Papers by Qing Zhang
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. |
MLLM-Protector: Ensuring MLLM’s Safety without Hurting Performance (2024.emnlp-main)
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Renjie Pi, Tianyang Han, Jianshu Zhang, Yueqi Xie, Rui Pan, Qing Lian, Hanze Dong, Jipeng Zhang, Tong Zhang
| Challenge: | MLLMs are deployed on limited image-text pairs, which makes them more vulnerable to catastrophic forgetting of their original abilities during safety fine-tuning. |
| Approach: | They propose a plug-and-play strategy that detects harmful visual inputs and transforms harmful ones into harmless ones. |
| Outcome: | The proposed approach mitigates the risks posed by malicious visual inputs without compromising the original performance of MLLMs. |
TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection (2026.acl-long)
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| Challenge: | Existing benchmarks for political user-level stance detection rely on noisy heuristics or distant supervision. |
| Approach: | They propose a large-scale, expert-annotated benchmark for political user-level stance detection with explicit social network structure that integrates user content and followee signals. |
| Outcome: | The proposed framework outperforms baselines in terms of quality and reliability. |
Learning to Solve Domain-Specific Calculation Problems with Knowledge-Intensive Programs Generator (2025.naacl-long)
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| Challenge: | Domain Large Language Models (LLMs) are developed for domain-specific tasks based on general LLMs, but it still requires professional knowledge to facilitate the expertise for some domain- specific tasks. |
| Approach: | They propose a pipeline to solve domain-specific calculation problems with KIPG . they use it to extract key variables and calculate outcomes dependent on domain knowledge . |
| Outcome: | The proposed pipeline solves domain-specific calculation problems more effectively . it generates knowledge-intensive programs according to the domain- specific documents . |
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. |
CamoQuery: Language-Guided Reasoning Camouflaged Object Segmentation (2026.acl-long)
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| Challenge: | Existing methods for camouflaged object segmentation are limited to vision-only mask prediction under fixed task assumptions. |
| Approach: | They propose a language-guided reasoning camouflaged object segmentation task that generates an intent-consistent segmentation mask from an image and an implicit query text instruction. |
| Outcome: | The proposed task can generate an intent-consistent segmentation mask from a camouflaged image and an implicit query text instruction. |
Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts (2024.emnlp-main)
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Xianzhen Luo, Qingfu Zhu, Zhiming Zhang, Libo Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che
| Challenge: | Program of Thoughts (PoT) is an approach characterized by its executable intermediate steps, which ensure the accuracy of the logical calculations in the reasoning process. |
| Approach: | They propose a task and model agnostic approach which harnesses strength and diversity from various languages to achieve better performance across all tasks. |
| Outcome: | The proposed approach outperforms Python Self-Consistency in almost all tasks and models and achieves comparable or superior performance on ChatGPT. |
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. |
TranS: Transition-based Knowledge Graph Embedding with Synthetic Relation Representation (2022.findings-emnlp)
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| Challenge: | Knowledge graph embedding (KGE) is a computational approach to learn continuous vector representations of relations and entities in knowledge graphs. |
| Approach: | They propose a transition-based method to learn continuous vector representations of relations and entities in knowledge graph (KG) it replaces a single relation vector in the relation part with a synthetic relation representation with entity-relation interactions to solve these problems. |
| Outcome: | The proposed method achieves state-of-the-art on a large knowledge graph dataset. |
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training (2024.emnlp-main)
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Yixuan Wang, Xianzhen Luo, Fuxuan Wei, Yijun Liu, Qingfu Zhu, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che
| Challenge: | Existing speculative decoding methods require additional model structure and training processes to assist the model for draft token generation. |
| Approach: | They propose a make some noise training framework that introduces some noise at the input for the model to learn the denoising task. |
| Outcome: | The proposed model improves inference speed by 2.3-2.7x times without compromising model performance. |
Generating Extractive Answers: Gated Recurrent Memory Reader for Conversational Question Answering (2023.findings-emnlp)
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| Challenge: | Conversational question answering (CQA) requires models to extract answers from given contents to answer follow-up questions according to conversation history. |
