Papers by Wei Hu
WavLLM: Towards Robust and Adaptive Speech Large Language Model (2024.findings-emnlp)
Copied to clipboard
Shujie Hu, Long Zhou, Shujie Liu, Sanyuan Chen, Lingwei Meng, Hongkun Hao, Jing Pan, Xunying Liu, Jinyu Li, Sunit Sivasankaran, Linquan Liu, Furu Wei
| Challenge: | Recent advances in large language models (LLMs) have expanded their scope to encompass multimodal functions. |
| Approach: | They propose a robust and adaptive speech large language model with dual encoders . they validate the model on universal speech benchmarks and apply it to specialized speech-question-answer datasets based on a CoT approach . |
| Outcome: | The proposed model achieves state-of-the-art performance across a range of speech tasks on the same model size. |
Guiding Neural Machine Translation with Semantic Kernels (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Empirical studies show that our approach gains approximately an improvement of 1 BLEU score on most benchmarks over the Transformer baseline. |
| Approach: | They propose to extract several semantic kernels from a source sentence to capture global semantic information. |
| Outcome: | Empirical results show that the proposed approach improves 1 BLEU score on benchmarks . it is also 1.7 times faster than previous works on average at inference time . |
DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations (2021.acl-long)
Copied to clipboard
| Challenge: | Recent studies on ERC lack the ability to extract and integrate emotional clues from the conversational context. |
| Approach: | They propose a new model that uses multi-turn reasoning modules to extract and integrate emotional clues from conversational context. |
| Outcome: | The proposed model outperforms existing models on three public benchmark datasets and is highly effective and superior to existing models. |
Multiscale Collaborative Deep Models for Neural Machine Translation (2020.acl-main)
Copied to clipboard
| Challenge: | Neural machine translation models with deeper neural networks are difficult to train. |
| Approach: | They propose a MultiScale Collaborative framework to boost gradient back-propagation . they let each encoder block learn a fine-grained representation and enhance it . |
| Outcome: | The proposed framework outperforms baseline models on translation tasks with three translation directions and achieves a BLEU score of 30.56 on the English-to-German task. |
Autoregressive Speech Synthesis without Vector Quantization (2025.acl-long)
Copied to clipboard
Lingwei Meng, Long Zhou, Shujie Liu, Sanyuan Chen, Bing Han, Shujie Hu, Yanqing Liu, Jinyu Li, Sheng Zhao, Xixin Wu, Helen M. Meng, Furu Wei
| Challenge: | MELLE is a novel language modeling approach for text-to-speech synthesis that generates continuous tokens from text . authors demonstrate that it reduces the need for vector quantization and improves model robustness . |
| Approach: | They propose to autoregressively generate continuous mel-spectrogram frames directly from text condition, bypassing vector quantization. |
| Outcome: | The proposed model achieves superior performance across multiple metrics and is more streamlined. |
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification (2022.acl-long)
Copied to clipboard
| Challenge: | Recent studies suggest that pre-trained language models have gained rich knowledge during pre-training. |
| Approach: | They propose to tune pre-trained language models with task-specific prompts to improve and stabilize prompttuning. |
| Outcome: | Extensive experiments on zero and few-shot text classification tasks show that prompt-tuning improves and stabilizes prompttun-ing. |
To Diff or Not to Diff? Structure-Aware and Adaptive Output Formats for Efficient LLM-based Code Editing (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are increasingly used for code editing, yet the full-code generation paradigm suffers from severe efficiency bottlenecks. |
| Approach: | They propose to use a structure-aware diff format to train LLMs to choose the most token-efficient format between a given diff format and full code. |
| Outcome: | The proposed approach matches the most token-efficient format with full-code generation while reducing latency and cost by over 30% on long-code editing tasks. |
Representation Learning with Conditional Information Flow Maximization (2024.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge-theoretic representation learning frameworks are based on the information bottleneck principle, which preserves redundant features irrelevant to the given task. |
| Approach: | They propose a conditional information flow maximization framework to learn sufficient representations for the input data and target task by maximizing both input-representation and representation-label mutual information. |
| Outcome: | The proposed framework can extract noise-invariant sufficient representations for the input data and target task. |
Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods to detect fake news neglect a broader propagation uncertainty issue . Existing studies leverage the user interactions in a social media conversation thread to detect false news. |
| Approach: | They propose a dual graph-based model for improving fake news detection . they propose to explore latent interactions in the actual propagation . |
| Outcome: | The proposed model improves on two real-world datasets showing that it is superior to existing models. |
SDBench: A Survey-based Domain-specific LLM Benchmarking and Optimization Framework (2025.acl-long)
Copied to clipboard
| Challenge: | acquiring domain-specific knowledge often requires professional expert manpower. |
| Approach: | They propose a generic framework for generating evaluation datasets for domain-specific LLMs. |
| Outcome: | The proposed framework reduces the reliance on expert manpower while ensuring that the collected data is uniformly distributed. |
IFlyEA: A Chinese Essay Assessment System with Automated Rating, Review Generation, and Recommendation (2021.acl-demo)
Copied to clipboard
| Challenge: | Automated Essay Assessment (AEA) aims to judge students’ writing proficiency in an automatic way. |
| Approach: | They propose to use Chinese AEA system IFlyEssayAssess to evaluate essays written by native Chinese students from primary and junior schools. |
| Outcome: | The proposed system provides application services for essay scoring, review generation, recommendation, and explainable analytical visualization. |
Knowing the No-match: Entity Alignment with Dangling Cases (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to find entities that cannot find alignment across knowledge graphs (KGs) despite their importance, knowledge graph is expensive and suffers from incompleteness. |
| Approach: | They propose a framework for entity alignment and dangling entity detection that can be used to abstain from predicting alignment for detected dangle entities. |
| Outcome: | The proposed framework can abstain from predicting alignment for detected dangling entities. |
ScalingFilter: Assessing Data Quality through Inverse Utilization of Scaling Laws (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing quality filtering methods rely on a high-quality dataset as reference . Existing methods introduce potential biases and compromise diversity . |
| Approach: | They propose a method that evaluates text quality based on the perplexity difference between two language models trained on the same data. |
| Outcome: | The proposed approach improves performance of pre-trained models without increasing training costs. |
How Do Answer Tokens Read Reasoning Traces? Self-Reading Patterns in Thinking LLMs for Quantitative Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Prior work on activation steering has focused on shaping reasoning traces, but it remains unclear how answer tokens actually read and integrate the reasoning to produce reliable outcomes. |
| Approach: | They propose a training-free steering method that uses self-reading quality scores to guide inference toward benign self-readiness and away from uncertain and disorganized reading. |
| Outcome: | The proposed method yields consistent accuracy gains in the reasoning traces generated by thinking LLMs. |
Attention-Guided Answer Distillation for Machine Reading Comprehension (D18-1)
Copied to clipboard
| Challenge: | Existing approaches to reading comprehension systems are vulnerable to adversarial attacks. |
| Approach: | They propose to use knowledge distillation to transfer knowledge from an ensemble to a single model. |
| Outcome: | The proposed methods outperform the teacher on adversarial datasets and NarrativeQA benchmarks. |
Timeline-based Sentence Decomposition with In Context Learning for Temporal Fact Extraction (2024.acl-long)
Copied to clipboard
| Challenge: | Recent research on temporal fact extraction fails to establish time-to-fact correspondences in complex sentences. |
| Approach: | They propose a timeline-based sentence decomposition strategy using large language models with in-context learning to extract temporal facts from natural language text. |
| Outcome: | The proposed method achieves state-of-the-art on a complex temporal fact extraction dataset. |
ELTLM: Evaluation of Longitudinal Temporal Large Multimodal Models in Clinical Scenarios (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing evaluation benchmarks focus on static evaluation of large multimodal models . existing evaluation paradigms neglect a critical aspect of clinical practice: longitudinal analysis . |
| Approach: | They propose a temporal perception and reasoning benchmark to assess models' temporal grounding and consistency. |
| Outcome: | ELTLM features a hierarchical task taxonomy comprising Temporal Perception QA and Temporal Reasoning QA. |
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)
Copied to clipboard
Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen
| Challenge: | Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval. |
| Approach: | They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs). |
| Outcome: | The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models. |
Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users based on Weakly Supervised Learning (2020.coling-main)
Copied to clipboard
| Challenge: | Existing models for fake news detection are often insufficient or lacking in features . a novel structure-aware multi-head attention network can detect fake news in 4 hours . |
