Papers with content
Detecting context abusiveness using hierarchical deep learning (D19-50)
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| Challenge: | Abusive text is a serious problem in social media and causes many issues among users . a model that detects text abusiveness in context without explicit abusive words is challenging . |
| Approach: | They propose to use an abusive lexicon to determine the existence of an abusive word in text . they combine local and global features to evaluate the model using benchmark data . |
| Outcome: | The proposed model outperforms all previous models for detecting abusiveness in text without abusive words. |
Zero Pronoun Resolution with Attention-based Neural Network (C18-1)
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| Challenge: | Recent neural network methods for zero pronoun resolution use contextual information to encode the zero pronomins since they contain no actual content. |
| Approach: | They propose a self-attention mechanism for encoding zero pronouns that focus on some informative parts of the associated texts and produce an efficient way of encode them. |
| Outcome: | The proposed model significantly surpasses existing Chinese zero pronoun resolution baseline systems. |
The Battlefront of Combating Misinformation and Coping with Media Bias (2022.aacl-tutorials)
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| Challenge: | a growing number of misinformation and misinformation is affecting our daily lives . a tutorial aims to address the challenges of detecting fake news and media bias . |
| Approach: | They provide an overview of the frontier in fighting misinformation . they propose to develop a robust fake news detection system to combat misinformation. |
| Outcome: | This tutorial examines the frontiers of fake news detection and media bias detection . it focuses on how to fact-check information pieces and uncover bias and agenda of news sources . |
Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer (2021.acl-short)
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| Challenge: | Existing methods for unsupervised text style transfer focus on transferring a specific attribute, but this technique has never been explored in natural language generation tasks. |
| Approach: | They propose a counterfactual-based method to modify latent representations by posing a ‘what-if’ scenario. |
| Outcome: | The proposed method is tested on multiple attribute transfer tasks like Sentiment, Formality and Excitement to support the hypothesis. |
Structural Patent Classification Using Label Hierarchy Optimization (2025.findings-emnlp)
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| Challenge: | Existing methods for patent classification ignore key technical content claims and citation relationships . existing methods treat labels as independent targets, failing to exploit semantic and structural information within the label taxonomy. |
| Approach: | They propose a Claim Structure based Patent Classification model with Label Awareness . structural graph learning is used to mine the internal logic of patent claims . |
| Outcome: | The proposed method is more effective than state-of-the-art classification models. |
A Web Scale Entity Extraction System (2021.findings-emnlp)
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| Challenge: | Existing systems for large-scale entity extraction are limited by the scale and variety of data available on internet platforms. |
| Approach: | They propose to build an entity extraction system for multiple document types at large scale using multi-modal Transformers. |
| Outcome: | The proposed system extracts multiple types of entities from multiple document types at large scale using multi-modal Transformers. |
An Item Response Theory Framework for Persuasion (2022.findings-naacl)
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| Challenge: | Several studies have considered the audience as a whole or by building separate models for different types of audiences. |
| Approach: | They propose a framework that can represent individual audience members in one model across a diverse set of persuasion tasks. |
| Outcome: | The proposed model performs well on three datasets including a novel dataset in the area of political advocacy. |
Exploring Hyperbolic Hierarchical Structure for Multimodal Rumor Detection (2025.findings-emnlp)
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| Challenge: | rumor detection models often assume a simplistic one-to-one alignment between modalities . authors present a method that preserves hierarchical, non-linear relationships . |
| Approach: | They propose a method that uses hyperbolic geometry to preserve hierarchical relationships . it decomposes image and text content into three levels and embeds them in hyperbolical space . |
| Outcome: | The proposed method preserves hierarchical relationships rather than representing them at a flat semantic level. |
T2-RAGBench: Text-and-Table Benchmark for Evaluating Retrieval-Augmented Generation (2026.eacl-long)
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| Challenge: | Existing QA datasets containing text-and-table data typically contain context-dependent questions, which may yield multiple correct answers depending on the provided context. |
| Approach: | They propose a benchmark to evaluate RAG methods on text-and-table data. |
| Outcome: | The proposed method evaluates RAG methods on real-world text-and-table data. |
Benchmarking LLM’s Capability in Reasoning over Conflicting Web References (2026.acl-long)
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| Challenge: | Large language models (LLMs) integrated with retrieval-augmented generation (RAG) are a dominant framework for building intelligent assistants. |
| Approach: | They propose a benchmark to evaluate LLMs' reasoning capability over real-world conflicting documents retrieved from the web. |
| Outcome: | The proposed benchmark evaluates LLMs' reasoning capability over real-world conflicting documents retrieved from the web. |
RU-ADEPT: Russian Anonymized Dataset with Eight Personality Traits (2022.lrec-1)
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C. Anton Rytting, Valerie Novak, James R. Hull, Victor M. Frank, Paul Rodrigues, Jarrett G. W. Lee, Laurel Miller-Sims
| Challenge: | Social media has provided a platform for many individuals to express themselves naturally and publicly, but most of the work in this area has focused on English and other Western European languages. |
| Approach: | They propose to use a Russian dataset to combine author trait data with social media content to find out how personality traits are manifested. |
| Outcome: | The proposed dataset is the first to associate demographic and personality trait data with Russian-language social media content and to a limited extent, the first publicly-available dataset of personality traits to author content across multiple social media platforms. |
Youling: an AI-assisted Lyrics Creation System (2020.emnlp-demos)
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Rongsheng Zhang, Xiaoxi Mao, Le Li, Lin Jiang, Lin Chen, Zhiwei Hu, Yadong Xi, Changjie Fan, Minlie Huang
| Challenge: | Recent studies have focused on a single pass of lyrics generation with little human intervention. |
| Approach: | They propose an AI-assisted lyrics creation system that supports one pass full-text generation and interactive generation modes. |
| Outcome: | The proposed system supports full-text generation and interactive generation modes . it also provides a revision module which enables users to revise undesired lyrics repeatedly. |
SocialForge: simulating the social internet to provide realistic training against influence operations (2025.acl-industry)