| Approach: | They propose a novel architecture that integrates extractive MRC models into a generalized sequence-to-sequence framework. |
| Outcome: | The proposed architecture can use less storage space and consider historical memory deeply and selectively. |
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. |
The Instinctive Bias: Spurious Images lead to Illusion in MLLMs (2024.emnlp-main)
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| Challenge: | Existing multi-modal large language models (MLLMs) are able to process visual inputs by converting them into visual tokens that share the same latent space as language tokens in LLMs. |
| Approach: | They propose a benchmark that assesses the visual illusion level given spurious images and a pipeline that converts visual inputs into visual tokens. |
| Outcome: | The proposed benchmark shows that MLLMs suffer from an instinctive bias to varying degrees when presented with spurious images. |
From Model-centered to Human-Centered: Revision Distance as a Metric for Text Evaluation in LLMs-based Applications (2024.findings-acl)
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| Challenge: | Existing evaluation metrics for large language models yield numerical scores that ignore user experience. |
| Approach: | They propose a metric that suggests revision edits that mimic the human writing process . their results show that the metric offers more insightful feedback and distinguishes between texts . |
| Outcome: | The proposed metric can provide a self-explained text evaluation result in a human-understandable manner beyond the context-independent score. |
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. |
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. |
Zero-shot Event Extraction via Transfer Learning: Challenges and Insights (2021.acl-short)
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| Challenge: | Existing methods for event extraction require expensive annotation and are not extensible to new event ontologies. |
| Approach: | They propose to use textual entailment and/or question answering queries to extract a zero-shot event from a set of TE and/ or QA queries. |
| Outcome: | The proposed method achieves acceptable results on ACE-2005 and ERE, but there is still a large gap from supervised approaches. |
Knowledge Graph Embedding with Hierarchical Relation Structure (D18-1)
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| Challenge: | Existing knowledge graph embedding models embed entities and relations into latent vectors without leveraging rich information from relation structure. |
| Approach: | They extend existing KGE models to learn knowledge representations by leveraging relation structure . authors say their approach is capable of extending other KGEs . |
| Outcome: | The proposed approach can extend existing KGE models, and validates against baselines. |
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. |
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. |
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. |
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. |
Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding (2025.findings-naacl)
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Xingjian Diao, Chunhui Zhang, Weiyi Wu, Zhongyu Ouyang, Peijun Qing, Ming Cheng, Soroush Vosoughi, Jiang Gui
| Challenge: | Multimodal foundation models have demonstrated significant success in tasks such as visual captioning, question answering, and image-text retrieval. |
| Approach: | They propose a specialized cognitive module, temporal working memory, which selectively retains task-relevant information across temporal dimensions. |
| Outcome: | The module retains task-relevant information across temporal dimensions, ensuring that critical details are preserved throughout the processing of video and audio content. |
MultiPL-MoE: Multi-Programming-Lingual Extension of Large Language Models through Hybrid Mixture-of-Experts (2025.findings-emnlp)
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| Challenge: | MultiPL is a special case of multiple natural languages and requires limited computational resources to generate multilingual code. |
| Approach: | They propose to extend LLMs by combining two paired experts to optimize expert selection at token and segment levels. |
| Outcome: | The proposed extension improves the performance of the base LLMs while retaining the most popular ones using limited computational resources. |
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. |
Distillation with Explanations from Large Language Models (2024.lrec-main)
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| Challenge: | Large language models (LLMs) like ChatGPT and GPT-4 have made remarkable progress in various NLP tasks while also providing explanations alongside their answers. |
| Approach: | They propose to use Large language models (LLMs) to generate more accurate answers and corresponding free-text explanations by combining ground truth labels and answers-explanations generated by LLMs. |
| Outcome: | The proposed method achieves improved predictive performance and generates explanations that exhibit greater alignment with the model’s task outputs. |
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. |
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. |
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. |