| Approach: | They propose a structure-aware multi-head attention network to detect fake news in mass news . they use credibility of publishers and users as prior weakly supervised information . |
| Outcome: | The proposed model can detect fake news in 4 hours with an accuracy of over 91% . the proposed model is faster than the state-of-the-art models . |
Unsupervised Morphological Tree Tokenizer (2025.findings-acl)
Copied to clipboard
| Challenge: | Conventional statistical tokenizers often disrupt constituent boundaries within words, thereby corrupting semantic information. |
| Approach: | They propose a method that uses morphological structure guidance to induce character-level structures of words by training a deep model. |
| Outcome: | Empirical results show that the proposed method retains complete morphemes and outperforms existing methods on morphological segmentation and language modeling tasks. |
One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues (P19-1)
Copied to clipboard
| Challenge: | Currently, retrieval-based dialogues are performed in shallow ways . a recent study investigated the problem of context-response matching in open-domain . |
| Approach: | They propose a model that lets utterance-response interaction go deep by stacking interaction blocks. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three benchmark data sets. |
Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Multi-agent LLMs are rapidly moving from prototype to real-world use . network topology is a first-order security parameter in multi-aggent systems . |
| Approach: | They propose a framework for comparing topology-conditioned memory leakage in multi-agent LLM systems. |
| Outcome: | The proposed framework evaluates topology-conditioned memory leakage in multi-agent LLM systems. |
Knowledge Graph-Guided Retrieval Augmented Generation (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing studies on RAG focus on semantic retrieval of isolated relevant chunks, which ignore their intrinsic relationships. |
| Approach: | They propose a framework that utilizes knowledge graphs to provide fact-level relationships between chunks, improving the diversity and coherence of the retrieved results. |
| Outcome: | Extensive experiments on the HotpotQA dataset and its variants demonstrate the advantages of KG2RAG compared to existing RAG-based approaches in terms of response quality and retrieval quality. |
Improving Continual Relation Extraction by Distinguishing Analogous Semantics (2023.acl-long)
Copied to clipboard
| Challenge: | Existing works store a small number of typical samples to re-train the model for alleviating forgetting. |
| Approach: | They propose a continual relation extraction model that uses memory-insensitive relation prototypes and memory augmentation to overcome the overfitting problem. |
| Outcome: | The proposed model outperforms existing models on analogous relations and overcomes overfitting problem. |
Uncertainty-Aware Semantic Augmentation for Neural Machine Translation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for neural machine translation only observe one source sentence at training time . this discrepancy in data distribution leads to a formidable learning challenge . |
| Approach: | They propose an uncertainty-aware semantic augmentation approach to capture universal semantic information among multiple source sentences and enhance hidden representations with this information. |
| Outcome: | The proposed approach outperforms baseline and existing methods on translation tasks. |
KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing studies focus on partial aspects of knowledge abstraction, concretization, and completion (KACC). |
| Approach: | They propose a unified knowledge graph benchmark to improve existing benchmarks . they collect new datasets that contain larger concept graphs and cross-view links . |
| Outcome: | The proposed benchmark improves existing benchmarks in terms of dataset scale, task coverage, and difficulty. |
QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing metrics fail to align well with human judgments when evaluating QG questions. |
| Approach: | They propose a multi-dimensional evaluation benchmark for QG and automatic metrics that evaluates questions and automated metrics across 7 dimensions. |
| Outcome: | The proposed benchmark evaluates QG models and automatic metrics across 7 dimensions . it shows that most QG model performs unsatisfactorily in terms of answerability and answer consistency . |
DeepSynth-Eval: Objectively Evaluating Information Consolidation in Deep Survey Writing (2026.findings-acl)
Copied to clipboard
Hongzhi Zhang, Yuanze Hu, Tinghai Zhang, Jia Fu, Tao Wang, Junwei Jing, Zhaoxin Fan, Wei Bi, Ruiming Tang, Han Li, Guorui Zhou, Kun Gai
| Challenge: | Large Language Models (LLMs) are evolving towards autonomous agents . retrieval capabilities are well-benchmarked, but post-retrieval synthesis is under-evaluated due to open-ended writing. |
| Approach: | They propose a benchmark to evaluate information consolidation capabilities using survey papers as gold standards. |
| Outcome: | The proposed benchmark analyzes the post-retrieval synthesis stage of large language models . it leverages high-quality survey papers as gold standards and reverse-engineers research requests . the proposed benchmark outperforms single-turn generation and reduces hallucinations . |
Psychology-guided Controllable Story Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Existing controllable story generation systems ignore the psychological changes of the protagonists and focus on the appointed keywords or emotions. |
| Approach: | They propose a Psychology-guided Controllable Story Generation System (PICS) that generates stories that adhere to the given leading context and desired psychological state chains for the protagonist. |
| Outcome: | The proposed system outperforms baselines and shows that it can generate stories with more consistent psychological changes. |
MAssistant: A Personal Knowledge Assistant for MOOC Learners (D19-3)
Copied to clipboard
| Challenge: | Massive Open Online Courses (MOOCs) have experienced a rapid development since 2012 . many MOOC platforms have been launched, including Coursera1 , edX2 , and Udacity3 etc. |
| Approach: | They present a personal knowledge assistant system called MAssistant for MOOC learners . MAsistants has a large-scale concept graph built from open data . it also provides a browser extension which interacts with users during video lectures . |
| Outcome: | The proposed system helps users trace the concepts they have learned in MOOCs, and to build their own concept graphs. |
Mitigating the Alignment Tax of RLHF (2024.emnlp-main)
Copied to clipboard
Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, Tong Zhang
| Challenge: | Large Language Models (LLMs) acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, which is also known as the alignment tax. |
| Approach: | They propose to use a model averaging technique to find the most powerful alignment-forging Pareto front among RLHF algorithms. |
| Outcome: | The proposed method achieves the strongest alignment-forging Pareto front among competing methods. |
Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots (D19-1)
Copied to clipboard
| Challenge: | Existing studies focus on matching candidate responses with every context utterance, but it also brings noise signals and unnecessary information. |
| Approach: | They propose a multi-hop selector network to match context with candidate responses . they propose to use a selector to filter the relevant utterances as context . |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public multi-turn dialogue datasets. |
Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to recognize emotions have limitations in discovering the intrinsic structure of data relevant to emotion labels, and struggle to extract generalized and robust representations. |
| Approach: | They propose a supervised adversarial contrastive learning framework for learning class-spread structured representations in a controlled manner. |
| Outcome: | The proposed framework can extract generalized and robust representations on three datasets and achieves state-of-the-art performance. |
Unveiling the Unknown: Open-Set Entity Typing via Two-Stage Generation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing fine-grained entity typing models are susceptible to misclassify unknown-type instances . manual collection and annotation of large unknown-Type instances is time-consuming and labor-intensive in open environments. |
| Approach: | They propose a novel task that uses open-set entity typing to classify entities of unknown types . they propose 'two-stage generation model' that automatically produces large-scale pseudo unknown-type instances . |
| Outcome: | The proposed framework surpasses baselines on two newly established benchmark datasets. |
Multi-stream Information Fusion Framework for Emotional Support Conversation (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for ESC do not capture the dynamic transition of emotion intensity due to the difficulty to model its dynamic transition. |
| Approach: | They propose to fuse three streams for the effective modelling of emotion intensity using a multi-stream fusion unit. |
| Outcome: | The proposed model reduces the emotional distress of users with high-intensity of negative emotions by incorporating three different kinds of streams for the dynamic transition of emotion intensity. |
Multi-Aspect Controllable Text Generation with Disentangled Counterfactual Augmentation (2024.acl-long)
Copied to clipboard
| Challenge: | Existing studies neglect attribute correlations formed by the intertwining of different attributes. |
| Approach: | They propose a multi-aspect controllable text generation method with disentangled counterfactual augmentation that alleviates imbalanced attribute correlations during training by disentanglement. |
| Outcome: | The proposed method outperforms state-of-the-art methods in imbalanced and balanced attribute correlation scenarios. |
Mem2ActBench: A Benchmark for Evaluating Long-Term Memory Utilization in Task-Oriented Autonomous Agents (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks focus on direct queries for a factual answer, but fail to evaluate the more crucial capability of actively applying memory to execute tasks. |