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Ulysse Oliveri, Guillaume Gadek, Alexandre Dey, Benjamin Costé, Damien Lolive, Arnaud Delhay, Bruno Grilheres
| Challenge: | Social media platforms have enabled large-scale influence campaigns, impacting democratic processes. |
| Approach: | They propose a system to enhance diversity and realism of the generated content while ensuring its adherence to the original scenario. |
| Outcome: | The proposed system improves diversity and realism while ensuring its adherence to the original scenario. |
Plan-then-Seam: Towards Efficient Table-to-Text Generation (2023.findings-eacl)
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| Challenge: | Recent work explicitly decomposes the generation process into content planning and surface generation stages, employing two autoregressive networks for them respectively. |
| Approach: | They propose a non-parallelelizable table-to-text model that produces outputs in parallel with one network. |
| Outcome: | The proposed model achieves 3.0 5.6 times speedup for inference time, reducing 50% parameters, while maintaining as least comparable performance against strong two-stage table-to-text competitors. |
Constructing a Dataset for Hallucination Detection in Japanese Summarization with Fine-grained Faithfulness Labels (2026.eacl-srw)
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| Challenge: | Large language models (LLMs) can generate fluent text, but the quality of generated content depends on its consistency with the given input. |
| Approach: | They constructed a Japanese evaluation dataset for hallucination detection in summarization by manually annotating sentence-level faithfulness labels in LLM-generated summaries of Japanese documents. |
| Outcome: | The proposed model can detect hallucinations in Japanese documents by annotating faithfulness labels in Japanese summaries. |
The Mediomatix Corpus: Parallel Data for Romansh Language Varieties via Comparable Schoolbooks (2026.findings-eacl)
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| Challenge: | a small-scale human evaluation confirms that the segments are highly parallel, making the dataset suitable for NLP applications. |
| Approach: | They present a first parallel corpus of Romansh idioms from 291 schoolbooks . they use automatic alignment methods to extract 207k multi-parallel segments from the books . |
| Outcome: | The proposed corpus is based on 291 schoolbook volumes, which are comparable in content for the five idioms. |
Like a Good Nearest Neighbor: Practical Content Moderation and Text Classification (2024.eacl-long)
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| Challenge: | Few-shot text classification systems are infeasible to deploy and use reliably due to their dependence on prompting and billion-parameter language models. |
| Approach: | They propose a modification to SetFit that fine-tunes a Sentence Transformer under a contrastive learning paradigm and achieves similar results to more unwieldy systems. |
| Outcome: | The proposed model fine-tunes a Sentence Transformer under a contrastive learning paradigm and achieves similar results to more unwieldy systems. |
“Hold on honey, men at work”: A semi-supervised approach to detecting sexism in sitcoms (2021.acl-srw)
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| Challenge: | sexist dialogue in sitcoms is an important part of society's development, according to Sink and Mastro (2017). |
| Approach: | They propose a semi-supervised text classification model that automatically detects instances of sexism in popular sitcom dialogues. |
| Outcome: | The proposed model outperforms deep learning-based systems in detecting sexist dialogues over time and shows that sexism decreases over the years. |
Towards Incremental Learning of Word Embeddings Using Context Informativeness (P19-2)
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| Challenge: | In this paper, we investigate the task of learning word embeddings from very sparse data in an incremental, cognitively-plausible way. |
| Approach: | They propose a model that incorporates informativeness into a proposed model of nonce learning, using it for context selection and learning rate modulation. |
| Outcome: | The proposed model is based on a proposed model of nonce learning, and it performs well on the task of learning new words from definitions and potentially uninformative contexts. |
JiraiBench: A Bilingual Benchmark for Evaluating Large Language Models’ Detection of Human risky health behavior Content in Jirai Community (2026.eacl-long)
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Yunze Xiao, Tingyu He, Lionel Z. Wang, Yiming Ma, Xingyu Song, Xiaohang Xu, Mona T. Diab, Irene Li, Ka Chung Ng
| Challenge: | a cross-lingual dataset captures a transnational cultural phenomenon . risky health behaviors (RHB) are often linked to complex mental health conditions . |
| Approach: | They present the first cross-lingual dataset that captures a transnational cultural phenomenon . their dataset of more than 15,000 annotated social media posts forms the core of JiraiBench . |
| Outcome: | The study shows that cultural context can be more influential than linguistic similarity . the study also shows that the Japanese prompts better handle Chinese content . |
What’s in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus (2021.acl-short)
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| Challenge: | Recent advances in NLP have been driven by Transformer-based language models. |
| Approach: | They analyze the Common Crawl, a web corpus extensively used for training language models. |
| Outcome: | The Common Crawl contains hate speech and sexually explicit content even after filtering procedures. |
Understanding tables with intermediate pre-training (2020.findings-emnlp)
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| Challenge: | Textual entailment is well studied, but is less well studied for table enlargement . a new dataset of millions of examples is used to train the model . |
| Approach: | They adapt a table-based BERT model to recognize entailment from a dataset . they evaluate table pruning techniques as a pre-processing step to improve model efficiency . |
| Outcome: | The proposed model improves training and prediction efficiency at a moderate drop in accuracy. |
Fantastic Expressions and Where to Find Them: Chinese Simile Generation with Multiple Constraints (2023.acl-long)
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| Challenge: | Existing attempts to generate similes as context-free tasks are not suitable for simile generation . however, simile generated under such settings might be undesirable, we argue . |
| Approach: | They propose a model to generate a simile with multiple simile elements . they propose to use a vehicle retrieval module to obtain the explicable comparison . |
| Outcome: | The proposed model can generate a simile with multiple simile elements, e.g., context and vehicle. |
Fundus: A Simple-to-Use News Scraper Optimized for High Quality Extractions (2024.acl-demos)
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| Challenge: | Fundus is a news scraper that extracts news articles from the web with just a few lines of code. |
| Approach: | They introduce Fundus, a news scraper that enables users to obtain news articles with just a few lines of code. |
| Outcome: | The proposed news scraper optimizes for quality and provides a unified interface for newspapers. |
MDC3: A Novel Multimodal Dataset for Commercial Content Classification in Bengali (2025.naacl-srw)
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| Challenge: | Identifying commercial posts in resource-constrained languages remains a challenge for automatic text classification tasks. |
