Papers with SOTA baselines
MATSA: Multi-Agent Table Structure Attribution (2024.emnlp-demo)
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| Challenge: | Tabular data present unique challenges for attribution due to ambiguities, complex header hierarchies, and the difficulty in interpreting individual table cells without row and column context. |
| Approach: | They propose a task to generate row and column-level attributions supporting LLM-generated answers. |
| Outcome: | The proposed task outperforms baselines on tabCite and improves F1 score. |
ADEPT-SQL: A High-performance Text-to-SQL Application for Real-World Enterprise-Level Databases (2025.acl-demo)
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Yongnan Chen, Zhuo Chang, Shijia Gu, Yuanhang Zong, Zhang Mei, Shiyu Wang, Hezixiang Hezixiang, Hongzhi Chen, Jin Wei, Bin Cui
| Challenge: | et al., 2017) address domain-specific knowledge barriers, schemas complexity, and computational costs of large LLMs. |
| Approach: | They propose a domain-adapted Text2SQL system that addresses critical deployment challenges in professional fields. |
| Outcome: | The proposed system achieves 97% execution accuracy on real-world databases . it is faster than existing systems and has a higher performance than existing ones. |
RAVEN: Query-Guided Representation Alignment for Question Answering over Audio, Video, Embedded Sensors, and Natural Language (2025.emnlp-main)
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| Challenge: | Multimodal question answering often requires identifying which video, audio, or sensor tokens are relevant to the question. off-camera speech, background noise, or motion outside the field of view often mislead fusion models that weight all streams equally. |
| Approach: | They propose a unified architecture for multimodal question answering that assigns scalar relevance scores to each token across modalities. |
| Outcome: | The proposed model outperforms state-of-the-art multimodal large language models on seven multi-modal QA benchmarks and egocentric and exocentric tasks. |
A Hybrid Model of Classification and Generation for Spatial Relation Extraction (2022.coling-1)
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| Challenge: | Existing studies only focus on spatial relations extraction as a classification task . spatial information is one kind of critical information for natural language understanding . |
| Approach: | They propose a hybrid model that generates null-role relations and extracts non-null-rol . they propose varying kinds of schemes to represent spatial relation . |
| Outcome: | The proposed model outperforms the baselines on the spatial relation extraction task on SpaceEval. |
Image Manipulation via Multi-Hop Instructions - A New Dataset and Weakly-Supervised Neuro-Symbolic Approach (2023.emnlp-main)
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Harman Singh, Poorva Garg, Mohit Gupta, Kevin Shah, Ashish Goswami, Satyam Modi, Arnab Mondal, Dinesh Khandelwal, Dinesh Garg, Parag Singla
| Challenge: | Recent studies have shown that neuro-symbolic models lack interpretability and are not robust to noise. |
| Approach: | They propose to extend Neuro Symbolic Concept Learning (NSCL) which has been quite effective for the task of Visual Question Answering (VQA) they create a new dataset for the image manipulation task and demonstrate that NeuroSIM is highly competitive with or beats SOTA baselines that make use of supervised data for manipulation. |
| Outcome: | The proposed system performs complex multi-hop reasoning over multi-object scenes and only requires weak supervision in the form of annotated data for VQA. |
DCT-Centered Temporal Relation Extraction (2022.coling-1)
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| Challenge: | Existing work on temporal relation extraction focuses on extracting temporal relations between events . previous work on relation extraction focused on focusing on event-centered tasks . |
| Approach: | They propose a temporal relation extraction model that unifies events, timexes and DCT . they propose combining event mentions, time expressions and document creation time into a sentence-style model . |
| Outcome: | The proposed model outperforms baselines on E-E, E-T and E-D significantly. |
Incorporating Centering Theory into Neural Coreference Resolution (2022.naacl-main)
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| Challenge: | Recent years, transformer-based coreference resolution systems have achieved remarkable improvements on the CoNLL dataset. |
| Approach: | They propose to incorporate centering transitions derived from centering theory into a neural coreference model by using a graph. |