| Approach: | They propose a benchmark to evaluate whether agents can proactively leverage long-term memory to execute tool-based actions by selecting appropriate tools and grounding their parameters. |
| Outcome: | The proposed benchmarks show that 91.3% of tasks are memory-dependent . the benchmarks simulate persistent assistant usage, where users mention the same topic across long, interrupted interactions and expect previously established preferences and task states to be implicitly applied. |
CLEAN–EVAL: Clean Evaluation on Contaminated Large Language Models (2024.findings-naacl)
Copied to clipboard
Wenhong Zhu, Hongkun Hao, Zhiwei He, Yun-Ze Song, Jiao Yueyang, Yumeng Zhang, Hanxu Hu, Yiran Wei, Rui Wang, Hongyuan Lu
| Challenge: | Existing methods to evaluate large language models are prone to data contamination. |
| Approach: | They propose a method which parses contaminated data and back-translates it into a candidate set. |
| Outcome: | The proposed method reduces data contamination and evaluates the LLMs more cleanly. |
Multi-Granularity Semantic Revision for Large Language Model Distillation (2026.acl-long)
Copied to clipboard
Xiaoyu Liu, Yun Zhang, Wei Li, Simiao Li, Xudong Huang, Hanting Chen, Yehui Tang, Jie Hu, Zhiwei Xiong, Yunhe Wang
| Challenge: | Existing methods for generating large language models rely on student-generated outputs, which introduce generation errors and misguide the distillation process. |
| Approach: | They propose a multi-granularity semantic revision method for LLM distillation that corrects errors using teacher-generated tokens and re-generates the sequence to minimize errors. |
| Outcome: | The proposed method reduces errors and misguides distillation on student models and improves consistency between teacher and student outputs. |
InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection (2026.eacl-long)
Copied to clipboard
Yuhang Liu, Pengxiang Li, Zishu Wei, Congkai Xie, Xueyu Hu, Xinchen Xu, Shengyu Zhang, Xiaotian Han, Hongxia Yang, Fei Wu
| Challenge: | Existing GUI Agents face challenges in multi-step reasoning and reliance on textual annotations, limiting their effectiveness. |
| Approach: | They propose an MLLM-based GUI Agent with a two-stage supervised fine-tuning pipeline that enhances GUI understanding and grounding. |
| Outcome: | InfiGUIAgent achieves competitive performance on several GUI benchmarks, highlighting the impact of native reasoning skills in enhancing GUI interaction for automation tasks. |
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations (2022.acl-long)
Copied to clipboard
Wei Chen, Yeyun Gong, Can Xu, Huang Hu, Bolun Yao, Zhongyu Wei, Zhihao Fan, Xiaowu Hu, Bartuer Zhou, Biao Cheng, Daxin Jiang, Nan Duan
| Challenge: | Existing studies focus on coarse-grained response selection in retrieval-based dialogue systems. |
| Approach: | They propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations. |
| Outcome: | The proposed model improves over baseline methods on two datasets based on the Reddit comments dump and Twitter corpus compared with baseline methods. |
Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing studies focus on the recognition step, while paying less attention to sign language translation. |
| Approach: | They propose a task-aware instruction network, namely TIN-SLT, for sign language translation, by introducing the isntruction module and the learning-based feature fuse strategy into a Transformer network. |
| Outcome: | The proposed system outperforms existing solutions on two benchmark datasets, PHOENIX-2014-T and ASLG-PC12, and outperformed previous best solutions by 1.65 and 1.42 in terms of BLEU-4. |
MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)
Copied to clipboard
Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen
| Challenge: | Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed . |
| Approach: | They propose a framework that integrates tri-modally aligned cultural benchmarks and a five-dimensional evaluation protocol to assess cross-country awareness disparities. |
| Outcome: | The proposed framework assesses cultural awareness disparities across modalities and languages . it is the first dataset aligned at the input level across text, image, and speech . |
A Black-Box Attack on Code Models via Representation Nearest Neighbor Search (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for generating adversarial code examples face challenges such as limted availability of substitute variables and the creation of adversarials with noticeable perturbations. |
| Approach: | They propose a search seed based on historical attacks to find adversarial substitutes . they employ a pre-trained variable name encoder to map the search seed to a continuous vector space . |
| Outcome: | The proposed approach outperforms baseline methods in terms of ASR and QT. |
Structure-adaptive Adversarial Contrastive Learning for Multi-Domain Fake News Detection (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing models for fake news detection capture domain-shared semantic features but fail to generalize well due to poor adaptability. |
| Approach: | They propose a framework to enable structure knowledge transfer between multiple domains . they compare content-only and propagation-rich data to preserve structural patterns . |
| Outcome: | The proposed framework can learn semantic and structural features across domains. |
A Unified Propagation Forest-based Framework for Fake News Detection (2022.coling-1)
Copied to clipboard
| Challenge: | Recent studies on fake news detection have focused on textual news material, but there is a lack of authoritative regulators. |
| Approach: | They propose a framework to explore latent correlations between propagation trees and a root-induced training strategy to encourage representations of propagation tree to be closer to their prototypical root nodes. |
| Outcome: | The proposed framework explores latent correlations between propagation trees to improve fake news detection. |
Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph (2022.aacl-main)
Copied to clipboard
| Challenge: | Existing knowledge graph question answering methods only search for the answer in a large knowledge graph. |
| Approach: | They propose to partition retrieved knowledge subgraphs into smaller sub-KSGs and then use a graph-augmented learning to rank method to select the top-ranked sub-kSGs. |
| Outcome: | The proposed method can capture global interactions in question and subgraphs and local interactions on the full KSG and top-ranked sub-KSGs respectively. |
Improve Student’s Reasoning Generalizability through Cascading Decomposed CoTs Distillation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have tried to distill these capabilities into smaller language models (SLMs) however, these capabilities are often associated with more parameters, which is not practical to emergent in smaller models. |
| Approach: | They propose to decompose the traditional single-step learning process into two cascaded learning steps by restructuring the training objectives and concatenating the question with the rationale as input. |
| Outcome: | Extensive experiments show that the proposed method improves reasoning generalizability and diversity of the model. |
Neural Response Generation with Meta-words (P19-1)
Copied to clipboard
| Challenge: | Experimental results show that meta-words can be used to generate open domain dialogues . human-machine conversation is a fundamental problem in NLP . |
| Approach: | They propose a goal-tracking memory network that formalizes meta-word expression as a target in response generation and manages the generation process with a state memory panel and a controller. |
| Outcome: | The proposed model outperforms state-of-the-art generation models in response relevance, response diversity, and accuracy. |
Continual Event Extraction with Semantic Confusion Rectification (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies focus on continual event extraction to extract incessantly emerging information . the semantic confusion on event types stems from the annotations of the same text being updated over time . |
| Approach: | They propose a continual event extraction model with semantic confusion rectification to reduce semantic confusion. |
| Outcome: | The proposed model outperforms state-of-the-art models and is proficient in imbalanced datasets. |
CoMoE: Contrastive Representation for Mixture-of-Experts in Parameter-Efficient Fine-tuning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Currently, mixture-of-experts (MoE) is underutilized on heterogeneous datasets, ignoring the fact that experts may learn similar knowledge. |
| Approach: | They propose a method to promote modularization and specialization in MoE by specializing functionalities into different experts and sparsely activating them appropriately. |
| Outcome: | The proposed method improves the capacity and specialization of mixture-of-experts (MoE) by sampling from activated and inactivated experts in top-k routing. |
Every Token Counts: Generalizing 16M Ultra-Long Context in Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | a recent study explores efficient ultra-long context modeling. |
| Approach: | They propose to use Hierarchical Sparse Attention to achieve efficient ultra-long context modeling. |
| Outcome: | The proposed model performs comparable to full-attention baselines on in-domain and out-of-domain tasks. |
Where to Attack: A Dynamic Locator Model for Backdoor Attack in Text Classifications (2022.coling-1)
Copied to clipboard
| Challenge: | BackDoor Attack (BDA) study aims to train a poisoned model with clean data and some trigger-embedded instances to perform normally on normal inputs. |
| Approach: | They propose to train a poisoned model with clean and poisonest inputs . they propose to use triggers to predict those poisonets as target labels . |
| Outcome: | The proposed model can predict P2P dynamically without human intervention. |
Bi-directional CognitiveThinking Network for Machine Reading Comprehension (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods for reading comprehension are still in their infancy at the level of cognitive intelligence. |
| Approach: | They propose a bi-directional cognitive knowledge framework to simulate reverse thinking and inertial thinking in the brain to answer questions. |
| Outcome: | The proposed framework shows that bi-directional knowledge helps the QA task. |
