| Outcome: | The proposed attacks bypass conventional safety alignments that are not designed to detect behavioral or systemic attacks. |
SpecCoT: Accelerating Chain-of-Thought Reasoning through Speculative Exploration (2025.findings-emnlp)
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| Challenge: | Large Reasoning Models suffer from high inference latency due to lengthy reasoning chains. |
| Approach: | They propose a collaborative framework that combines large and small models for effective reasoning. |
| Outcome: | The proposed framework reduces inference latency by 1.7-4.1 while maintaining comparable accuracy to standard large model inference. |
Re-Examine Distantly Supervised NER: A New Benchmark and a Simple Approach (2025.coling-main)
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| Challenge: | Existing DS-NER approaches rely on large validation sets and test set for tuning inappropriately. |
| Approach: | They propose a method where training data is annotated using domain dictionaries and test data is analyzed by domain experts. |
| Outcome: | The proposed method reduces the need for labor-intensive manual annotations but rely on large human labeled validation set. |