R-Tuning: Instructing Large Language Models to Say ‘I Don’t Know’ (2024.naacl-long)
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Hanning Zhang, Shizhe Diao, Yong Lin, Yi Fung, Qing Lian, Xingyao Wang, Yangyi Chen, Heng Ji, Tong Zhang
| Challenge: | Existing methods for instruction tuning force the model to complete a sentence no matter whether it knows the knowledge or not. |
| Approach: | They propose a new approach to tuning large language models to refrain from answering questions beyond its parametric knowledge by identifying the disparity in parametric and parametric information. |
| Outcome: | The proposed approach improves a model’s ability to answer known questions and refrain from answering unknown questions. |
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). |
Unilaw-R1: A Large Language Model for Legal Reasoning with Reinforcement Learning and Iterative Inference (2025.emnlp-main)
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| Challenge: | Reasoning-focused large language models (LLMs) are rapidly evolving across various domains, yet their capabilities in handling complex legal problems remain underexplored. |
| Approach: | They propose a large language model tailored for legal reasoning with a 7-billion parameter scale and a two-stage training strategy combining Supervised Fine-Tuning and Reinforcement Learning. |
| Outcome: | The proposed model outperforms all models of similar scale on authoritative benchmarks and outperformed Qwen-2.5-7B-Instruct (46.6%) by an average margin of 6.6%. |
Adaptive Attention for Sparse-based Long-sequence Transformer (2023.findings-acl)
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| Challenge: | Recent studies show that Transformers can process longer sequences because of their complexity and time scales quadratic to the sequence length. |
| Approach: | They propose an efficient Transformer model with adaptive attention that can select useful tokens automatically in sparse attention by learnable position vectors. |
| Outcome: | The proposed model can select useful tokens automatically in sparse attention by learnable position vectors. |
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. |
TurnBack: A Geospatial Route Cognition Benchmark for Large Language Models through Reverse Route (2025.emnlp-main)
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Hongyi Luo, Qing Cheng, Daniel Matos, Hari Krishna Gadi, Yanfeng Zhang, Lu Liu, Yongliang Wang, Niclas Zeller, Daniel Cremers, Liqiu Meng
| Challenge: | Existing studies on large language models have limited evaluation of their geospatial cognition . a unified framework for evaluating geospcial cognition in LLMs remains absent . |
| Approach: | They propose a benchmark to evaluate the geospatial route cognition of Large Language Models . they propose 'pathbuilder' tool for converting natural language instructions into navigation routes . |
| Outcome: | The proposed framework and metrics evaluate 9 state-of-the-art LLMs on route reversal task. |
AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation (2024.acl-long)
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Zhaowei Wang, Wei Fan, Qing Zong, Hongming Zhang, Sehyun Choi, Tianqing Fang, Xin Liu, Yangqiu Song, Ginny Wong, Simon See
| Challenge: | Existing work shows that LLMs are deficient in abstract ability, and how to improve it remains unexplored. |
| Approach: | They propose a framework AbsInstruct to enhance LLMs’ abstract ability through instruction tuning. |
| Outcome: | The proposed framework can enhance LLMs’ abstraction ability with strong generalization performance while maintaining their general instruction-following abilities. |
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. |
FedCSR: A Federated Framework for Multi-Platform Cross-Domain Sequential Recommendation with Dual Contrastive Learning (2025.coling-main)
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Dongyi Zheng, Hongyu Zhang, Jianyang Zhai, Lin Zhong, Lingzhi Wang, Jiyuan Feng, Xiangke Liao, Yonghong Tian, Nong Xiao, Qing Liao
| Challenge: | Existing federated frameworks for cross-domain sequential recommendation rely on user alignment, which increases communication costs and privacy risks. |
| Approach: | They propose a federated cross-domain sequential recommendation framework that eliminates the need for user alignment between platforms. |
| Outcome: | The proposed framework eliminates the need for user alignment between platforms. |
MMAPG: A Training-Free Framework for Multimodal Multi-hop Question Answering via Adaptive Planning Graphs (2025.emnlp-main)
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| Challenge: | Existing multimodal question answering models rely on sequential retrieval and reasoning, but this single-path paradigm makes them vulnerable to errors due to misleading intermediate steps. |
| Approach: | They propose a multimodal multi-hop question answering framework guided by an Adaptive Planning Graph . they propose modality-specific strategies that dynamically adapt to distinct data types . |
| Outcome: | The proposed framework outperforms existing models that rely on training. |
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. |
AlphaEdit+: Model Editing in the Presence of Conflicting and Inconsistent Knowledge (2026.findings-acl)
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| Challenge: | Existing methods for knowledge editing struggle with knowledge conflicts and inconsistencies. |