Context-Agent: Dynamic Discourse Trees for Non-Linear Dialogue (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to managing non-linear dialogue flow are misaligned with the intrinsically hierarchical and branching structure of natural discourse. |
| Approach: | They propose a framework that models multi-turn dialogue history as a dynamic tree structure. |
| Outcome: | The proposed framework enhances task completion rates and improves token efficiency across various LLMs. |
Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models (2026.acl-long)
Copied to clipboard
Wei Wu, Liyi Chen, Congxi Xiao, Tianfu Wang, Qimeng Wang, Chengqiang Lu, Yan Gao, null Yiwu, Yao Hu, Hui Xiong
| Challenge: | Existing efficient reasoning methods rely on explicit length penalties for excessive verbosity on simple queries. |
| Approach: | They propose a training-time intervention that selectively suppresses redundant tokens . they find length shift occurs when models generate unnecessary reasoning on trivial inputs - a phenomenon that is often unexplored . |
| Outcome: | The proposed method reduces inference token usage by 78% while increasing accuracy compared to the initial policy and surpasses state-of-the-art efficient reasoning methods. |
Automatic Article Commenting: the Task and Dataset (P18-2)
Copied to clipboard
| Challenge: | Existing methods to make comments on articles are based on human-annotated subsets, but they are not suitable for online forums. |
| Approach: | They propose to use a large-scale Chinese corpus with millions of real comments and a human-annotated subset characterizing the comments’ varying quality to generalize a broad set of popular reference-based metrics. |
| Outcome: | The proposed model incorporates human-annotated subset characterizing the comments’ varying quality and shows that it is more accurate than previous models. |
Capture the Key in Reasoning to Enhance CoT Distillation Generalization (2025.acl-long)
Copied to clipboard
| Challenge: | Existing distillation methods for Large Language Models (LLMs) focus on fine-tuning student SLMs on correct data, resulting in students struggling to learn the key instead of analyzing mistakes according to correct solutions. |
| Approach: | They propose a method that exposes key reasoning steps rather than simple fine-tuning students' CoTs data by using a set of prompts with similar reasoning paths but divergent conclusions. |
| Outcome: | The proposed method improves student SLMs' ability to learn key reasoning steps rather than fine-tuning them on teacher data. |
KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction (2024.acl-long)
Copied to clipboard
Zixuan Li, Yutao Zeng, Yuxin Zuo, Weicheng Ren, Wenxuan Liu, Miao Su, Yucan Guo, Yantao Liu, Lixiang Lixiang, Zhilei Hu, Long Bai, Wei Li, Yidan Liu, Pan Yang, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | None. None.. None! |
| Approach: | None. None.. None! |
| Outcome: | None. None. No. : |
COMMA: Modeling Relationship among Motivations, Emotions and Actions in Language-based Human Activities (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for modeling motivations, emotions and actions in language-based human activities have been limited. |
| Approach: | They propose to model motivations, emotions and actions in language-based human activities using a dataset called Story Commonsense. |
| Outcome: | The proposed model can better reveal the essential relationship between motivations, emotions and actions than existing methods. |
ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents (2025.acl-long)
Copied to clipboard
Zhigen Li, Jianxiang Peng, Yanmeng Wang, Yong Cao, Tianhao Shen, Minghui Zhang, Linxi Su, Shang Wu, Yihang Wu, YuQian Wang, Ye Wang, Wei Hu, Jianfeng Li, Shaojun Wang, Jing Xiao, Deyi Xiong
| Challenge: | Existing models that use Large Language Models (LLMs) show superior performance in various tasks, but lack of controllability leads to unfocused conversations or task failure. |
| Approach: | They propose a standard operating procedure (SOP) framework to regulate dialogue flow by integrating Chain of Thought reasoning and supervised fine-tuning for SOP prediction. |
| Outcome: | The proposed method achieves a 27.95% improvement in action accuracy compared to baseline models based on GPT-3.5 and also shows notable gains for open-source models. |
Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence (2024.findings-acl)
Copied to clipboard
| Challenge: | Emotional Intelligence (EI) is a key concept in the field of human intelligence. |
| Approach: | They propose a method to enhance EI of large language models by naive fine-tuning on EI-related tasks. |
| Outcome: | The proposed method improves EI of two LLM-based assistants without compromising GI. |
Neural Multitask Learning for Simile Recognition (D18-1)
Copied to clipboard
| Challenge: | Simile is a special type of metaphor, where comparators such as like and as are used to compare two objects. |
| Approach: | They propose a neural network framework for simile sentence classification, simile component extraction and language modeling. |
| Outcome: | The proposed framework outperforms rule-based and feature-based approaches in simile sentence classification and simile component extraction tasks. |
EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated impressive ability to role-play humans and replicate complex social dynamics. |
| Approach: | They propose an efficient agent communication language induction for social simulations that reduces token consumption by over 20%. |
| Outcome: | The proposed model reduces token consumption by over 20% while preserving human language. |
Improving Knowledge Graph Completion with Structure-Aware Supervised Contrastive Learning (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing contrastive methods focus on individual triples, overlooking the broader structural connectivities and topologies of KGs. |
| Approach: | They propose a new contrastive learning framework that incorporates four tasks specifically tailored to KG data: Vertex-level CL, Neighbor-level Cl, Path-levelCL, and Relation composition level CL. |
| Outcome: | The proposed framework achieves SOTA performance under standard supervised and low-resource settings. |
Advancing Fine-Grained Visual Understanding with Multi-Scale Alignment in Multi-Modal Models (2025.emnlp-main)
Copied to clipboard
Wei Wang, Zhaowei Li, Qi Xu, Linfeng Li, YiQing Cai, Botian Jiang, Hang Song, Xingcan Hu, Pengyu Wang, Li Xiao
| Challenge: | Recent advances in multi-modal large language models have demonstrated remarkable capabilities in multimodal understanding, reasoning, and interaction. |
| Approach: | They propose a method that effectively aligns and integrates multi-scale knowledge of objects . they use a pipeline that provides over 300K essential training data to enhance alignment . |
| Outcome: | The proposed method effectively aligns and integrates multi-scale knowledge of objects, including texts, coordinates, and images. |
Diagnosing Hidden Instabilities in Model Editing via Uncertainty Quantification (2026.acl-long)
Copied to clipboard
Zihan Gu, TianYi Zhang, Xinyan Zhang, Zhiyuan Wang, Han Zhang, Yuhao Wei, Jiacheng Lu, Tianyi Ma, Xingsheng Zhang, Hua Zhang, Yue Hu
| Challenge: | Existing methods to update large language models (LLMs) without expensive retraining are fragile under single-edit evaluation protocols. |
| Approach: | They propose a framework that characterizes activation-based editing as a constrained intervention on intermediate representations. |
| Outcome: | The proposed method reveals local knowledge conflicts invisible to existing benchmarks. |
Global-to-Local Neural Networks for Document-Level Relation Extraction (2020.emnlp-main)
Copied to clipboard
| Challenge: | Relation extraction (RE) aims to identify the semantic relations between named entities in text. |
| Approach: | They propose a novel relation extraction model that encodes document information in terms of entity global and local representations and context relation representations. |
| Outcome: | The proposed model achieves superior performance on two public datasets for document-level RE. |
PMSS: Pretrained Matrices Skeleton Selection for LLM Fine-tuning (2025.coling-main)
Copied to clipboard
| Challenge: | Low-rank adaptation and its variants have been popular due to their ability to avoid excessive inference costs. |
| Approach: | They propose a low-rank adaptation method that enables high-rank updates with low costs while leveraging semantic and linguistic information inherent in pre-trained weight. |
| Outcome: | The proposed method outperforms LoRA and other fine-tuning methods across tasks with less trainable parameters. |
Multi-Task Representation Alignment on Language Understanding: A Mutual Information Perspective (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to multitask learning fail to address task interference issues . Existing methods focus on task balancing or probabilistic modeling but fail to learn sufficient representations for all target tasks. |
| Approach: | They propose a multi-task representation alignment framework to achieve task-specific alignment and self-alignment on shared representations from a mutual information perspective. |
| Outcome: | The proposed framework outperforms 13 representative MTL methods under label-noisy and data-constrained conditions. |
Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for relation extraction ignore the incompleteness of existing knowledge bases . current methods are too weak and cause noises when training and testing are not based on training data. |
| Approach: | They propose a method to automatically align unstructured text with relation instances in a knowledge base . they use heuristics to leverage the memory mechanism of deep neural networks to find out possible FN samples . |
| Outcome: | Experiments on two wildly-used benchmark datasets show the effectiveness of the proposed method. |
Reasoning in the Dark: Interleaved Vision-Text Reasoning in Latent Space (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing multimodal reasoning methods depend on explicit reasoning steps that require labor-intensive vision-text annotations and inherently introduce significant inference latency. |
| Approach: | They propose a method that integrates visual and visual information into the reasoning process to improve the performance of multimodal LLMs. |
| Outcome: | The proposed method achieves an average performance increase of 5.45% while achieving a speed increase of over 5 times compared to existing methods. |
