Papers with SOTA methods
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development (2023.acl-demo)
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Avi Sil, Jaydeep Sen, Bhavani Iyer, Martin Franz, Kshitij Fadnis, Mihaela Bornea, Sara Rosenthal, Scott McCarley, Rong Zhang, Vishwajeet Kumar, Yulong Li, Md Arafat Sultan, Riyaz Bhat, Juergen Bross, Radu Florian, Salim Roukos
| Challenge: | Question Answering (QA) is a major area of research in Natural Language Processing (NLP) |
| Approach: | They propose a one-stop and open-source QA repository for question answering . it supports core QA functionalities like retrieval and reading comprehension . they say it will facilitate easy replication of state-of-the-art (SOTA) QA methods . |
| Outcome: | The proposed framework enables easy replication of state-of-the-art (SOTA) QA methods. |
A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation (2024.acl-long)
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| Challenge: | Existing methods for temporal knowledge graphs de-emphasize temporal correlations between facts sequences and ignore inferring clues from missing facts. |
| Approach: | They propose a Temporal PAth-based reasoning model that is robust to ambiguous temporal data. |
| Outcome: | The proposed model outperforms SOTA methods on the link prediction task. |
An Improved Baseline for Sentence-level Relation Extraction (2022.aacl-short)
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| Challenge: | Sentence-level relation extraction (RE) aims at identifying the relationship between two entities in a sentence. |
| Approach: | They propose to improve sentence-level relation extraction by adding entity representations with typed markers to the model. |
| Outcome: | The proposed model outperforms existing methods on entity representation and noisy labels on TACRED dataset. |
SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction (2025.coling-main)
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| Challenge: | Joint relation extraction models face high computational complexity, complex network architectures, difficult parameter tuning and limited interpretability. |
| Approach: | They develop a candidate label marker mechanism that prioritizes strategic label selection over simple label generation. |
| Outcome: | The proposed candidate label marks improve the SOTA methods by 2.5%, 1.9%, 1.2% . the proposed candidate labels improve the performance of the proposed methods . |
Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction (2023.eacl-main)
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| Challenge: | Existing models for event temporal relation extraction are based on data-driven machine learning . however, TEMPREL extraction is not accurate under distribution shifts. |
| Approach: | They propose to conduct counterfactual analysis to attenuate the effects of two types of training biases: the event trigger bias and the frequent label bias. |
| Outcome: | The proposed model extracts TempRel and timelines more faithfully compared to SOTA methods . it is based on two perspectives: one is to extract genuinely based upon contextual description . the other is to provide proper uncertainty estimation and abstain from extraction when no relation is described in the text . |
A Table-to-Text Framework with Heterogeneous Multidominance Attention and Self-Evaluated Multi-Pass Deliberation (2023.findings-emnlp)
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| Challenge: | Table-to-text works have been widely applied in different domains, such as weather forecast and financial report generation. |
| Approach: | They propose a table-to-text approach on top of Self-evaluated multi-pass Generation and Heterogenous Multidominance Attention to explore the hierarchical structure. |
| Outcome: | The proposed method outperforms several SOTA methods quantitatively and qualitatively on three public datasets. |
Towards LLM-driven Dialogue State Tracking (2023.emnlp-main)
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| Challenge: | emergence of large language models (LLMs) such as GPT3 and ChatGPT has sparked considerable interest in assessing their efficacy across diverse applications. |
| Approach: | They present a framework for a domain-slot instruction tuning method that allows LDST to achieve performance on par with ChatGPT. |
| Outcome: | The proposed framework performs better in zero-shot and few-shot settings than previous SOTA methods. |
FineSurE: Fine-grained Summarization Evaluation using LLMs (2024.acl-long)
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| Challenge: | Existing methods for text summarization evaluation do not correlate well with human judgments . evaluators that use Likert scale scores are limited in their ability to perform deeper analysis. |
| Approach: | They propose a fine-grained evaluator specifically tailored for the summarization task using large language models. |