| Outcome: | The proposed model improves on pronoun resolution in long documents, formal well-structured text, and clusters with scattered mentions. |
MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning (2022.findings-acl)
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| Challenge: | Existing methods to infer logical relations with annotated training data suffer from over-fitting and poor generalization problems due to the dataset sparsity. |
| Approach: | They propose a MEta-path guided contrastive learning method for logical ReasonIng of text that performs self-supervised pre-training on abundant unlabeled text data. |
| Outcome: | The proposed method outperforms the baselines on two logical reasoning benchmarks with significant improvements. |
MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasets (2024.naacl-long)
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Hossein Aboutalebi, Hwanjun Song, Yusheng Xie, Arshit Gupta, Lijia Sun, Hang Su, Igor Shalyminov, Nikolaos Pappas, Siffi Singh, Saab Mansour
| Challenge: | Existing approaches to augment textual dialogues with retrieved images pose privacy, diversity, and quality constraints. |
| Approach: | They propose a framework to augment text-only dialogues with diverse and high-quality images by using a diffusion model and a feedback loop. |
| Outcome: | The proposed framework is comparable to or better than baselines, with significant improvements in human evaluation, especially against retrieval baselines where the image database is small. |
LyapLock: Bounded Knowledge Preservation in Sequential Large Language Model Editing (2025.emnlp-main)
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| Challenge: | Existing models for enhancing knowledge updating are prone to performance degradation due to incomplete knowledge preservation mechanisms. |
| Approach: | They propose a model for locate-then-edit that decomposes long-term constrained programming into tractable stepwise subproblems for efficient solving. |
| Outcome: | The proposed framework achieves asymptotic optimal editing performance while meeting the constraints of long-term knowledge preservation. |
Alleviating Distribution Shift in Synthetic Data for Machine Translation Quality Estimation (2025.acl-long)
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| Challenge: | Quality Estimation (QE) models evaluate the quality of machine translations without reference translations, serving as the reward models for the translation task. |
| Approach: | They propose a framework for alleviating distribution shift in synthetic QE data . they employ a constrained beam search algorithm and distinct generation models to enhance translation diversity. |
| Outcome: | The proposed framework outperforms SOTA baselines like CometKiwi in supervised and unsupervised settings. |
COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion (2021.acl-long)
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| Challenge: | Existing inference models are opaque, but they can be made more interpretable by explicitly generating interim inference rules and using them to guide the generation of task-specific textual outputs. |
| Approach: | They propose a recursive inference framework that iteratively reads context sentences and dynamically generates contextualized inference rules, encodes them, and uses them to guide output generation. |
| Outcome: | The proposed framework generates better story sentences than baseline models, and is more interpretable than existing models. |
DUQGen: Effective Unsupervised Domain Adaptation of Neural Rankers by Diversifying Synthetic Query Generation (2024.naacl-long)
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| Challenge: | State-of-the-art rankers pre-trained on large task-specific training data such as MS-MARCO exhibit strong performance on various ranking tasks without domain adaptation, also called zero-shot. |
| Approach: | They propose a method to generate unsupervised domain adaptation for ranking using large-scale task-specific training data such as MS-MARCO and Wikipedia retrieval. |
| Outcome: | The proposed method outperforms all zero-shot baselines and significantly outperfies the SOTA baselines on 16 out of 18 datasets, for an average of 4% relative improvement across all datasets. |
Improving Event Coreference Resolution Using Document-level and Topic-level Information (2022.emnlp-main)
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| Challenge: | Experimental results show that our model outperforms the SOTA baselines due to the encoding length limitation. |
| Approach: | They propose a longformer-based encoder and an encoder with a trigger-mask mechanism to learn sentence-level embeddings based on local context. |
| Outcome: | The proposed model outperforms the baselines on the KBP 2017 dataset. |
ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis (2024.acl-long)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable in-context learning capabilities in various natural language processing tasks. |
| Approach: | They propose a novel approach ERA-CoT which aids LLMs in understanding context by capturing relationships between entities and supports the reasoning of diverse tasks through Chain-of-Thoughts (CoT). |
| Outcome: | The proposed method improves on GPT3.5 and previous SOTA prompting methods by an average of 5.1% compared to previous prompting approaches. |
Sherry: Hardware-Efficient 1.25-Bit Ternary Quantization via Fine-grained Sparsification (2026.acl-long)
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| Challenge: | ternary quantization is a powerful solution for resource-constrained edge devices . current implementations suffer from a fundamental misalignment with commodity hardware . |
| Approach: | They propose a hardware-efficient ternary quantization framework that packs weights into five bits to restore power-of-two alignment. |
| Outcome: | The proposed framework reduces weights to -1, 0, +1 while preserving power-of-two alignment. |
LINKED: Eliciting, Filtering and Integrating Knowledge in Large Language Model for Commonsense Reasoning (2024.findings-emnlp)
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Jiachun Li, Pengfei Cao, Chenhao Wang, Zhuoran Jin, Yubo Chen, Kang Liu, Xiaojian Jiang, Jiexin Xu, Jun Zhao
| Challenge: | Large language models (LLMs) often exhibit poor performance on knowledge-intensive tasks, such as commonsense reasoning. |
| Approach: | They propose a method to elicit, filter and integrate knowledge in large language models (LINKED) they propose 'reward model' to filter out noisy knowledge and 'take marginal consistent reasoning module' |
| Outcome: | The proposed method outperforms SOTA baselines on two commonsense reasoning tasks. |
Improving Dialogue Discourse Parsing via Reply-to Structures of Addressee Recognition (2023.emnlp-main)
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| Challenge: | Existing approaches to learn dialogue discourse parsing with related tasks require additional annotation, thus limiting their generality. |
| Approach: | They propose a multitasking framework that integrates dialogue discourse parsing with addressee recognition to reflect relation-based structure of dialogue. |
| Outcome: | The proposed framework outperforms baselines on the Molweni and STAC datasets. |
Pruning Foundation Models for High Accuracy without Retraining (2024.findings-emnlp)
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| Challenge: | Despite the superior performance of foundation models, it is challenging to deploy large language models in practical applications due to their massive parameters and computations. |
| Approach: | They propose a pruning algorithm to prune LLMs in one-shot without retraining . they propose retrainable pruning algorithms to prune multiple weights in LLM . |
| Outcome: | The proposed pruning methods perform better than baseline pruning methods on sparse and unstructured sparsity models. |
GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion (2025.findings-acl)
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Kangyang Luo, Yuzhuo Bai, Cheng Gao, Shuzheng Si, Zhu Liu, Yingli Shen, Zhitong Wang, Cunliang Kong, Wenhao Li, Yufei Huang, Ye Tian, Xuantang Xiong, Lei Han, Maosong Sun
| Challenge: | Existing knowledge graphs lack the ability to integrate structural information into LLMs and output predictions deterministically. |
| Approach: | They propose a method which encodes structural information of KGs and merges it with LLMs to enhance KGC performance. |
| Outcome: | The proposed method improves the performance of KG Completion datasets on KGs by integrating structural information with LLMs. |
MedFilter: Improving Extraction of Task-relevant Utterances through Integration of Discourse Structure and Ontological Knowledge (2020.emnlp-main)
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| Challenge: | Identifying task-relevant utterances improves performance at downstream medical processing. |
| Approach: | They propose a novel approach that uses task-oriented conversations to improve utterance classification over SOTA models. |
| Outcome: | The proposed model improves on a corpus of 7,000 doctor-patient conversations on 7,000 patient conversations. |
Let’s Be Self-generated via Step by Step: A Curriculum Learning Approach to Automated Reasoning with Large Language Models (2025.findings-acl)
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| Challenge: | Existing efforts to improve CoT prompting have limitations that require extensive human effort or performance needs to be improved. |