AutoEvolve: Automatically Evolving Queries for Applicable and Scalable Retrieval-Augmented Generation Benchmarking (2025.findings-emnlp)
Copied to clipboard
Ding-Chu Zhang, Xiaowen Zhang, Yue Fei, Renjun Hu, Xiao-Wen Yang, Zhi Zhou, Baixuan Li, Yu-Feng Li, Xing Shi, Wei Lin
| Challenge: | Existing automated generation methods exhibit Weak Applicability and Weak Scalability . existing methods are limited by their reliance on metadata from specific corpora . |
| Approach: | They propose an approach to generate scalable RAG benchmarks using corpus-agnostic methods . they propose a difficulty-guided metric that directs query evolution process . |
| Outcome: | The proposed approach evolves queries significantly more challenging than existing methods . it is able to dynamically increase difficulty, limiting scalability of the query . |
QueryLink: Leveraging Query-Memory Alignment for Long-Term Reasoning in LLM Agents (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to integrating external memory prioritize memory organization while overlooking a critical semantic gap between implicit, intent-driven queries and explicit, narrative-based memories. |
| Approach: | They propose a framework that leverages Query-Memory Alignment to project both queries and memories into a shared semantic space. |
| Outcome: | The proposed framework significantly outperforms SOTA methods on the LoCoMo and LongMemEval benchmarks and can be integrated as a plug-and-play component to boost existing vector-based systems like A-MEM. |
Bootstrapping Code Translation with Weighted Multilanguage Exploration (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods to improve code translation depend on abundant parallel code of high quality, which may not always be available. |
| Approach: | They propose a method that leverages functional invariance and cross-lingual portability of test suites to serve as universal verification oracles for multilingual reinforcement learning. |
| Outcome: | The proposed method leverages functional invariance and cross-lingual portability of test suites to serve as universal verification oracles for multilingual reinforcement learning (RL) training. |
Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection (2021.acl-long)
Copied to clipboard
| Challenge: | Existing studies on rumor detection focus on text content and propagation structure . however, the uncertainty caused by unreliable relations in propagation structures is common . |
| Approach: | They propose a Bayesian-based model that captures propagation uncertainty for rumor detection. |
| Outcome: | The proposed model achieves better performance than baseline methods on rumor detection and early rumour detection tasks. |
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)
Copied to clipboard
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. |
CT-GAT: Cross-Task Generative Adversarial Attack based on Transferability (2023.emnlp-main)
Copied to clipboard
| Challenge: | Neural network models are vulnerable to adversarial examples, and current methods based on adversarially transferable models rely on substitute models, which can be impractical and costly in real-world scenarios due to the unavailability of training data and the victim model’s structural details. |
| Approach: | They propose a novel approach that directly constructs adversarial examples by extracting transferable features across various tasks. |
| Outcome: | The proposed approach achieves superior attack performance with small cost on ten datasets and demonstrates that it is a novel approach. |
DORA: Dynamic Optimization Prompt for Continuous Reflection of LLM-based Agent (2025.coling-main)
Copied to clipboard
| Challenge: | Existing studies have shown that reflection can enhance performance, but our investigation reveals an undesirable pattern in reflection framework: effective self-reflection occurs primarily at the beginning of iterations, with subsequent attempts failing to produce further improvements. |
| Approach: | They propose a framework that generates task-adaptive reflection advice using an external open-source small language model. |
| Outcome: | The proposed framework generates task-adaptive and diverse reflection advice in MiniWoB++ and Alfworld environments. |
Learning to Focus on the Foreground for Temporal Sentence Grounding (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for temporal sentence grounding do not capture subtle details of small objects. |
| Approach: | They propose a detection-free framework for temporal sentence grounding that learns to locate foreground regions related to the query in consecutive frames. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on three challenging datasets. |
AIR: Complex Instruction Generation via Automatic Iterative Refinement (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for generating complex instructions are resource-intensive and lack diversity. |
| Approach: | They propose a framework to generate complex instructions with constraints using a document-generated initial instruction and an iterative refinement framework to incorporate LLM-as-judge guidance. |
| Outcome: | The proposed framework significantly outperforms existing methods for generating complex instructions, and outperformed existing methods. |
STRUX: An LLM for Decision-Making with Structured Explanations (2025.naacl-short)
Copied to clipboard
| Challenge: | a new LLM decision-making framework is designed to help users understand how and why decisions are made. |
| Approach: | They introduce a new LLM decision-making framework called STRUX that provides structured explanations for LLM decisions. |
| Outcome: | The proposed framework improves decision-making by providing structured explanations . it has been evaluated on the task of forecasting stock investment decisions based on earnings call transcripts - superior performance against strong baselines compared with previous frameworks based upon earnings call transcriptions demonstrating superior performance . |
Adversarial Preference Learning for Robust LLM Alignment (2025.findings-acl)
Copied to clipboard
Yuanfu Wang, Pengyu Wang, Chenyang Xi, Bo Tang, Junyi Zhu, Wenqiang Wei, Chen Chen, Chao Yang, Jingfeng Zhang, Chaochao Lu, Yijun Niu, Keming Mao, Zhiyu Li, Feiyu Xiong, Jie Hu, Mingchuan Yang
| Challenge: | Modern language models rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors, but they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation; (2) the vast diversity of potential adversarials; and (3) the risk of feedback bias and reward hacking. |
| Approach: | They propose an iterative adversarial training method that incorporates three key innovations to address these challenges. |
| Outcome: | Experiments on Mistral-7B-Instruct-v0.3 show that the proposed method significantly enhances robustness and reduces harmful outputs from 5.88% to 0.43%. |
Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering (D19-1)
Copied to clipboard
| Challenge: | Existing approaches build universal paraphrasing or ranking models for whole questions . current approaches build a universal ranking model for the whole questions, which fails for complex, long-tail questions. |
| Approach: | They propose a new query generation approach based on frequent query substructures which helps rank existing query structures or build new query structures. |
| Outcome: | The proposed approach significantly outperforms existing models on two benchmark datasets. |
Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)
Copied to clipboard
Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen
| Challenge: | Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. |
| Approach: | They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
| Outcome: | The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods to generate correct code completions in private repositories are insufficiently relevant. |
| Approach: | They propose a dataflow-guided retrieval augmentation approach for repository-level code completion . they parses a private repository into code entities and establishes their relations through an extended dataflow analysis . |
| Outcome: | The proposed method improves code exact match and identifier F1-score by 3.43% compared to the state-of-the-art approach. |
Does Memory Need Graphs? A Unified Framework and Empirical Analysis for Long-Term Dialog Memory (2026.acl-long)
Copied to clipboard
| Challenge: | Existing literature on dialog memory systems is inconsistent on their effectiveness . empirical findings on graph structures are difficult to attribute to specific design choices . |
| Approach: | They propose a framework that decomposes dialog memory systems into core components . they conduct stage-wise experiments on LongMemEval and HaluMeM, and compare implementation details . |
| Outcome: | The proposed framework compares graph-based and non-graph memory architectures on long-term dialog memory systems. |
DataSciBench: An LLM Agent Benchmark for Data Science (2026.findings-acl)
Copied to clipboard
Dan Zhang, Sining Zhoubian, Min Cai, Fengzu Li, Lekang Yang, Wei Wang, Tianjiao Dong, Ziniu Hu, Jie Tang, Yisong Yue
| Challenge: | Existing benchmarks focus on single task, simple evaluation metrics, and readily available ground truth (GT) DataSciBench is built on curated, natural, and challenging prompts with complex evaluation criteria and uncertain GT. |
| Approach: | They propose a benchmark for evaluating Large Language Models in data science that integrates LLM-based self-consistency and human verification to ensure accuracy. |
| Outcome: | The proposed framework outperforms open-source models in all metrics and offers rigorous insights into LLM strengths and weaknesses. |
Skill-Aware Data Selection and Fine-Tuning for Data-Efficient Reasoning Distillation (2026.acl-short)
Copied to clipboard
| Challenge: | Large reasoning models such as DeepSeek-R1 and their distilled variants achieve impressive performance on complex reasoning tasks, yet their costs remain substantial. |
| Approach: | They propose a skill-centric distillation framework that efficiently transfers reasoning ability to weaker models with two components: (1) Skill-based data selection, which prioritizes examples targeting the student model’s weaker skills, and (2) Skillaware fine-tuning, which encourages explicit skill decomposition during problem solving. |
| Outcome: | The proposed framework surpasses baselines on Qwen3-4B and Qwend3-8B and focuses on skills emphasized during training. |