| Outcome: | The proposed method improves on open-source and proprietary LLMs and shows better completeness and conciseness than existing methods. |
DocInfer: Document-level Natural Language Inference using Optimal Evidence Selection (2022.emnlp-main)
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| Challenge: | Documentlevel NLI is an important problem for many tasks including verification of factual correctness of documents. |
| Approach: | They propose a document-level natural language inference model that builds a hierarchical document graph enriched through inter-sentence relations and performs paragraph pruning using the novel SubGraph Pooling layer. |
| Outcome: | The proposed model performs on a legal judicial reasoning task with a dataset enriched with document graphs and a proposed evidence selection algorithm. |
Joint Constrained Learning for Event-Event Relation Extraction (2020.emnlp-main)
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| Challenge: | Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other. |
| Approach: | They propose a joint constrained learning framework that enforces logical constraints within and across multiple temporal and subevent relations of events by converting constraints into differentiable learning objectives. |
| Outcome: | The proposed framework outperforms SOTA methods on benchmarks for temporal relation extraction and event hierarchy construction. |
RxLens: Multi-Agent LLM-powered Scan and Order for Pharmacy (2025.naacl-industry)
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| Challenge: | paper prescriptions are difficult for customers to interpret and are often unstructured, handwritten, and illegible. |
| Approach: | They propose a multi-step Large Language Model-based solution for automated pharmacy cart construction. |
| Outcome: | The proposed solution can yield up to 19% - 40% and 11% - 26% increase in Recall@3 relative to SOTA methods. |
BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering (2024.acl-long)
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Zheng Chu, Jingchang Chen, Qianglong Chen, Haotian Wang, Kun Zhu, Xiyuan Du, Weijiang Yu, Ming Liu, Bing Qin
| Challenge: | Large language models (LLMs) have demonstrated strong reasoning capabilities, but they still suffer from factual errors when tackling knowledge-intensive tasks. |
| Approach: | They propose a reasoning framework for knowledge-intensive multi-hop QA that prioritizes promising answers at each hop of question. |
| Outcome: | The proposed framework outperforms SOTA methods on four open-domain multi-hop reasoning datasets by 8.5%. |
Flexible Weight Tuning and Weight Fusion Strategies for Continual Named Entity Recognition (2024.findings-acl)
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| Challenge: | Existing methods for Named Entity Recognition (CNER) use knowledge distillation to retain old knowledge, but they are too expensive and fail to integrate with existing state-of-the-art models. |
| Approach: | They propose a weight tuning and weightfusion strategy to learn new entity types while mitigating catastrophic forgetting of old models. |
| Outcome: | The proposed strategies improve the performance of existing models and are model-agnostic. |
E-VarM: Enhanced Variational Word Masks to Improve the Interpretability of Text Classification Models (2022.coling-1)
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Ling Ge, ChunMing Hu, Guanghui Ma, Junshuang Wu, Junfan Chen, JiHong Liu, Hong Zhang, Wenyi Qin, Richong Zhang
| Challenge: | Empirical studies show that our approach outperforms the SOTA methods in improving the interpretability of text classification models. |
| Approach: | They propose an enhanced variational word masks approach that exploits the Variational Information Bottleneck to obtain task-specific words. |
| Outcome: | Empirical results show that the proposed method outperforms the SOTA methods in improving the interpretability of the model. |
Numerical Optimizations for Weighted Low-rank Estimation on Language Models (2022.emnlp-main)
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| Challenge: | Singular value decomposition (SVD) is one of the most popular methods for estimating a target matrix with smaller matrices. |
| Approach: | They propose a method that approximates a target matrix with smaller matrices by two smaller . they also propose metric to predict when the SVD may introduce a significant performance drop. |
| Outcome: | The proposed method can perform better than current SOTA methods in compressing Transformer-based language models. |
SetConv: A New Approach for Learning from Imbalanced Data (2020.emnlp-main)
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| Challenge: | Existing methods for classification are biased towards the majority class when the Imbalance Ratio (IR) is high. |
| Approach: | They propose a set convolution operation and an episodic training strategy to extract a single representative for each class, so that classifiers can later be trained on a balanced class distribution. |