| Approach: | They propose a dataset for Bengali social media posts classified as commercial and noncommercial . they include an annotation guideline to aid future dataset creation in resource-constrained languages . |
| Outcome: | The proposed dataset is based on an annotation guideline for future dataset creation in resource-constrained languages. |
ATLAS: Improving Lay Summarisation with Attribute-based Control (2024.acl-short)
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| Challenge: | Lay summarisation aims to produce scientific summaries that are comprehensible to non-experts. |
| Approach: | They propose an abstractive summarisation approach that can control properties contributing to overall "layness" they evaluate ATLAS on a combination of biomedical lay summarization datasets. |
| Outcome: | The proposed approach outperforms state-of-the-art summarisation metrics on biomedical datasets and shows that it can be discriminatory and emergently influenced. |
FlashBack: Efficient Retrieval-Augmented Language Modeling for Fast Inference (2025.findings-acl)
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| Challenge: | Retrieval-Augmented Language Modeling (RALM) is a popular approach for large language models. |
| Approach: | They propose a modular RALM that integrates large language models with documents from an external corpus to improve inference efficiency. |
| Outcome: | The proposed method improves inference efficiency with appending context pattern while maintaining decent performance after fine-tuning by Low-Rank Adaption. |
Selective Span-Level Unlearning for Large Language Models (2026.acl-short)
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| Challenge: | Existing selective methods that focus on identifying token-level or span-level unlearning targets are misaligning unlearning objectives with the model’s internal behavior. |
| Approach: | They propose a selective method that uses model-intrinsic information to identify token-level or span-level unlearning targets within a text rather than entire sequences. |
| Outcome: | The proposed method achieves comparable unlearning performance while significantly better preserving retained knowledge. |
Transferring Knowledge via Neighborhood-Aware Optimal Transport for Low-Resource Hate Speech Detection (2022.aacl-main)
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| Challenge: | Existing approaches to detect hate speech are expensive and time-consuming . a new approach allows for flexible learning of neighborhood information . |
| Approach: | They propose a method that allows flexible modeling of neighbors retrieved from a resource-rich corpus to learn the amount of transfer. |
| Outcome: | The proposed training strategy improves on low-resource hate speech corpora over baselines. |
Disentangling the Effects of Unlearning in Measuring Parametric Faithfulness of Chain-of-Thought (2026.acl-srw)
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Ryo Mitsuhashi, Gaku Morio, Ayana Niwa, Masahiro Kaneko, Kentaro Inui, Terufumi Morishita, Yuta Koreeda, Yasuhiro Sogawa
| Challenge: | Chain-of-Thought (CoT) has been debated as a model's faithfulness to internal reasoning process. |
| Approach: | They propose to use unlearning to measure parametric faithfulness of models by adjusting for unintended artifacts of unlearning. |
| Outcome: | The proposed metric accounts for the unintended artifacts of unlearning and shows that it is non-negligible. |
Topic-aware Multimodal Summarization (2022.findings-aacl)
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| Challenge: | Existing work on multimodal summarization does not consider the topic of the content. |
| Approach: | They propose a topic-aware MS system which performs two tasks simultaneously: differentiating images into "on-topic" and "off-topic". |
| Outcome: | The proposed system outperforms the state-of-the-art approach by 1.7 % in ROUGE-L metric. |
ESG-KG: A Multi-modal Knowledge Graph System for Automated Compliance Assessment (2026.eacl-demo)
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| Challenge: | Existing methods for ESG compliance assessment rely on fact-based retrieval methods. |
| Approach: | They propose a multi-modal information extraction pipeline to extract, structure, and evaluate sustainability reports. |
| Outcome: | The proposed system extracts, structures, and evaluates ESG-related content from text, tables, figures, and infographics. |
OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding (2023.emnlp-demo)
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| Challenge: | Event understanding is fundamental for humans to understand the world. |
| Approach: | They propose an event understanding toolkit called OmniEvent that is comprehensive and fair . it supports mainstream modeling paradigms and the processing of 15 widely-used datasets . |
| Outcome: | The toolkit supports mainstream modeling paradigms and the processing of 15 widely-used English and Chinese datasets. |
MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection (2025.acl-long)
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| Challenge: | a rapid expansion of memes on social media highlights the need for effective methods to detect harmful content. |
| Approach: | They propose a multi-agent framework for zero-shot harmful meme detection that does not rely on annotated data. |
| Outcome: | The proposed framework outperforms existing zero-shot approaches on three meme datasets. |
Structured Discourse Representation for Factual Consistency Verification (2025.findings-acl)
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| Challenge: | Existing methods to verify factual consistency of text capture a performance gap compared with sentence-level entailment. |
| Approach: | They propose a method that combines structured discourse information extraction with a classifier, FDSpotter, for factual consistency verification. |
| Outcome: | The proposed method achieves competitive performance on two tasks: data-to-text generation and text summarisation. |
Measuring the Effect of Influential Messages on Varying Personas (2023.acl-short)
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| Challenge: | a new task estimates the response a persona might have upon seeing a news message . a first benchmark dataset is used to evaluate the performance of the proposed task . |
| Approach: | They propose a task to estimate the response a persona might have upon seeing a news message. |
| Outcome: | The proposed task estimates the response a persona might have upon seeing a news message. |
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding (2023.emnlp-demo)
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| Challenge: | Large Language Models (LLMs) are capable of understanding multi-modal content, but textonly human-computer interaction is not sufficient for many application scenarios. |
| Approach: | They propose a video-to-text generation task and a multi-modal framework that bootstraps cross-modal training from frozen pre-trained visual & audio encoders and frozen LLMs. |
| Outcome: | The proposed framework can understand both visual and auditory content in video and generate meaningful responses grounded in the visual and audio information presented in the videos. |
SLARD: A Chinese Superior Legal Article Retrieval Dataset (2025.coling-main)
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| Challenge: | Existing retrieval methods struggle to achieve ideal results, a study finds . existing large language models lack prior knowledge of the content of superior legal articles . |
| Approach: | They propose to use a Chinese superior legal article retrieval dataset to find relevant articles with higher legal effectiveness. |
| Outcome: | The proposed dataset shows that existing retrieval methods struggle to achieve ideal results. |