Lost in Execution: On the Multilingual Robustness of Tool Calling in Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are increasingly deployed as agents that invoke external tools through structured function calls. |
| Approach: | They introduce a diagnostic benchmark and conduct a systematic evaluation of multilingual tool calling across Chinese, Hindi, and the low-resource language Igbo. |
| Outcome: | The proposed benchmarks show that multilingual tool calling fails despite correct intent understanding and tool selection. |
HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy (2026.acl-industry)
Copied to clipboard
| Challenge: | relying on large language models for information has raised concerns about reliability and accuracy of outputs. |
| Approach: | They propose a hallucination taxonomy with 11 categories for various NLG tasks and propose HAllucination Detection models which integrate hallucinism detection, span-level identification, and correction into a single inference process. |
| Outcome: | The proposed models outperform baselines on HaluEval, FactCHD, and FaithBench, confirming their robustness and versatility. |
LegalAgentBench: Evaluating LLM Agents in Legal Domain (2025.acl-long)
Copied to clipboard
Haitao Li, Junjie Chen, Jingli Yang, Qingyao Ai, Wei Jia, Youfeng Liu, Kai Lin, Yueyue Wu, Guozhi Yuan, Yiran Hu, Wuyue Wang, Yiqun Liu, Minlie Huang
| Challenge: | Existing general-domain benchmarks do not capture complexity of real-world judicial cognition and decision-making. |
| Approach: | They propose a benchmark specifically designed to evaluate LLM Agents in the legal domain. |
| Outcome: | The proposed benchmark includes 17 corpora from real-world legal scenarios and provides 37 tools for interacting with external knowledge. |
Implicit Relation Linking for Question Answering over Knowledge Graph (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing methods rely on textual similarities between NL and KG to build relation links. |
| Approach: | They propose an implicit relation linking method called ImRL which links relation phrases in NL to relation paths in KG. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on two benchmarks and a newly-created datasets. |
Structure-aware Propagation Generation with Large Language Models for Fake News Detection (2025.findings-emnlp)
Copied to clipboard
| Challenge: | propagation-based methods for fake news detection often lack structural data . authors propose a structure-aware synthetic propagation enhanced detection framework . |
| Approach: | They propose a structure-aware synthetic propagation enhanced detection framework to capture real-world propagation. |
| Outcome: | The proposed framework captures structural dynamics from real propagation, while ignoring structural patterns. |
Narrative Order Aware Story Generation via Bidirectional Pretraining Model with Optimal Transport Reward (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing storytelling systems suffer from insufficient understanding of event correlations and inadequate awareness of event temporal order. |
| Approach: | They propose a narrative order aware framework to generate coherent stories with flashbacks . they propose 'bidirectional pretraining model with Optimal Transport Reward' to improve quality . |
| Outcome: | The proposed framework generates coherent stories with flashbacks with a novel optimal transport reward. |
Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering (2025.acl-long)
Copied to clipboard
| Challenge: | Empirical results show that ChainRAG consistently outperforms baselines in both effectiveness and efficiency. |
| Approach: | They propose a method which sequentially handles each sub-question by completing missing key entities and retrieving relevant sentences from a sentence graph for answer generation. |
| Outcome: | The proposed method outperforms baselines on three multi-hop QA datasets. |
GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs (2026.findings-acl)
Copied to clipboard
Meixiu Long, Duolin Sun, Dan Yang, Yihan Jiao, Lei Liu, Jiahai Wang, Binbin Hu, Yue Shen, Jie Feng, Zhehao Tan, Junjie Wang, Lianzhen Zhong, Jian Wang, Peng Wei, Jinjie Gu
| Challenge: | Existing rankers excel in lexical-matching scenarios, while they struggle with complex queries requiring deep reasoning. |
| Approach: | They propose a new paradigm that balances flexibility and context awareness to unlock the full potential of groupwise reranking. |
| Outcome: | The proposed approach achieves a state-of-the-art 65.2 NDCG@10 on BRIGHT and surpasses baselines by 2.1 points on R2MED while delivering a 6.4 inference speedup. |
SATA: A Paradigm for LLM Jailbreak via Simple Assistive Task Linkage (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to design sophisticated instructions for the LLM to follow, or rely on multiple iterations, could hinder the performance and efficiency of jailbreaks. |
| Approach: | They propose a simple assistive task linkage paradigm which masks harmful keywords within malicious queries and uses a masked language model task to encode the semantics of the mangled keywords. |
| Outcome: | The proposed paradigm can effectively circumvent LLM safeguards and elicit harmful responses. |
Mixture of LoRA Experts for Continual Information Extraction with LLMs (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to continual information extraction are either task-specialized for a single task or suffer from catastrophic forgetting and insufficient knowledge transfer in continual IE. |
| Approach: | They propose a continual IE model that uses token-level mixture of LoRA experts with LLMs to extract emerging information across diverse IE tasks incessantly while avoiding forgetting. |
| Outcome: | The proposed model achieves state-of-the-art performance, effectively mitigating catastrophic forgetting and enhancing knowledge transfer in continual IE. |
Playing 20 Question Game with Policy-Based Reinforcement Learning (D18-1)
Copied to clipboard
| Challenge: | The 20 Questions (Q20) game encourages deductive reasoning and creativity. |
| Approach: | They propose a policy-based Reinforcement Learning method which learns optimal question selection . the method is robust to noisy answers and uses a reward network to estimate the more informative reward . |
| Outcome: | The proposed method outperforms an entropy-based engineering system and has competitive performance in noisy-free simulation environment. |
LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models (2025.acl-long)
Copied to clipboard
Zhiyuan Hu, Yuliang Liu, Jinman Zhao, Suyuchen Wang, WangYan WangYan, Wei Shen, Qing Gu, Anh Tuan Luu, See-Kiong Ng, Zhiwei Jiang, Bryan Hooi
| Challenge: | Large language models face significant challenges in handling long-context tasks because of their limited effective context window size during pretraining, which restricts their ability to generalize over extended sequences. |
| Approach: | They propose a training strategy for extending the context window of LLMs including impactful token analysis, position index transformation, and training optimization strategies. |
| Outcome: | Experiments on three types of LLMs show that LongRecipe can utilize long sequences while requiring only 30% of the target context window size. |
Breaking the Stage Barrier: A Novel Single-Stage Approach to Long Context Extension for Large Language Models (2025.coling-main)
Copied to clipboard
Haoran Lian, Junmin Chen, Wei Huang, Yizhe Xiong, Wenping Hu, Guiguang Ding, Hui Chen, Jianwei Niu, Zijia Lin, Fuzheng Zhang, Di Zhang
| Challenge: | Recent studies show that Large language models struggle with handling long token sequences due to limited training context size. |
| Approach: | They propose a single-stage continual pretraining method to equip LLMs with long context modeling capabilities. |
| Outcome: | The proposed method outperforms existing methods on 4 language modeling benchmarks. |
SaCa: A Highly Compatible Reinforcing Framework for Knowledge Graph Embedding via Structural Pattern Contrast (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing knowledge Graph Embedding approaches lack structural semantics of knowledge graphs . structure-aware calibration (SaCa) is a framework designed to calibrate KGEs based on global structural patterns. |
| Approach: | a new framework is designed to calibrate knowledge graphs using global structural patterns. |
| Outcome: | a new framework can calibrate KGE models using global structural patterns . the framework consistently boosts performance across ten models on link prediction and entity classification tasks . |
SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions (2024.emnlp-industry)
Copied to clipboard
Zhi-Qi Cheng, Yifei Dong, Aike Shi, Wei Liu, Yuzhi Hu, Jason O’Connor, Alexander Hauptmann, Kate Whitefoot
| Challenge: | EV battery supply chain is vulnerable to disruptions caused by natural disasters and geopolitical tensions. |
| Approach: | They propose a system integrating Large Language Models with domain expertise for EV supply chain risk assessment. |
| Outcome: | Evaluated on 12,070 paragraphs from 365 sources (2022-2023), SHIELD outperforms baseline GCNs and LLM+prompt methods in disruption prediction. |
CloneMem: Benchmarking Long-Term Memory for AI Clones (2026.acl-long)
Copied to clipboard
| Challenge: | Existing memory benchmarks rely on user–agent conversational histories, which are temporally fragmented and insufficient for capturing continuous life trajectories. |
| Approach: | They propose a benchmark for evaluating long-term memory in AI Clone scenarios grounded in non-conversational digital traces, including diaries, social media posts, and emails, spanning one to three years. |
| Outcome: | Experiments show that existing memory benchmarks struggle in this setting, highlighting open challenges for life-grounded personalized AI. |
Dynamic Spatial-Temporal Aggregation for Skeleton-Aware Sign Language Recognition (2024.lrec-main)
Copied to clipboard
| Challenge: | Current sign language recognition methods use spatial graphs and temporal modules to capture spatial and temporal features, but their spatial graph modules are typically built on fixed graph structures. |
| Approach: | They propose a new spatial architecture that captures input-sensitive joint relationships and a temporal module to model multi-scale temporal information to capture complex human dynamics. |
| Outcome: | The proposed method achieves state-of-the-art accuracy on four large-scale SLR benchmarks. |
DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs (2025.emnlp-main)
Copied to clipboard
Minxuan Lv, Zhenpeng Su, Leiyu Pan, Yizhe Xiong, Zijia Lin, Hui Chen, Wei Zhou, Jungong Han, Guiguang Ding, Wenwu Ou, Di Zhang, Kun Gai, Songlin Hu
| Challenge: | Existing sparsification methods like pruning can lose model knowledge through parameter removal. |
| Approach: | They propose a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks. |
| Outcome: | The proposed approach achieves superior performance across language modeling and downstream tasks under equivalent computational constraints. |
Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation (2025.findings-emnlp)
Copied to clipboard
Tong Li, Shu Yang, Junchao Wu, Jiyao Wei, Lijie Hu, Mengdi Li, Derek F. Wong, Joshua R. Oltmanns, Di Wang
| Challenge: | Existing data on suicidal ideation in private conversations are limited . a new dataset of 1,200 test cases is presented to address this gap . |
| Approach: | They propose a dataset of 1,200 test cases simulating implicit suicidal ideation in private contexts. |
| Outcome: | The proposed dataset includes 1,200 test cases simulating implicit suicidal ideation in dialogue scenarios. |
LLMs Can Simulate Standardized Patients via Agent Coevolution (2025.acl-long)
Copied to clipboard
Zhuoyun Du, LujieZheng LujieZheng, Renjun Hu, Yuyang Xu, Xiawei Li, Ying Sun, Wei Chen, Jian Wu, Haolei Cai, Haochao Ying
| Challenge: | Training medical personnel using standardized patients (SPs) remains a complex challenge, necessitating extensive domain expertise and role-specific practice. |
| Approach: | They propose a simulated patient framework that allows patient agents to simulate diagnostic process through multi-turn dialogues. |
| Outcome: | The proposed framework improves over existing reasoning methods by more than 10% in requirement alignment and better human preference after evolving over 200 cases for 10 hours with excellent generalizability. |
Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model (C18-1)
Copied to clipboard
| Challenge: | Existing research explores different text features of reply comments on word level and ignores interactions between participants. |
| Approach: | They propose a co-attention mechanism based neural network to capture interactions between participants on argument level to better model dialogical argumentation. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a publicly available dataset showing that it extracts interactive argument pairs from the original post and the reply. |
Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)
Copied to clipboard
Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang
| Challenge: | Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level. |
| Approach: | They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context. |
| Outcome: | The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set. |
Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation (2022.acl-long)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) tasks require large amounts of parallel data to augment training. |
| Approach: | They propose a data augmentation paradigm that augments each training instance with an adjacency semantic region that could cover adequate variants of literal expression under the same meaning. |
| Outcome: | The proposed paradigm improves on the state-of-the-art in supervised neural machine translation tasks. |
Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion Cause Pair Extraction (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to extract emotions and causes as pairs neglect effective semantic connections between distant clauses, leading to poor generalization ability towards position-insensitive data. |
| Approach: | They propose a novel multi-granularity semantic-aware Graph model to integrate fine-grained and coarse-grain semantic features together without regard to distance limitation. |
| Outcome: | The proposed model outperforms existing models significantly in position-insensitive data. |
A Data-Centric Framework for Composable NLP Workflows (2020.emnlp-demos)
Copied to clipboard
Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Xiaodan Liang, Teruko Mitamura, Eric Xing, Zhiting Hu
| Challenge: | Empirical natural language processing (NLP) systems involve interoperation among multiple components . a wealth of NLP toolkits exist ( 4), such as spaCy, DKPro, CoreNLP. |
| Approach: | They propose a unified open-source framework that supports fast development of NLP workflows . framework includes processors for NLP tasks, visualization, and annotation . |
| Outcome: | The framework offers processors for NLP tasks, visualization, and annotation, and is extensible . it is delivered through two modularized yet integratable open-source projects, Forte and Stave . |
MMUnlearner: Reformulating Multimodal Machine Unlearning in the Era of Multimodal Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in machine learning (MU) have enabled the selective removal of private or sensitive information encoded within deep neural networks. |
| Approach: | They propose to "reformulate" the task of multimodal MU in the era of MLLMs by preserving only the visual patterns associated with a given entity while preserving the corresponding textual knowledge. |
| Outcome: | The proposed method surpasses baselines that finetuned MLLMs with VQA data directly through Gradient Ascent (GA) or Negative Preference Optimization (NPO), across all evaluation dimensions. |
MiLe Loss: a New Loss for Mitigating the Bias of Learning Difficulties in Generative Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing generative language models neglect an inherent challenge in text corpus during training, i.e., the imbalance between frequent tokens and infrequent ones. |
| Approach: | They propose a function to mitigate the imbalance between frequent and infrequent tokens . authors propose 'MiLe Loss' function to assess learning difficulty of tokens during training . |
| Outcome: | Experiments show that models with proposed model can improve on downstream benchmarks. |
Serial Contrastive Knowledge Distillation for Continual Few-shot Relation Extraction (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing models for few-shot relation extraction (RE) are not suitable for continual few-sshot RE. |
| Approach: | They propose a new model to train a model for new relations with few labeled training data. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets. |
Dynamic Simulation Framework for Disinformation Dissemination and Correction With Social Bots (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Current studies rely on simplistic user and network modeling and neglect dynamic behavior of bots. |
| Approach: | They propose a multi-agent-based framework for disinformation dissemination . it incorporates both malicious and legitimate bots and allows quantitative evaluation of correction strategies. |
| Outcome: | The proposed framework incorporates both malicious and legitimate bots and their controlled dynamic participation allows for quantitative analysis of correction strategies. |
Regularized Contrastive Decoding with Hard Negative Samples for LLM Hallucination Mitigation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models are prone to generate hallucinations, which can undermine their reliability in high-stakes applications. |
| Approach: | They propose a method to capture hallucination signals for mitigating hallucis in large language models by regularizing the model's internal signals to a weaker model . |
| Outcome: | The proposed method achieves better hallucination mitigation performance on four benchmarks. |
Dial-MAE: ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systems (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing studies on dialogue response selection focus on post-training and fine-tuning for cross-encoders. |
| Approach: | They propose a post-training technique tailored for dense encoders in dialogue response selection . they propose 'Dialogue Contextual Masking Auto-Encoder' which compresses dialogue semantics into dense vectors . |
| Outcome: | The proposed technique achieves state-of-the-art on two commonly evaluated benchmarks. |
KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for parameter-efficient finetuning (PEFT) are limited and only finetune a small number of parameters using limited instruction data. |
| Approach: | They propose a method that inserts an adaptation layer into an LLM to integrate embeddings of entities appearing in the input text. |
| Outcome: | The proposed method can activate parameterized knowledge in an LLM without changing its parameters or input prompts. |
Evaluating Test-Time Scaling LLMs for Legal Reasoning: OpenAI o1, DeepSeek-R1, and Beyond (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Experimental results show that Legal-R1 delivers competitive performance across diverse tasks. |
| Approach: | They propose to evaluate 12 large language models across 17 legal tasks across statutory and case-law traditions to determine their general reasoning performance. |
| Outcome: | The proposed model performs well across 17 legal tasks across statutory and case-law traditions. |
Privacy Checklist: Privacy Violation Detection Grounding on Contextual Integrity Theory (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing privacy studies focus on sub-fields, but they focus on a few sub-domains. |
| Approach: | They propose to use the Health Insurance Portability and Accountability Act of 1996 as an example to develop a checklist that covers social identities, private attributes, and existing privacy regulations. |
| Outcome: | The proposed checklist covers social identities, private attributes, and existing privacy regulations. |
From Style to Story: A Curriculum Learning Approach for Imitative Novel Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Novels create rich, immersive worlds with intricate plots and distinct styles, captivating readers through complex storytelling. |
| Approach: | They propose a novel generation system that imitates novel elements by predicting plot developments and writing concrete details using vivid, expressive language. |
| Outcome: | The novel imitative novel generation system is trained through a curriculum learning paradigm, progressing from low-level stylistic mastery to high-level narrative coherence. |
OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use (2025.acl-long)