| Outcome: | The proposed algorithm is permutation-invariant despite the order of inputs and shows superiority on multiple large-scale benchmark text datasets. |
Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims (2022.naacl-main)
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| Challenge: | Existing datasets focus on a single medium, information domain or specific application . authors propose novel methods for automated veracity assessment based on Natural Language Inference . |
| Approach: | They propose to build a PANACEA dataset that combines different data sources with different foci to ensure a unique set of claims. |
| Outcome: | The proposed methods are competitive with SOTA methods and provide a detailed discussion. |
MotivGraph-SoIQ: Integrating Motivational Knowledge Graphs and Socratic Dialogue for Enhanced LLM Ideation (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have limitations in grounding ideas and mitigating confirmation bias during refinement. |
| Approach: | They propose a framework that integrates a Motivational Knowledge Graph with a Q-Driven Socratic Ideator to enhance LLM ideation. |
| Outcome: | The proposed framework enhances LLM ideation by integrating a Motivational Knowledge Graph with a Q-Driven Socratic Ideator. |
Learning Hierarchy-Aware Quaternion Knowledge Graph Embeddings with Representing Relations as 3D Rotations (2022.coling-1)
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| Challenge: | Existing knowledge graph embedding models fail to model semantic hierarchies . Existing methods fail to understand the semantic hierarchies of knowledge graphs . |
| Approach: | They propose a model which embeds entities as pure quaternions and constrains the modulus of entities to make them have hierarchical distributions. |
| Outcome: | The proposed model can encode symmetry/antisymmetry, inversion, composition, multiple relation patterns and learn semantic hierarchies simultaneously. |
Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER (2022.coling-1)
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| Challenge: | Existing methods to extract entities from visually-rich documents ignore the inherent multimodality of VRDs and thus the suboptimal results are achieved. |
| Approach: | They propose a multimodal semantic enhancement method that filters redundant information in the current document and a cross-document information awareness technique to enrich the entity-related context. |
| Outcome: | The proposed method outperforms existing methods on two documents understanding benchmarks covering eight languages. |
Self-prompted Chain-of-Thought on Large Language Models for Open-domain Multi-hop Reasoning (2023.findings-emnlp)
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| Challenge: | Existing open-domain question-answering methods lack quality assurance . existing methods lack scalability and poor diversity, hindering LLMs' capabilities . |
| Approach: | They propose an open-domain multi-hop reasoning framework to answer multi-choice questions . they propose an adaptive sampler for in-context selection and self-prompted inference . |
| Outcome: | The proposed framework surpasses the existing SOTA methods on large-scale datasets and doubles the zero-shot performance of small-scale LLMs. |
ZeroAE: Pre-trained Language Model based Autoencoder for Transductive Zero-shot Text Classification (2023.findings-acl)
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| Challenge: | Existing methods for text classification use only encoders or decoders that do not allow for the use of labels in unseen domains. |
| Approach: | They propose an autoencoder that encodes text into two disentangled spaces and decodes it to generate text with labels in the unseen domains. |
| Outcome: | The proposed model outperforms the existing methods in label-partially-unseen and label-fully-un-seeen scenarios and even outperfects the SOTA methods. |
Low-resource Interactive Active Labeling for Fine-tuning Language Models (2022.findings-emnlp)
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| Challenge: | Existing active learning methods for fine-tuning language models are underperforming in low-resource, interactive labeling setting. |
| Approach: | They propose a novel active learning method that employs a hybrid sampling strategy to minimize labeling cost and acquisition latency while providing a framework for adapting to dataset diversity. |
| Outcome: | The proposed method reduces labeling cost and acquisition latency while providing a framework for adapting to dataset diversity via user guidance. |
Automatic rule generation for time expression normalization (2021.findings-emnlp)
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| Challenge: | Existing SOTA methods for normalization rely on expert-designed rules or grammars . current methods are domain sensitive and not sufficient on emerging corpora . |
| Approach: | They propose a method that generates normalization rules from annotated data without expert intervention. |