| Approach: | They propose a new method for knowledge editing that relaxes null-space constraints and introduces a weighting scheme to mitigate conflicts between new and historical knowledge. |
| Outcome: | The proposed method outperforms existing methods on challenging datasets and outperformed existing methods. |
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. |
Judging with Many Minds: Do More Perspectives Mean Less Prejudice? On Bias Amplification and Resistance in Multi-Agent Based LLM-as-Judge (2025.findings-emnlp)
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Chiyu Ma, Enpei Zhang, Yilun Zhao, Wenjun Liu, Yaning Jia, Peijun Qing, Lin Shi, Arman Cohan, Yujun Yan, Soroush Vosoughi
| Challenge: | LLM-as-Judge frameworks provide scalable alternative to human evaluation . but the question of how intrinsic biases manifest in these settings remains unexplored . |
| Approach: | They conduct systematic analysis of four bias types in multi-agent LLM-as-Judge frameworks . they find debate framework amplifies biases sharply after initial debate . |
| Outcome: | The proposed frameworks amplify biases after debate and show they are stronger in meta-judge scenarios. |
SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models (2025.emnlp-main)
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Xingjian Diao, Chunhui Zhang, Keyi Kong, Weiyi Wu, Chiyu Ma, Zhongyu Ouyang, Peijun Qing, Soroush Vosoughi, Jiang Gui
| Challenge: | Recent large language models have demonstrated impressive reasoning abilities, but their extension to the audio modality remains underexplored. |
| Approach: | They propose a rule-based reinforcement learning algorithm to equip LALMs with robust reasoning capabilities. |
| Outcome: | The proposed algorithm improves on the SoundMind benchmark. |
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. |
Improving Gradient Trade-offs between Tasks in Multi-task Text Classification (2023.acl-long)
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| Challenge: | Existing methods to mitigate task conflict problem are heuristics or gradient-based algorithms to achieve an arbitrary Pareto optimal trade-off among different tasks . |
| Approach: | They propose a gradient trade-off approach to mitigate the task conflict problem by using heuristics or gradient-based algorithms to achieve an arbitrary Pareto optimal trade- off among different tasks. |
| Outcome: | The proposed model can achieve an arbitrary Pareto optimal trade-off among different tasks near the main objective of multi-task text classification (MTC) it is found that training all tasks simultaneously yields degraded performance than learning them independently, leading to poor training. |
IPL: Leveraging Multimodal Large Language Models for Intelligent Product Listing (2024.emnlp-industry)
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Kang Chen, Qing Zhang, Chengbao Lian, Yixin Ji, Xuwei Liu, Shuguang Han, Guoqiang Wu, Fei Huang, Jufeng Chen
| Challenge: | Unlike professional Business-to-Consumer (B2C) e-commerce platforms, consumer-to consumer (C2C), is mainly targeting individual sellers. |
| Approach: | They develop an intelligent product listing tool that generates product descriptions using various product attributes such as category, brand, color, condition, etc. |
| Outcome: | The proposed tool outperforms the base model in domain-specific tasks while producing less hallucination. |
GLiM: Integrating Graph Transformer and LLM for Document-Level Biomedical Relation Extraction with Incomplete Labeling (2025.findings-acl)
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Hao Fang, Yuejie Zhang, Rui Feng, Yingwen Wang, Qing Wang, Wen He, Xiaobo Zhang, Tao Zhang, Shang Gao
| Challenge: | Document-level relation extraction (DocRE) solves problems of document quality . number of entities and entity-pair relations increases, causing incomplete annotations . |
| Approach: | a framework that reduces the problem space using a graph-enhanced Transformer-based model is proposed . GLiM leverages large language models for reasoning to reduce the problem-space . |
| Outcome: | GLiM boosts average recall and F1 scores on biomedical datasets . compared with existing models, GLim outperforms existing models on biomedicine benchmarks compared to existing models . |
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. |
Cause-CSD: A Challenge Multimodal Conversational Stance Cause Detection Dataset and Effective Method (2026.findings-acl)
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| Challenge: | Existing stance detection methods treat opinions as surface-level labels, overlooking conversational evidence behind stance expressions. |
| Approach: | They propose a task that jointly identifies stance polarity and contextual evidence . they propose stance-cause Detection language model that leverages explicit context reasoning . |
| Outcome: | The proposed task outperforms baseline methods on text-only and multimodal subtasks. |
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. |
HydraRAG: Structured Cross-Source Enhanced Large Language Model Reasoning (2025.emnlp-main)
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| Challenge: | Current RAG system retrieves evidence from knowledge graphs and text documents but has limitations in multi-hop reasoning, multi-entity questions, and source verification. |