SummHelper: Collaborative Human-Computer Summarization (2023.emnlp-demo)
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| Challenge: | Existing approaches for text summarization are mostly automated, with limited space for human intervention and control. |
| Approach: | They propose a 2-phase summarization assistant that facilitates human-machine collaboration . it suggests possible content and generates a coherent summary from these selections . authors hope to improve the efficiency of the computer and human-involved approach . |
| Outcome: | The proposed summarization assistant is a 2-phase summarizing assistant . it suggests potential content and consolidates the output with visual mappings . the proposed system is available for free on youtube . |
Improve Language Model and Brain Alignment via Associative Memory (2025.findings-acl)
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| Challenge: | Existing studies have shown that associative memory is essential for language comprehension and comprehension. |
| Approach: | They propose to integrate associative memory into language models to improve alignment . they find alignment is improved in brain regions closely related to associativ memory processing . |
| Outcome: | The proposed model improves in brain regions closely related to associative memory processing. |
Entity-level Factual Adaptiveness of Fine-tuning based Abstractive Summarization Models (2024.eacl-long)
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| Challenge: | Abstractive summarization models generate factually inconsistent content when parametric knowledge conflicts with knowledge in the input document. |
| Approach: | They propose a method to enhance factual adaptiveness while achieving factual consistency on original datasets. |
| Outcome: | The proposed method improves factual adaptiveness while achieving factual consistency on original datasets. |
Towards Actual (Not Operational) Textual Style Transfer Auto-Evaluation (D19-55)
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| Challenge: | elucidates the dangerous current state of style transfer auto-evaluation research. |
| Approach: | They propose ways to aggregate the three metrics into one evaluator. |
| Outcome: | The proposed method could be used to aggregate the three metrics into one evaluator. |
A Corpus for Modeling User and Language Effects in Argumentation on Online Debating (P19-1)
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| Challenge: | Existing argumentation datasets have allowed only limited assessment of "user" traits because information on background of users is generally unavailable. |
| Approach: | They present a dataset of 78,376 debates generated over a 10-year period along with surprisingly comprehensive participant profiles. |
| Outcome: | The proposed dataset includes 78,376 debates generated over a 10-year period along with comprehensive participant profiles. |
HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarization (2022.acl-long)
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| Challenge: | Document structure is critical for efficient information consumption, but it is difficult to encode it efficiently into the modern Transformer architecture. |
| Approach: | They propose a task which injects Hierarchical Biases foR Incorporating Document Structure into attention score calculation. |
| Outcome: | The proposed model produces better question-summary hierarchies than comparisons on hierarchy quality and content coverage, the authors show . |
LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following (2025.coling-main)
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| Challenge: | E-commerce authoring requires engaging, diverse, and targeted content . Large language models lack memorization of domain-specific features in e-commerce applications . |
| Approach: | They propose a unified e-commerce authoring models that address contextual preferences of customers, sellers, and platforms . they propose to integrate interleaved features presented by participating objects into the models to empower authoring applications with comprehensive scenario understanding . |
| Outcome: | The proposed models achieve state-of-the-art evaluation performance and exhibit the advantage in zero-shot practical applications. |
An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation (2024.acl-long)
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Kun Zhu, Xiaocheng Feng, Xiyuan Du, Yuxuan Gu, Weijiang Yu, Haotian Wang, Qianglong Chen, Zheng Chu, Jingchang Chen, Bing Qin
| Challenge: | Experimental results show that retrieval-augmented generation improves accuracy and relevance of large language models. |
| Approach: | They propose to introduce the information bottleneck theory into retrieval-augmented generation by maximizing mutual information between compression and ground output while minimizing mutual information . |
| Outcome: | The proposed approach improves accuracy and correctness of answer generation and conciseness with 2.5% compression rate. |
CitationIE: Leveraging the Citation Graph for Scientific Information Extraction (2021.acl-long)
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| Challenge: | Existing work on scientific information extraction (SciIE) considers extraction solely based on the content of an individual paper, without considering the paper’s place in the broader literature. |
| Approach: | They propose to automate the extraction of key information from scientific documents by leveraging a complementary source: the citation graph of referential links between citing and cited papers. |
| Outcome: | The proposed model improves on a set of English-language scientific documents. |
Aligning Large Language Models via Fine-grained Supervision (2024.acl-short)
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| Challenge: | Pre-trained large-scale language models often generate biased or toxic text, misaligning with human intentions. |
| Approach: | They propose to use human feedback to improve LLM alignment by fine-grained token supervision . they ask annotators to edit less preferred responses to make them more favorable . |
| Outcome: | The proposed method improves LLM alignment by up to 5.1% in terms of win rate compared with the traditional model. |
The neural dynamics of word recognition and integration (2023.emnlp-main)
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| Challenge: | Using a computational model of word recognition, listeners combine expectations about upcoming content with incremental sensory evidence. |
| Approach: | They fit this model to scalp EEG signals recorded as subjects passively listened to a fictional story and found that words require more than 150 ms of input to be recognized. |
| Outcome: | The proposed model formalizes this perceptual process in Bayesian decision theory and reveals distinct neural processing of words depending on whether or not they can be quickly recognized. |
J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization (2026.acl-long)
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| Challenge: | Large language models (LLMs) are increasingly being used for reasoning intensive tasks. |
| Approach: | They propose an algorithm that trains judges to be robust to positional biases . they also propose a benchmark that evaluates judges in diverse reasoning settings . |
| Outcome: | The proposed algorithm outperforms GPT-4o and the next best small judge by 6.7% and 9% on ReasoningJudgeBench and JudgeBench. |
Extending Neural Generative Conversational Model using External Knowledge Sources (D18-1)
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| Challenge: | Existing generative dialogue models lack coherence and are content poor . however, current models lack the capacity to handle large unstructured knowledge sources. |