| Approach: | They propose a prompt approach for automatic reasoning called LBS3 inspired by curriculum learning which better reflects human learning habits. |
| Outcome: | The proposed approach achieves strongly competitive performance compared to baselines in reasoning-intensive tasks with varying open- and closed-source LLMs. |
EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification (2023.emnlp-main)
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| Challenge: | Existing methods to augment training data with counterfactuals fail to handle multi-hop fact verification due to their incapability to preserve complex logical relationships. |
| Approach: | They propose to augment training data with counterfactuals that alter causal features of the original data by preserving logical relationships. |
| Outcome: | The proposed method outperforms the baselines and can generate linguistically diverse counterfactuals without disrupting their logical relationships. |
Incorporating Factuality Inference to Identify Document-level Event Factuality (2023.findings-acl)
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| Challenge: | Document-level Event Factuality Identification (DEFI) is a task for identifying the degree of certainty that a specific event occurs in a document. |
| Approach: | They propose a document-level event factuality inference task to bridge the sentence-level and document- level event factualality semantically. |
| Outcome: | The proposed model outperforms the baselines on the public English and Chinese DLEF datasets. |
iAgent: LLM Agent as a Shield between User and Recommender Systems (2025.findings-acl)
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Wujiang Xu, Yunxiao Shi, Zujie Liang, Xuying Ning, Kai Mei, Kun Wang, Xi Zhu, Min Xu, Yongfeng Zhang
| Challenge: | Traditional recommender systems focus on the user-platform paradigm, where users are directly exposed under the control of the platform's recommendation algorithms. |
| Approach: | They propose a user-agent-platform paradigm where agent serves as the protective shield between user and recommender system that enables indirect exposure. |
| Outcome: | The proposed model improves 16.6% over baselines on four datasets and mitigates echo chamber effects and reduces model bias in disadvantaged users. |
GCML: Gradient Coherence Guided Meta-Learning for Cross-Domain Emerging Topic Rumor Detection (2025.emnlp-main)
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| Challenge: | Existing domain adaptation rumor detection methods ignore the data generalization differences and rely on a large amount of unlabeled target domain samples to achieve domain adaptation. |
| Approach: | They propose a Gradient Coherence guided Meta-Learning approach for emerging topics rumor detection that selectively learns more "generalizable" tasks that are more beneficial in adapting to the target domain. |
| Outcome: | The proposed method outperforms baselines on real-world datasets and significantly outperformed traditional methods on the in-domain condition. |
Evidence-Augmented Policy Optimization with Reward Co-Evolution for Long-Context Reasoning (2026.acl-long)
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| Challenge: | Evidence-Augmented Policy Optimization (EAPO) improves long-context reasoning performance . Xu et al., 2025): large language models are a critical part of NLP . |
| Approach: | They propose an Evidence-Augmented Reasoning paradigm that uses a group-relative reward to improve evidence quality. |
| Outcome: | EAPO significantly improves long-context reasoning performance compared to baselines. |
SCOPE: Preserving Modality-Specific Cues to Mitigate Modality Laziness in Multimodal Learning (2026.findings-acl)
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| Challenge: | Existing approaches to learning multimodal representations emphasize shared semantics and overlook modality-specific cues. |
| Approach: | They propose a framework for learning complete multimodal representations using shared and practical cues. |
| Outcome: | SCOPE outperforms SOTA benchmarks on four datasets and achieves 27.10% accuracy improvement. |
CLMTracing: Black-box User-level Watermarking for Code Language Model Tracing (2025.emnlp-main)
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| Challenge: | Open-source code language models (code LMs) are a growing threat for intellectual property protection. |
| Approach: | They propose a black-box code LM watermarking framework that uses rule-based watermarks and utility-preserving injection method for user-level model tracing. |
| Outcome: | The proposed framework shows that it performs well across multiple state-of-the-art code LMs and is harmless compared to existing baselines. |