| Outcome: | The proposed method surpasses existing rule-based methods on the Tweets benchmark and on the TempEval-3 benchmark. |
Actively Supervised Clustering for Open Relation Extraction (2023.acl-long)
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| Challenge: | Existing methods for Open Relation Extraction (OpenRE) use a two-stage pipeline, which learns relation representations and assignments in the first stage, then manually labels relation for each cluster. |
| Approach: | They propose a method that performs relation learning and relation labeling simultaneously without a significant increase in human effort. |
| Outcome: | The proposed method improves existing SOTA methods by 13.8% and 10.6% on two datasets. |
Is Parameter Collision Hindering Continual Learning in LLMs? (2025.coling-main)
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| Challenge: | Existing methods to learn multiple tasks in parallel often lead to catastrophic forgetting, resulting in overwriting knowledge. |
| Approach: | They propose a non-collision low-rank Adaptation approach that leverages low collision rates to enhance continual learning (CL) in large language models. |
| Outcome: | The proposed approach achieves better task orthogonality and higher task orthognality than existing SOTA methods. |
Self-attention-based Graph-of-Thought for Math Problem Solving (2025.findings-acl)
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| Challenge: | Existing methods for generating reasoning paths in a chain structure are inefficient and non-human-like. |
| Approach: | They propose a decoding method for a chain-based LLM that constructs a thought graph simultaneously as an LLM inference and generates reasoning steps with a graph-structured self-attention mechanism. |
| Outcome: | The proposed method improves reasoning accuracy without huge computational over-expensive LLMs and avoids performance degradation issues when the LLM is too small to comprehend complex prompts. |
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System (2022.acl-long)
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| Challenge: | Existing pre-trained language models often form a cascaded generation problem . this can lead to error accumulation across different sub-tasks and greater data annotation overhead. |
| Approach: | They propose a plug-and-play model for task-oriented dialogue that learns primary TOD task completion skills from heterogeneous dialog corpora. |
| Outcome: | The proposed model learns primary TOD task completion skills from heterogeneous dialog corpora. |
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification (2022.naacl-main)
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| Challenge: | Existing methods for data augmentation do not fully exploit the potential of DA in NLP. |
| Approach: | They propose an easy and plug-in framework for data augmentation to support effective text classification. |
| Outcome: | The proposed framework outperforms existing methods in most cases, but not using agent networks or pre-trained generation networks. |
Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples (2022.emnlp-main)
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| Challenge: | State-of-the-art methods based on pre-trained models have achieved remarkable performance on the standard test dataset. |
| Approach: | They propose to incorporate hierarchical structure of logical forms into the model and exploit automatically generated counterfactual data for training. |
| Outcome: | The proposed method is effective to alleviate spurious correlations between the headers of the tables and operators of the logical form. |
LADIS: Language Disentanglement for 3D Shape Editing (2022.findings-emnlp)
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| Challenge: | Existing methods for 3D shape editing produce decoupled, local edits to 3D shapes. |
| Approach: | They propose a tool set that learns latent representations that ground language in 3D geometry. |
| Outcome: | The proposed method outperforms existing SOTA methods by 20% in edit locality and up to 6.6% in language reference resolution accuracy. |
A Theory Guided Scaffolding Instruction Framework for LLM-Enabled Metaphor Reasoning (2024.naacl-long)
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| Challenge: | Existing methods for metaphor detection and reasoning struggle to explain the underlying reasoning process behind the metaphorical/literal judgment. |
| Approach: | They propose a Theory guided Scaffolding Instruction framework that instructs an LLM to infer the underlying reasoning process of metaphor detection guided by metaphor theories for the first time. |
| Outcome: | The proposed method significantly outperforms both the LLM-based reasoning methods and the SOTA methods in metaphor detection. |
MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation (2021.acl-long)