| Approach: | They propose an architecture to incorporate unstructured knowledge sources to enhance the next utterance prediction in chit-chat type of generative dialogue models. |
| Outcome: | The proposed architecture improves the next utterance prediction in chit-chat type of generative dialogue models by incorporating external knowledge from Wikipedia summaries and the NELL knowledge base. |
FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts (2024.findings-acl)
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Shubhankar Singh, Purvi Chaurasia, Yerram Varun, Pranshu Pandya, Vatsal Gupta, Vivek Gupta, Dan Roth
| Challenge: | Existing benchmarks for visual question answering lack in visual grounding and complexity, particularly in evaluating spatial reasoning skills. |
| Approach: | They propose to use flowcharts as visual contexts to assess the capabilities of visual question-answering multimodal language models in reasoning. |
| Outcome: | The proposed benchmarks evaluate models' ability to follow visual information without pre-existing knowledge on a suite of open-source and proprietary multimodal language models using various strategies, followed by an analysis of directional bias. |
Demonstrating ViviDoc: Generating Interactive Documents through Human-Agent Collaboration (2026.acl-demo)
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| Challenge: | Recent LLM-based agents can automate content creation, but naively applying them yields uncontrollable and unverifiable outputs. |
| Approach: | They propose a human-agent collaborative system that generates interactive educational documents from a single topic input. |
| Outcome: | The proposed system generates documents comparable in quality to human-authored ones. |
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization (2023.findings-eacl)
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| Challenge: | a recent study shows that accessing medical literature is difficult for laypeople because it is written for specialists and contains medical jargon. |
| Approach: | They propose a two-stage strategy to identify relevant content to be simplified . they first generate reference summaries via sentence matching between the original and simplified abstracts . |
| Outcome: | The proposed approach improves on a seq2seq-based test set on an English medical corpus . it also improves the SARI score by 1.1% . |
To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models (2024.findings-emnlp)
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Bozhong Tian, Xiaozhuan Liang, Siyuan Cheng, Qingbin Liu, Mengru Wang, Dianbo Sui, Xi Chen, Huajun Chen, Ningyu Zhang
| Challenge: | Existing unlearning paradigms are mired in vague forgetting boundaries, erasing knowledge indiscriminately. |
| Approach: | They propose a benchmark to evaluate if unlearning erases essential knowledge . they propose 'knowUnDo' which uses copyrighted content and privacy domains . |
| Outcome: | The proposed method is superior to existing methods in both precise knowledge unlearning and general knowledge retaining of LLMs. |
SafetyQuizzer: Timely and Dynamic Evaluation on the Safety of LLMs (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) have been used to evaluate the safety of their users . however, evaluation questions in current benchmarks are too straightforward and difficult to update with practical relevance due to their lack of correlation with real-world events. |
| Approach: | They propose a question-generation framework to evaluate the safety of LLMs in the Chinese context. |
| Outcome: | The proposed framework reduces decline rate while maintaining similar attack success rate. |
Exploiting contextual information to improve stance detection in informal political discourse with LLMs (2025.acl-srw)
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| Challenge: | Political stance detection is an increasingly relevant part of analyzing the flow of ideas in online environments where discourse is informal and implicitly expressed. |
| Approach: | They evaluate large language models for political stance detection in informal online discourse by analyzing user profiles derived from historical posts. |
| Outcome: | The proposed model improves accuracy by up to 74% on a political forum dataset. |
Explainable Hallucination through Natural Language Inference Mapping (2025.findings-acl)
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| Challenge: | Large language models (LLMs) often generate hallucinated content, making it crucial to identify and quantify inconsistencies in their outputs. |
| Approach: | They propose a framework that maps entailment and contradiction relations between inputs and outputs using a natural language inference model. |
| Outcome: | The proposed framework outperforms state-of-the-art methods by five percentage points while providing clear, interpretable explanations. |
Predicting Foreign Language Usage from English-Only Social Media Posts (N18-2)
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| Challenge: | Social media is known for its multi-cultural and multilingual interactions, a natural product of which is code-mixing. |
| Approach: | They analyze 6 million tweets produced by 27 thousand multilingual users speaking 12 other languages besides English to build predictive models to infer non-English languages users speak exclusively from their tweets. |
| Outcome: | The proposed models are based on a corpus of 6 million tweets produced by 27 thousand multilingual users speaking 12 other languages besides English . they show that content, style and syntax are the most predictive of non-English languages that users speak on Twitter. |
A Mixed Hierarchical Attention Based Encoder-Decoder Approach for Standard Table Summarization (N18-2)
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| Challenge: | Structured data summarization involves generation of summaries from structured input data. |
| Approach: | They propose a hierarchical attention-based encoder-decoder model which leverages the structure in addition to the content of the tables. |
| Outcome: | The proposed model improves on the weathergov dataset by 30% over the current state-of-the-art. |
Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints (2020.acl-main)
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| Challenge: | Existing methods for text generation ignore faithfulness between generated text and table . current methods ignore faithfulity, leading to generated information that goes beyond table content . |
| Approach: | They propose a Transformer-based generation framework to enforce faithfulness between generated text and table . they propose metric to evaluate faithfulness and automatic metric for automatic generating . |
| Outcome: | The proposed framework outperforms state-of-the-art methods in automatic evaluations and human evaluations. |
Abstract Meaning Representation for Multi-Document Summarization (C18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation of natural language based on linguistic theory . |
| Approach: | They propose to use Abstract Meaning Representation (AMR) as a content representation. |
| Outcome: | The proposed framework is fully data-driven and flexible. |
Fine-Grained Natural Language Inference Based Faithfulness Evaluation for Diverse Summarisation Tasks (2024.eacl-long)
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| Challenge: | Existing approaches to evaluate summary faithfulness are sub-optimal due to the granularity level considered for premises and hypotheses. |
| Approach: | They propose a novel approach that uses a variable premise size and simplifies summary sentences into shorter hypotheses. |
| Outcome: | The proposed model performs better on diverse summarisation tasks than existing models. |