| Approach: | They propose a training-free framework that unifies graph topology, document semantics, and source reliability to support deep, faithful reasoning in large language models. |
| Outcome: | The proposed framework outperforms the current hybrid model-based model-driven system by 20.3% and 30.1% on seven benchmark datasets. |
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. |
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. |
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. |
Evaluating Instructively Generated Statement by Large Language Models for Directional Event Causality Identification (2025.findings-acl)
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| Challenge: | Existing studies focus on identifying existence of causality between two event mentions, but the direction of causalities is crucial for understanding the causal relation. |
| Approach: | They propose to instruct a GLM to generate causality statements and identify directional event causality by evaluating the generated statements. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods even with fewer training data. |
Large Language Models are Complex Table Parsers (2023.emnlp-main)
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| Challenge: | Extensive experiments and results on Complex Table QA datasets, i.e., the open-domain dataset HiTAB and the aviation domain dataset AIT-QA show that our approach significantly outperforms previous work on both datasets. |
| Approach: | They propose to incorporate Generative Pre-trained Transformer 3.5 to address the specific challenges posed by Complex Table QA by reconstructing tables into tuples and using prompt templates to create dialogues. |
| Outcome: | The proposed approach outperforms previous work on complex table parsing datasets and leads to state-of-the-art (SOTA) performance. |
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. |
Logical Structure as Knowledge: Enhancing LLM Reasoning via Structured Logical Knowledge Density Estimation (2026.findings-acl)
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Zhen Bi, Zhenlin Hu, Xueshu Chen, Mingyang Chen, Cheng Deng, Yida Xue, Zhen Wang, Qing Shen, Ningyu Zhang, Jungang Lou
| Challenge: | Existing data-centric paradigms equate quality with factuality or diversity and ignore the internal logical complexity of training samples. |
| Approach: | They propose a density-aware re-cognizing optimization strategy that prioritizes high-density logical samples to align training with the model's reasoning boundary. |
| Outcome: | The proposed metric outperforms existing methods and improves reasoning performance without increasing total data volume. |
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. |
SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models (2024.acl-long)
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Weixiang Zhao, Shilong Wang, Yulin Hu, Yanyan Zhao, Bing Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che
| Challenge: | Existing methods to address catastrophic forgetting and knowledge transfer in large language models (LLMs) ignore potential of aligning the two modules to effectively address catastrophic forgetting and knowledge transfers simultaneously. |
| Approach: | They propose a Shared Attentive Learning & Selection module to align the PET learning and selection modules to address catastrophic forgetting and knowledge transfer simultaneously. |
| Outcome: | Experiments on two CL benchmarks show that the proposed framework is superior when scaled to different model sizes, different model architectures and unseen tasks. |
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. |
Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) generate only one token at each decoding step, leading to high latency. |
| Approach: | They propose a speculative decoding paradigm that stores tokens in an adjacency matrix and employs a breadth-first-search algorithm to construct a draft tree. |
| Outcome: | The proposed method outperforms existing train-free methods by 30% and even a training method by 25%. |
Extracting the Essence and Discarding the Dross: Enhancing Code Generation with Contrastive Execution Feedback (2025.coling-main)
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| Challenge: | erroneous code generation methods amalgamate feedback and correct code as target sentences . a new approach to code generation with feedback is needed to improve model performance . |
| Approach: | They propose a learning-based code generation model with execution feedback that integrates feedback and correct code as target sentences. |
| Outcome: | a new model with execution feedback shows improvements in generating accurate code and understanding error correction. |
Efficient Universal Goal Hijacking with Semantics-guided Prompt Organization (2025.acl-long)
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| Challenge: | Existing methods for prompt injection have focused on optimizing the suffix, overlooking the role of the prompt. |
| Approach: | They propose a method that incorporates an efficient optimization algorithm and two semantics-guided prompt organization strategies to optimize the suffix sequence for universal goal hijacking. |
| Outcome: | The proposed method can generate a fixed suffix that can concatenate to arbitrary user prompts for universal goal hijacking. |