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| Challenge: | Emotion recognition in conversation is a crucial component in affective dialogue systems, which helps the system understand users’ emotions and generate empathetic responses. |
| Approach: | They propose a multimodal fused graph convolutional network model which leverages multimodal dependencies and speaker information to model inter-speaker and intra-speech dependency. |
| Outcome: | The proposed model outperforms other SOTA methods on two public benchmark datasets, IEMOCAP and MELD. |
DrDiff: Dynamic Routing Diffusion with Hierarchical Attention for Breaking the Efficiency-Quality Trade-off (2025.emnlp-main)
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Jusheng Zhang, Yijia Fan, Kaitong Cai, Zimeng Huang, Xiaofei Sun, Jian Wang, Chengpei Tang, Keze Wang
| Challenge: | et al., 2019; Brown e.t al, 2023; Touvron e t al; 2024; OpenAI, 2024) Large Language Models (LLMs) have demonstrated remarkable capabilities in knowledge encoding and contextual understanding during their pretraining phase. |
| Approach: | They propose a dynamic expert scheduling mechanism that allocates computational resources based on text complexity and a hierarchical sparse attention mechanism that adjusts attention patterns according to a variety of input lengths. |
| Outcome: | The proposed framework overpowers existing methods on long-text generation benchmarks. |
One Unified Model for Diverse Tasks: Emotion Cause Analysis via Self-Promote Cognitive Structure Modeling (2025.naacl-long)
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| Challenge: | Existing models for emotion cause analysis overlook common ground rooted in cognitive emotion theories, in particular, the cognitive structure of emotions. |
| Approach: | They propose a unified model capable of tackling diverse emotion cause analysis tasks . they propose 'self-promote mechanism' that constructs the emotion cognitive structure through LLM . |
| Outcome: | The proposed model outperforms existing models and baselines on multiple emotion cause analysis tasks. |
GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization (2022.coling-1)
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| Challenge: | Existing approaches to capture and integrate global semantic information are limited due to their limited ability to capture long-range dependencies. |
| Approach: | They propose a graph contrastive topic enhanced language model that integrates a neural topic model with a pre-trained language model to capture global contextual semantics. |
| Outcome: | The proposed model outperforms existing methods on general domain and biomedical datasets. |
Program Transfer for Answering Complex Questions over Knowledge Bases (2022.acl-long)
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| Challenge: | Program induction for complex questions over knowledge bases relies on a large number of parallel question-program pairs for the given KB, but the gold program annotations are usually lacking, making learning difficult. |
| Approach: | They propose an approach to leverage program annotations on rich KBs as external supervision signals to aid program induction for low-resourced KB. |
| Outcome: | The proposed approach outperforms SOTA methods on ComplexWebQuestions and WebQuestionSP. |
GreenKGC: A Lightweight Knowledge Graph Completion Method (2023.acl-long)
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| Challenge: | Knowledge graph completion (KGC) aims to discover missing relationships in knowledge graphs (KGs). |
| Approach: | They propose a modularized knowledge graph completion solution that learns embeddings for entities and relations through a score function. |
| Outcome: | Experimental results show that GreenKGC outperforms SOTA methods in low dimensions and even better against high-dimensional models with a much smaller model size. |
Dynamic Low-rank Estimation for Transformer-based Language Models (2023.findings-emnlp)
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| Challenge: | RankDyna is a matrix decomposition method that can be used to compress Transformer-based language models. |
| Approach: | They propose a matrix decomposition method that enables dynamic rank resource allocation . they say it can outperform current SOTA methods under various parameter budget levels . |
| Outcome: | The proposed method outperforms current SOTA methods under various budget levels . the proposed method is more efficient with higher compression rates . |
Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models (2026.findings-acl)
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| Challenge: | Recent advances on prompting and post-training have enabled LLMs to perform step-wise reasoning tasks, but they tend to explore unproductive solution paths without effective backtracking or strategy adjustment. |
| Approach: | They propose a framework that empowers LLMs to “think about how to think” and dynamically adapts reasoning strategies in real-time. |
| Outcome: | The proposed framework outperforms previous SOTA methods by 9-12% in accuracy while reducing inference time by 28-35% under the same compute budget. |