A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism (2024.findings-acl)
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| Challenge: | Multi-way parallel, machine generated content dominates the translations in lower resource languages . a limited investigation suggests this selection bias is the result of low quality content generated in English and translated into many lower resource language via MT. |
| Approach: | They show that multi-way parallel, machine generated content dominates translations in many languages . they also find evidence of a selection bias in the type of content which is translated into many languages. |
| Outcome: | The results suggest that the low quality of multi-way translations on the web was likely created using machine translation. |
HELPD: Mitigating Hallucination of LVLMs by Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding (2024.emnlp-main)
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| Challenge: | Existing work detects hallucination by directly judging whether an object exists in an image, overlooking the association between the object and semantics. |
| Approach: | They propose a framework that incorporates hallucination feedback at both object and sentence semantic levels to alleviate over 15% of hallucinism. |
| Outcome: | The proposed framework can alleviate over 15% of hallucination even with a marginal degree of training. |
Measuring Bias and Agreement in Large Language Model Presupposition Judgments (2025.findings-acl)
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| Challenge: | Identifying linguistic bias in text requires the identification of explicit statements and presuppositions . large language models can be used to detect subtle forms of bias with no clear lexical signals . |
| Approach: | They propose to prompt large language models to evaluate presuppositions across texts . they find that LLMs may inadvertently reflect societal biases when identifying presuposed content . |
| Outcome: | The proposed model can be used to detect linguistic biases in text, but its accuracy is unclear . linguistic factors associated with human-model alignment suggest biase influenced by gender and ideology. |
Fine-Tuned LLMs are “Time Capsules” for Tracking Societal Bias Through Books (2025.naacl-long)
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| Challenge: | We develop a corpus comprising 593 fictional books across seven decades (1950-2019) to track bias evolution. |
| Approach: | They develop a method to trace and quantify bias evolution using fine-tuned LLMs on fictional books across seven decades to track bias evolution. |
| Outcome: | The proposed method traces and quantifies bias evolution in a corpus of 593 fictional books across seven decades. |
Course-Correction: Safety Alignment Using Synthetic Preferences (2024.emnlp-industry)
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Rongwu Xu, Yishuo Cai, Zhenhong Zhou, Renjie Gu, Haiqin Weng, Liu Yan, Tianwei Zhang, Wei Xu, Han Qiu
| Challenge: | Recent studies show that large language models generate harmful content, but the potential for generating harmful content is an escalating concern. |
| Approach: | They propose to fine-tune LLMs with preference learning to emphasize the preference for timely course-correction by using an automated pipeline. |
| Outcome: | The proposed model improves course-correction skills without affecting general performance and resists jailbreak attacks. |
Beyond "Not Novel Enough": Enriching Scholarly Critique with LLM-Assisted Feedback (2026.eacl-long)
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| Challenge: | Novelty assessment is a central yet understudied aspect of peer review . manuscript submissions double roughly every 15 years, and individual reviewers now complete an average of 14 reviews per year. |
| Approach: | They propose a structured approach for automated novelty evaluation that models expert reviewer behavior through three stages: content extraction, retrieval and synthesis of related work, and structured comparison for evidence-based assessment. |
| Outcome: | The proposed approach outperforms existing LLM-based baselines on 182 ICLR 2025 submissions with human-annotated reviewer novelty assessments. |
Optimizing Code Retrieval: High-Quality and Scalable Dataset Annotation through Large Language Models (2024.emnlp-main)
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| Challenge: | Existing methods for code retrieval struggle to balance scalability and annotation quality. |
| Approach: | They propose a method that integrates functions called within the repository and information on third-party APIs to enhance the annotation context. |
| Outcome: | The proposed method improves the annotation context by incorporating functions called within the repository and information on third-party API functionalities. |
Enhancing Reliability in Community Question Answering with an Expert-Oriented RAG System (2026.findings-eacl)
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| Challenge: | Existing LLMs require substantial computational resources and are prone to generating hallucinated or unreliable content. |
| Approach: | They propose an expert-oriented Retrieval-Augmented Generation framework which leverages user modeling to identify archived questions with answers that fully or partially address the user’s new query. |
| Outcome: | The proposed framework synthesizes expert-written answers from similar questions to generate unified answers. |
Rethinking Sentiment Style Transfer (2021.findings-emnlp)
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| Challenge: | Existing evaluation methods for text style transfer are unsatisfactory. |
| Approach: | They propose to use a graph-based method to extract attribute content from sentences . they propose an efficient regularization to leverage attribute-dependent content as guiding signals. |
| Outcome: | The proposed method is based on a YELP and IMDB dataset and it is able to detect errors in the human evaluation. |
The Correlation Between Emotion in Text and Speech Segments is Limited: A Cross-Modal Study (2026.findings-eacl)
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| Challenge: | a recent study has shown that text-to-speech systems can capture human-like emotion, but they lack the ability to predict emotion in speech. |
| Approach: | They propose to use 8 large language models for identifying emotion in text and 2 audio models for emotion in speech to investigate the correlation between emotion and speech. |
| Outcome: | The proposed models perform well on emotion recognition from situational text and audiobooks, but show weak correlation for Valence only. |
Profiling News Discourse Structure Using Explicit Subtopic Structures Guided Critics (2021.findings-emnlp)
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| Challenge: | Experimental results show that the hierarchical model learns to segment a document into subtopics and improves performance on the news discourse profiling task. |
| Approach: | They propose a hierarchical neural network that models multi-level interaction between sentences, subtopics, and the document. |
| Outcome: | The proposed model outperforms the existing model on the news discourse profiling task. |
Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation (D19-1)
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| Challenge: | Recent work on simultaneous translation is difficult because of its latency and quality. |
| Approach: | They propose a supervised-learning framework to learn adaptive policies from parallel text sequences . they use a model that predicts when a target word is read or WRITE if context provides enough information . |
| Outcome: | Experiments on German=>English show that the proposed method can learn flexible policies with better BLEU scores and similar latencies compared to previous work. |