Copied to clipboard
Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, Yuhuai Li, Shengze Xu, Shenzhi Wang, Xinchen Xu, Shuofei Qiao, Zhaokai Wang, Kun Kuang, Tieyong Zeng, Liang Wang, Jiwei Li, Yuchen Eleanor Jiang, Wangchunshu Zhou, Guoyin Wang, Keting Yin, Zhou Zhao, Hongxia Yang, Fan Wu, Shengyu Zhang, Fei Wu
| Challenge: | a new generation of (M)LLMs is enabling the creation of superintelligent AI assistants . OS Agents can complete tasks autonomously and have the potential to significantly enhance the lives of billions of users worldwide. |
| Approach: | They propose to build OS Agents that operate within operating systems' GUIs and GUIs . they examine evaluation metrics and benchmarks to identify promising directions . |
| Outcome: | The proposed agents are based on operating systems (OS) and operating systems frameworks. |
Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale (2024.acl-long)
Copied to clipboard
| Challenge: | Existing syntactic language models require a gold tree and sequential training to generate sentences. |
| Approach: | They propose an unsupervised syntactic language model that incrementally generates a sentence with its syntaktic tree in a left-to-right manner. |
| Outcome: | The proposed model outperforms existing models on grammar induction and comprehension tasks while holding a substantial acceleration on training. |
Avoiding Knowledge Edit Skipping in Multi-hop Question Answering with Guided Decomposition (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for knowledge editing fail to work in multi-hop question answering due to 'edit skipping' edit skipping occurs due to the mismatch between the granularity of LLMs in problem-solving and the facts in the edited memory. |
| Approach: | They propose a retrieval-augmented generation-based method that edits knowledge without modifying parameters without retraining LLMs. |
| Outcome: | The proposed method outperforms state-of-the-art methods for KE in multi-hop question answering. |
iPET: An Interactive Emotional Companion Dialogue System with LLM-Powered Virtual Pet World Simulation (2025.acl-demo)
Copied to clipboard
Zheyong Xie, Shaosheng Cao, Zuozhu Liu, Zheyu Ye, Zihan Niu, Chonggang Lu, Tong Xu, Enhong Chen, Zhe Xu, Yao Hu, Wei Lu
| Challenge: | Existing approaches to role-playing emotional companion products lack sustained personalization and contextual adaptability, limiting their effectiveness in real-world settings. |
| Approach: | They propose a virtual pet agent that can enhance user engagement through rich, dynamic pet behaviors and interactions tailored to individual preferences. |
| Outcome: | The proposed system has been deployed in a real-world, non-commercial product for 200 days and has demonstrated its effectiveness in practical applications. |
Investigating and Enhancing Vision-Audio Capability in Omnimodal Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent years have witnessed significant advancements in large language models (LLMs) but still struggle with integrating vision and audio. |
| Approach: | They propose a self-knowledge distillation method to improve vision-audio capabilities of OLLMs by learning from the vision-text components. |
| Outcome: | The proposed method improves vision-audio capabilities of OLLMs by learning from vision-text components, which improves interaction between audio and images and results in improved performance on multimodal tasks. |
Removal of Hallucination on Hallucination: Debate-Augmented RAG (2025.acl-long)
Copied to clipboard
| 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. |
Label-Specific Dual Graph Neural Network for Multi-Label Text Classification (2021.acl-long)
Copied to clipboard
| Challenge: | Existing studies for multi-label text classification do not explore label-specific semantic components from documents. |
| Approach: | They propose a label-specific dual graph neural network that incorporates category information to learn label-related components from documents. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark datasets and achieves better performance with respect to tail labels. |
Knowledge Association with Hyperbolic Knowledge Graph Embeddings (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for knowledge graphs (KGs) depend on high embedding dimensions and hierarchical structures to achieve expressiveness. |
| Approach: | They propose a hyperbolic relational graph neural network for KG embedding and capture knowledge associations with a high-dimensional transformation. |
| Outcome: | Experiments on entity alignment and type inference show the proposed method is effective and efficient. |
TeCES: Collaborative Geometric Knowledge Representation Framework under Evolving Fact Snapshots (2026.acl-long)
Copied to clipboard
Jiujiang Guo, Zhengliang Guo, Kai Wang, Meiyang Wang, Dehua Peng, Shaozu Yuan, Chengyin Hu, Shuan Ai, Yiwei Wei
| Challenge: | Existing knowledge graphs represent static facts but lack collaborative modeling of both . e.g., existing knowledge graph models lack a framework for integrating snapshots into knowledge graph. |
| Approach: | They propose a framework for high-fidelity modeling of evolving snapshots using concept of snapshots. |
| Outcome: | The proposed framework outperforms existing models on six benchmarks. |
PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models (2024.acl-long)
Copied to clipboard
Haoran Li, Dadi Guo, Donghao Li, Wei Fan, Qi Hu, Xin Liu, Chunkit Chan, Duanyi Yao, Yuan Yao, Yangqiu Song
| Challenge: | generative large language models (LLMs) exhibit surprising capability and integrate previous tasks into a unified text generation formulation. |
| Approach: | They propose a privacy evaluation benchmark to quantify the privacy leakage of language models. |
| Outcome: | The proposed benchmark compares PPLMs with different privacy implementations to find out how privacy leakage is handled. |
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)
Copied to clipboard
Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang
| Challenge: | Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction. |
| Approach: | They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack. |
| Outcome: | The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. |
OpenRLHF: A Ray-based Easy-to-use, Scalable and High-performance RLHF Framework (2025.emnlp-demos)
Copied to clipboard
Jian Hu, Xibin Wu, Wei Shen, Jason Klein Liu, Weixun Wang, Songlin Jiang, Haoran Wang, Hao Chen, Bin Chen, Wenkai Fang, null Xianyu, Yu Cao, Haotian Xu, Yiming Liu
| Challenge: | Existing RLHF frameworks face inference bottlenecks and complexity barriers restricting their accessibility for newcomers. |
| Approach: | They propose an open-source RLHF framework that can be used to train large language models. |
| Outcome: | The proposed framework achieves superior training efficiency with speedups ranging from 1.22 to 1.68 across different model sizes compared to state-of-the-art frameworks, while requiring significantly fewer lines of code for implementation. |
Transferable Direct Prompt Injection via Activation-Guided MCMC Sampling (2025.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results show superior cross-model transferability . Prompt injection attacks are among the most critical threats . |
| Approach: | They propose an activations-guided prompt injection attack framework to address the impracticality of existing white-box/gray-box methods and the poor transferability of black-box approaches. |
| Outcome: | The proposed framework achieves 49.6% success rate and 34.6% improvement over human-crafted prompts on five mainstream LLMs. |
DynaCode: A Dynamic Complexity-Aware Code Benchmark for Evaluating Large Language Models in Code Generation (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing code benchmarks for large language models remain static, resulting in data contamination and unreliable evaluation results. |
| Approach: | They propose a dynamic, complexity-aware benchmark that overcomes the limitations of static datasets and provides a memorization-advantaged benchmark. |
| Outcome: | DynaCode generates 189 million unique nested code problems across 4 units of code complexity and 16 types of call graphs. |
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (2022.acl-long)
Copied to clipboard
Wei Chen, Yeyun Gong, Song Wang, Bolun Yao, Weizhen Qi, Zhongyu Wei, Xiaowu Hu, Bartuer Zhou, Yi Mao, Weizhu Chen, Biao Cheng, Nan Duan
| Challenge: | Existing pre-trained dialog models shed light on various downstream tasks in natural language processing (NLP). |
| Approach: | They propose a dialog pre-training framework that introduces latent variables into the enhanced encoder-decoder pre-train framework to increase relevance and diversity of responses. |
| Outcome: | The proposed model achieves state-of-the-art on personaChat, DailyDialog, and DSTC7-AVSD datasets. |
Impartial Multi-task Representation Learning via Variance-invariant Probabilistic Decoding (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods focus on balancing loss or gradients but fail to address this issue due to the representation discrepancy in latent space. |
| Approach: | They propose a framework that harmonizes representation spaces across tasks to ensure impartial learning by harmonizing representation spaces. |
| Outcome: | The proposed framework outperforms 12 representative methods under the same multi-task settings, especially in heterogeneous task combinations and data-constrained scenarios. |
Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing models struggle on the text-to-SQL benchmarks, but we propose a method to improve their generalization ability. |
| Approach: | They propose a method to improve the combinatorial generalization of Text-to-SQL models by aligning previous SQL statements with the input utterance. |
| Outcome: | The proposed method improves the generalization ability of Text-to-SQL models. |
IntelliCockpitBench: A Comprehensive Benchmark to Evaluate VLMs for Intelligent Cockpit (2025.findings-acl)
Copied to clipboard
| Challenge: | Visual Question Answering (VQA) is a key task in vehicular systems. |
| Approach: | They propose a benchmark that encompasses diverse automotive scenarios . they use images from front, side, and rear cameras, various road types, weather conditions, and interior views . |
| Outcome: | The proposed benchmark includes images from front, side, and rear cameras, various road types, weather conditions, and interior views. |