Discovering the Gems in Early Layers: Accelerating Long-Context LLMs with 1000x Input Token Reduction (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities in handling long context inputs, but this comes at the cost of increased computational resources and latency. |
| Approach: | They propose an algorithm that uses early LLM layers as filters to select and compress input tokens, reducing the context length for subsequent processing. |
| Outcome: | The proposed method outperforms existing techniques on the Needle in a Haystack task while demonstrating comparable performance on the LongBench challenge. |
MasRouter: Learning to Route LLMs for Multi-Agent Systems (2025.acl-long)
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| Challenge: | Multi-agent systems (MAS) powered by Large Language Models (LLMs) have been demonstrated to push the boundaries of LLM capabilities, yet they often face significant costs and challenges in dynamic LLM selection. |
| Approach: | They propose a multi-agent system routing solution that integrates all components of MAS into a unified routing framework. |
| Outcome: | The proposed solution is high-performing, cost-effective, and efficient . it reduces overhead by up to 52.07 compared to current methods on HumanEval . |
QueryLink: Leveraging Query-Memory Alignment for Long-Term Reasoning in LLM Agents (2026.findings-acl)
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| 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. |
A Relation-Oriented Clustering Method for Open Relation Extraction (2021.emnlp-main)
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| Challenge: | Existing methods for open relation extraction (OpenRE) are designed for predefined relations, which cannot deal with new emerging relations in the real world. |
| Approach: | They propose a relation-oriented clustering model that leverages readily available labeled data to learn a relationship-oriented representation. |
| Outcome: | The proposed model reduces error rate by 29.2% and 15.7% on two datasets compared with current SOTA methods. |
Auto-Evolve: Enhancing Large Language Model’s Performance via Self-Reasoning Framework (2024.findings-emnlp)
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Krishna Aswani, Huilin Lu, Pranav Patankar, Priya Dhalwani, Xue Tan, Jayant Ganeshmohan, Simon Lacasse
| Challenge: | Recent advances in prompt engineering strategies rely on static seed reasoning modules to simulate human approach to problem-solving. |
| Approach: | They propose a framework that enables LLMs to self-create dynamic reasoning modules and downstream action plan. |
| Outcome: | The proposed framework outperforms existing prompting strategies on a BigBench-Hard dataset and improves performance by 2.8% over existing methods. |
Unsupervised Sounding Pixel Learning (2023.emnlp-main)
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| Challenge: | Sounding source localization is a challenging task due to the difficulty of cross-modal alignment. |
| Approach: | They propose an unsupervised method which enables pixel-level sounding source localization in unsupervised paradigm. |
| Outcome: | The proposed method achieves pixel-level sounding source localization without annotations. |
Multilingual Synopses of Movie Narratives: A Dataset for Vision-Language Story Understanding (2024.findings-emnlp)
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| Challenge: | Story video-text alignment is a core task in computational story understanding, but its progress has been held back by the scarcity of manually annotated video- text correspondences and the heavy concentration on English narrations of Hollywood movies. |
| Approach: | They construct a multilingual video story dataset with 13,166 movie summary videos from 7 languages and manual annotations of fine-grained video-text correspondences. |
| Outcome: | The proposed approach outperforms the SOTA methods on clip accuracy and Sentence IoU scores. |
LLM-empowered Dynamic Prompt Routing for Vision-Language Models Tuning under Long-Tailed Distributions (2025.findings-emnlp)
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| Challenge: | Pre-trained vision-language models (VLMs) often suffer from bias in class-imbalanced scenes. |
| Approach: | They propose a multi-dimensional dynamic prompt routing framework that integrates a knowledge base for classes spanning multiple visual-semantic dimensions. |
| Outcome: | The proposed framework achieves comparable results with current SOTA methods on long-tailed benchmarks, including CIFAR-LT, ImageNet-LT and Places-LT. |
Reasoning over Hierarchical Question Decomposition Tree for Explainable Question Answering (2023.acl-long)
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| Challenge: | Existing XQA methods focus on reasoning on a single knowledge source, e.g., structured knowledge bases, unstructured corpora, etc. Existing work in XQA focuses on integrating information from heterogeneous knowledge sources. |