Learning Sentiment Memories for Sentiment Modification without Parallel Data (D18-1)
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| Challenge: | Existing methods for sentiment modification generate input-irrelevant texts due to lack of parallel data. |
| Approach: | They propose a method that automatically extracts appropriate sentiment information from learned sentiment memories according to the specific context. |
| Outcome: | The proposed method significantly improves the content preservation degree and achieves the state-of-the-art performance. |
Zero-knowledge LLM hallucination detection and mitigation through fine-grained cross-model consistency (2025.emnlp-industry)
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| Challenge: | Existing methods for hallucination management fail to integrate both detection and mitigation without external knowledge sources. |
| Approach: | They propose a black-box framework that leverages fine-grained cross-model consistency to detect and mitigate hallucinations in LLM outputs without external knowledge sources. |
| Outcome: | The proposed framework improves hallucination detection scores by 6-39% on a FELM dataset . it achieves 9 percentage points improvement in answer accuracy on the GPQA-diamond dataset compared to existing approaches . |
How Alignment and Jailbreak Work: Explain LLM Safety through Intermediate Hidden States (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) rely on safety alignment to avoid malicious user inputs. |
| Approach: | They employ weak classifiers to explain LLM safety through the intermediate hidden states. |
| Outcome: | The proposed model can identify malicious and normal inputs and detect malicious ones without jailbreak. |
Investigating Content Planning for Navigating Trade-offs in Knowledge-Grounded Dialogue (2024.eacl-long)
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| Challenge: | Knowledge-grounded dialogues require a balance between being specific to what the conversation partner has said and being attributable to an underlying source document. |
| Approach: | They propose a framework that allows to experiment with various plan variables supported by prior work . they show that metric-aware planning mechanisms are better at automatic evaluations but underperform in human judgment compared to metric agnostic mechanisms. |
| Outcome: | The proposed framework supports metric-agnostic and metric aware content planning, but it underperforms in human judgment. |
Attack Prompt Generation for Red Teaming and Defending Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing studies construct attack prompts via manual or automatic methods, but these methods have limitations on cost and quality. |
| Approach: | They propose an attack framework to instruct LLMs to mimic human-generated prompts through in-context learning and a defense framework that fine-tunes victim LLM's through iterative interactions with the attack framework. |
| Outcome: | The proposed approach is based on experiments on different LLMs to evaluate their effectiveness against red teaming attacks. |
Predicting the Type and Target of Offensive Posts in Social Media (N19-1)
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| Challenge: | Prior work focused on detecting specific types of offensive content, such as hate speech, cyberbullying, or cyber-aggression. |
| Approach: | They propose to use a dataset to identify offensive content in social media . they compare the performance of different machine learning models to OLID . |
| Outcome: | The proposed dataset contains tweets annotated for offensive content using a fine-grained three-layer annotation scheme. |
DIA-HARM: Dialectal Disparities in Harmful Content Detection Across 50 English Dialects (2026.acl-long)
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| Challenge: | Current disinformation detection systems are predominantly developed and evaluated on Standard American English (SAE) . however, their robustness to dialectal variation is unexplored. |
| Approach: | They propose a benchmark for evaluating disinformation detection robustness across 50 English dialects . they use multi-value's linguistically-grounded transformations to introduce D-CUBE (Dialectal Disinformation Detection Corpus) |
| Outcome: | The proposed model outperforms zero-shot LLMs in human-written dialects while AI-generated content remains stable. |
MatRank: Text Re-ranking by Latent Preference Matrix (2022.findings-emnlp)
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| Challenge: | Existing methods for text ranking have improved performance, but there are still challenges. |
| Approach: | They propose a method that learns to re-rank the text retrieved for a given query by learning to predict the most relevant passage based on a latent preference matrix. |
| Outcome: | The proposed method outperforms all prior methods on datasets with extensive results. |
ComfyUI-R1: Exploring Reasoning Models for Workflow Generation (2026.findings-acl)
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| Challenge: | ComfyUI-R1 is the first large reasoning model for automated workflow generation. |
| Approach: | They propose a large reasoning model for automated workflow generation that builds on curated knowledge bases and a two-stage framework to fine-tune models for cold start and reinforcement learning for incentivizing reasoning capability. |
| Outcome: | The proposed model achieves 97% format validity rate, high pass rate, node-level and graph-level F1 scores, surpassing prior state-of-the-art methods that employ leading closed-source models such as GPT-4o and Claude series. |
A Multimodal Framework to Detect Target Aware Aggression in Memes (2024.eacl-long)
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| Challenge: | Recent research on memes’ detrimental facets is skewed towards high-resource languages, such as Bengali. |
| Approach: | They propose a dataset MIMOSA that annotates annotated memes across five aggression target categories in Bengali and propose 'Multimodal Attentive Fusion' to detect aggression targets. |
| Outcome: | The proposed method outperforms state-of-the-art methods in Bengali and in low-resource languages. |
Automated Essay Scoring System for Nonnative Japanese Learners (2020.lrec-1)
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| Challenge: | Existing systems only provide a holistic score that summarizes the quality of an essay, which provides little feedback for a language learner. |
| Approach: | They developed an automated essay scoring system for Japanese as a second language learners using an essay dataset with annotations for a holistic score and multiple trait scores. |
| Outcome: | The proposed system achieves the highest accuracy in various natural language processing tasks. |
A-TASC: Asian TED-Based Automatic Subtitling Corpus (2025.acl-long)
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| Challenge: | Existing AS corpora and primary metric SubER focus on European languages. |
| Approach: | They propose an Asian TED-based automatic subtitling corpus derived from English TED Talks and a modification of SubER to enable reliable evaluation of subtitle quality for languages without explicit word boundaries. |
| Outcome: | The proposed corpus is based on TED Talks audio segments, transcripts, and subtitles in Chinese, Japanese, Korean, and Vietnamese. |
Presentations by the Humans and For the Humans: Harnessing LLMs for Generating Persona-Aware Slides from Documents (2024.eacl-long)
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Ishani Mondal, Shwetha S, Anandhavelu Natarajan, Aparna Garimella, Sambaran Bandyopadhyay, Jordan Boyd-Graber