| Approach: | They propose to leverage question decomposing for heterogeneous knowledge integration by breaking down a complex question into simpler ones and selecting the appropriate knowledge source for each sub-question. |
| Outcome: | The proposed framework outperforms SOTA methods on complex QA datasets. |
Leveraging BERT and TFIDF Features for Short Text Clustering via Alignment-Promoting Co-Training (2024.emnlp-main)
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| Challenge: | Existing clustering methods rely on keyword information, but they lack this information. |
| Approach: | They propose a CO**-**T**raining **C**lustering framework to make use of BERT and TFIDF features. |
| Outcome: | The proposed framework outperforms existing SOTA methods on eight datasets. |
Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) are capable of answering knowledge-intensive complex questions with chain-of-thought reasoning. |
| Approach: | They propose a method to solve complex questions with a tree-of-thought approach using parametric knowledge and retrieved external knowledge to augment CoT reasoning. |
| Outcome: | The proposed approach outperforms SOTA methods on three Complex QA datasets under the open-domain setting. |
GeoDRL: A Self-Learning Framework for Geometry Problem Solving using Reinforcement Learning in Deductive Reasoning (2023.findings-acl)
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| Challenge: | Existing methods for automated geometry problem solving lack labeled data. |
| Approach: | They propose a framework that integrates logic graph deduction and deep reinforcement learning to optimize geometry reasoning as a Markov Decision Process. |
| Outcome: | The proposed framework improves accuracy and interpretability in the Geometry3K dataset while maintaining correctness. |
Efficient Data Learning for Open Information Extraction with Pre-trained Language Models (2023.findings-emnlp)
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| Challenge: | Experimental results indicate that, compared to previous SOTA methods, OK-IE requires only 1/100 of the training data (900 instances) and 1/120 of the time (3 minutes) to achieve comparable results. |
| Approach: | They propose a framework that transforms OpenIE into the pre-training task form of the T5 model, thereby reducing the need for extensive training data. |
| Outcome: | The proposed framework transforms OpenIE into the pre-training task form of the T5 model, reducing the need for extensive training data and significantly reducing training time. |
HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis (2023.findings-emnlp)
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| Challenge: | Authorship Analysis is an essential aspect of Natural Language Processing (NLP) for a long time. |
| Approach: | They propose to use 17 human speech datasets and 3 LLMs to create a benchmark for spoken texts. |
| Outcome: | The proposed benchmark encompasses 17 human datasets and AI-generated spoken texts created using 3 prominent LLMs: ChatGPT, PaLM2, and Vicuna13B. |
Template-assisted Contrastive Learning of Task-oriented Dialogue Sentence Embeddings (2026.acl-long)
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| Challenge: | Annotating and gathering utterance relationships in dialogues is difficult, while token-level annotations, entities, slots and templates, are much easier to obtain. |
| Approach: | They propose a template-aware augmentation method that utilizes template information to learn utterance embeddings via self-supervised contrastive learning framework. |
| Outcome: | The proposed method improves on five benchmark dialogue datasets and shows that it is more efficient than previous SOTA methods. |
UniGeM: Unifying Data Selection and Mixing via Geometric Exploration and Mining (2026.findings-acl)
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Changhao Wang, null Yunfeiyu, Xinhao Yao, Jiaolong Yang, Lu Yu, Junpeng Fang, Chaobo Li, Riccardo Cantoro, Qing Cui, Jun Zhou
| Challenge: | Large Language Models (LLMs) scaling is limited by data quality and domain mixing and instance selection are two separate problems. |
| Approach: | They propose a framework that unifies mixing and selection without training proxy models or relying on external reference datasets. |
| Outcome: | The proposed framework achieves 2.0 data efficiency over a random baseline and further improves overall performance compared to SOTA methods in reasoning-heavy evaluations and multilingual generalization. |
Beyond Query Bias: Candidate-Aware Iterative Refinement for Zero-Shot Composed Image Retrieval (2026.findings-acl)
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| Challenge: | Existing methods to retrieve target images suffer from inherent cognitive bias due to unknown candidate distribution. |
| Approach: | They propose a training-free framework that reframes ZS-CIR as a self-correcting process . they propose to use retrieved results as feedback to perceive the candidate distribution . |
| Outcome: | Experiments on public benchmarks show that CoRR outperforms other SOTA methods. |
CoverICL: Selective Annotation for In-Context Learning via Active Graph Coverage (2024.emnlp-main)
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Costas Mavromatis, Balasubramaniam Srinivasan, Zhengyuan Shen, Jiani Zhang, Huzefa Rangwala, Christos Faloutsos, George Karypis
| Challenge: | In-context learning (ICL) uses few-shot labeled examples to perform selective annotation. |
| Approach: | They propose an algorithm that incorporates uncertainty sampling into selective annotation for ICL . CoverICL builds a nearest-neighbor graph based on the semantic similarity between candidate ICL examples . |
| Outcome: | The proposed algorithm outperforms existing methods for low-budget active learning (AL) it is up to 2x more budget-efficient than SOTA methods for high-budge AL. |
Multi-matrix Factorization Attention (2025.findings-acl)
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| Challenge: | Existing variants for Multi-Head Attention (MHA) fail to maintain strong performance under stringent Key-Value cache (KV cache) constraints. |
| Approach: | They propose to use multi-matrix factorization attention and MFA-Key-reuse attention architectures to increase model capacity under tight KV cache constraints. |
| Outcome: | The proposed architecture outperforms existing methods while reducing KV cache usage by 56% and 93.7% in large-scale experiments. |
Controllable and Reliable Knowledge-Intensive Task-Oriented Conversational Agents with Declarative Genie Worksheets (2025.acl-long)
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| Challenge: | Existing LLMs suffer from hallucination, following instructions with conditional logic, and integrating knowledge from different sources. |
| Approach: | They propose a programmable framework for creating knowledge-intensive task-oriented conversational agents that handle involved interactions and answer complex queries. |
| Outcome: | The proposed framework outperforms SOTA methods on complex logic dialogue datasets by up to 20.5%. |
AHA: Aligning Large Audio-Language Models for Reasoning Hallucinations via Counterfactual Hard Negatives (2026.findings-acl)
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Yanxi Chen, Wenhui Zhu, Xiwen Chen, Zhipeng Wang, Xin Li, Peijie Qiu, Hao Wang, Xuanzhao Dong, Yujian Xiong, Anderson Schneider, Yuriy Nevmyvaka, Yalin Wang
| Challenge: | Large Audio-Language Models suffer from hallucinations, e.g., generating text not grounded in the audio input. |
| Approach: | They propose a framework to address hallucination problems in large audio-language models . they use a preference dataset to test the model's accuracy . |
| Outcome: | The proposed model outperforms the latest SOTA methods in terms of performance and generalization. |
AutoCT: Automating Interpretable Clinical Trial Prediction with LLM Agents (2025.emnlp-main)
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| Challenge: | Clinical trials are expensive and time-consuming, and accurate trial prediction is key to advancing medical treatments. |
| Approach: | They propose a framework that combines reasoning capabilities of large language models with the explainability of classical machine learning to generate, evaluate, and refine tabular features without human input. |
| Outcome: | The proposed framework performs better than SOTA methods on clinical trial prediction tasks within a limited number of iterations. |
Auto-Stega: An Agent-Driven System for Lifelong Strategy Evolution in LLM-Based Text Steganography (2026.findings-acl)
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| Challenge: | prevailing methods rely on hand-crafted or pre-specified strategies and struggle to balance efficiency, imperceptibility, and security, particularly at high embedding rates. |
| Approach: | They propose an agent-driven self-evolving framework that is the first to realize self-changing steganographic strategies by automatically discovering, composing, and adapting strategies at inference time. |
| Outcome: | The proposed framework achieves 42.2% perplexity and 1.6% anti-steganalysis performance over SOTA methods at high embedding rates. |
MTA:A Merge-then-Adapt Framework for Personalized Large Language Models (2026.acl-long)
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Xiaopeng Li, Yuanjin Zheng, Wanyu Wang, Wenlin Zhang, Pengyue Jia, Yingyi Zhang, Haiying He, Mengyang Ma, Yiqi Wang, Maolin Wang, Xuetao Wei, Xiangyu Zhao
| Challenge: | Personalized Large Language Models (PLLMs) aim to align outputs with individual user preferences . current methods of fine-tuning a separate module for each user are unscalable . |
| Approach: | They propose a Merge-then-Adapt framework for Personalized Large Language Models . they construct a shared Meta-LoRA bank and propose an Adaptive LoRA Fusion stage . |
| Outcome: | The proposed framework outperforms existing SOTA methods on the LaMP benchmark. |