| Challenge: | Existing efforts to automate document-to-slide generation have failed to adapt to the persona of target audience or duration of presentation. |
| Approach: | They propose a concept of end-user specification-aware document to slides conversion that incorporates end- user specifications into the conversion process. |
| Outcome: | The proposed model can create persona-aware presentations tailored to the persona of target audience and cognitive abilities of target audiences. |
Alignment-Enhanced Decoding: Defending Jailbreaks via Token-Level Adaptive Refining of Probability Distributions (2024.emnlp-main)
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| Challenge: | Existing defenses against jailbreaks focus on perturbing or inspecting inputs, but ignore competing objectives, the underlying cause of alignment failures. |
| Approach: | They propose a novel defense that employs adaptive decoding to address the root causes of jailbreak issues. |
| Outcome: | The proposed defense improves safety alignment while maintaining helpfulness. |
Age Suitability Rating: Predicting the MPAA Rating Based on Movie Dialogues (2020.lrec-1)
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| Challenge: | Using the MPAA rating, movie content can negatively affect children’s behaviour, for example, watching specific programs may encourage irresponsible sexual behavior and alcohol usage in teenagers. |
| Approach: | They propose an RNN-based architecture that jointly models the genre and the emotions in the script to predict the MPAA rating. |
| Outcome: | The proposed model outperforms the traditional machine learning method by 7% and achieves an 81% weighted F1 score. |
Grouped-Attention for Content-Selection and Content-Plan Generation (2021.findings-emnlp)
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| Challenge: | Recent neural data-to-text generation models explicitly learn content-plan given a set of attributes as input. |
| Approach: | They propose a neural content-planner that captures local and global contexts . they use a token-level attention constrained within each input attribute . |
| Outcome: | The proposed model outperforms competitors by 4.92%, 4.70%, and 16.56% on real-world datasets. |
Collaborative Chain-of-Agents for Parametric-Retrieved Knowledge Synergy (2026.acl-long)
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| Challenge: | Existing RAG methods focus on external retrieval, while ignoring the rich content of the model. |
| Approach: | They propose a framework that enhances explicit synergy over parametric and retrieved knowledge by integrating external retrieval components into the input context of the LLMs. |
| Outcome: | The proposed framework enhances explicit synergy over parametric and retrieved knowledge. |
An Evaluation of Disentangled Representation Learning for Texts (2021.findings-acl)
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| Challenge: | Disentangled representations of texts encode information pertaining to different aspects of the text in separate vector embeddings. |
| Approach: | They propose to use a highly-structured natural language dataset to evaluate disentangled representations for texts. |
| Outcome: | The proposed models are well-suited for learning disentangled representations of texts on a synthetic natural language dataset. |
HateModerate: Testing Hate Speech Detectors against Content Moderation Policies (2024.findings-naacl)
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| Challenge: | Existing studies on hate speech detection have failed to answer this question. |
| Approach: | They propose a dataset for testing the behaviors of automated content moderators against content policies. |
| Outcome: | The proposed dataset includes hateful and non-hateful examples matching the 41 community standards guideline policies of Facebook. |
On Faithfulness and Factuality in Abstractive Summarization (2020.acl-main)
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| Challenge: | Existing conditional text generation models produce unfaithful and unfaithed summaries . current models accomplish a high level of fluency and coherence . |
| Approach: | They propose to use pretrained models for document summarization to better understand hallucinations . they find that textual entailment measures better correlate with faithfulness . |
| Outcome: | The proposed models generate faithful and factual summaries as evaluated by humans. |
Recent Trends in Linear Text Segmentation: A Survey (2024.findings-emnlp)
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| Challenge: | Linear text segmentation is the task of automatically tagging text documents with topic shifts . the task is based on coherence modeling and/or local cues to identify topic boundaries . |
| Approach: | They provide an overview of current advances in linear text segmentation . they highlight limitations of available resources and of the task itself . |
| Outcome: | The proposed task is based on the most recent literature and under-explored research directions. |
Ask To The Point: Open-Domain Entity-Centric Question Generation (2023.findings-emnlp)
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| Challenge: | *entity-centric question generation (ECQG) is a task motivated by real-world applications such as topic-specific learning, assisted reading, and fact-checking. |
| Approach: | They propose a PLM-based framework GenCONE with two modules: content focusing and question verification. |
| Outcome: | The proposed framework outperforms baselines and is effective and complementary in generating high-quality questions. |
Atoxia: Red-teaming Large Language Models with Target Toxic Answers (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) are still vulnerable to generation safety vulnerabilities. |
| Approach: | They propose a method that A**tacks LLMs with target "toxi" given a particular harmful answer, the method generates a user query and a misleading answer opening to examine the internal defects of a given LLM. |
| Outcome: | The proposed method detects safety risks in open-source models and state-of-the-art models such as GPT-4o. |
Towards A Friendly Online Community: An Unsupervised Style Transfer Framework for Profanity Redaction (2020.coling-main)
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| Challenge: | Existing methods for redacting offensive comments into non-offensive ones are inadequate to detect hateful content on social media platforms. |
| Approach: | They propose a method for transforming offensive comments into non-offensive ones using a Retrieve, Generate and Edit unsupervised style transfer pipeline. |
| Outcome: | The proposed method outperforms existing models on automatic metrics and human evaluations and consistently performs well on all automatic evaluation metrics. |
Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decoding (P19-1)
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| Challenge: | Existing methods for review generation lack topical and syntactic characteristics of natural languages. |
| Approach: | They propose a review generation model that uses aspect semantics, syntactic sketch, and context information to generate a sentence and corresponding words. |
| Outcome: | The proposed model can generate long and informative review text for users given a product and her/his rating on it. |
Diversify Question Generation with Continuous Content Selectors and Question Type Modeling (2020.findings-emnlp)
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| Challenge: | Existing methods to generate questions based on answers and relevant contexts are not suitable for all questions . |
| Approach: | They propose a method to generate questions from a given answer and its relevant context. |
| Outcome: | The proposed method achieves a better trade-off between generation quality and diversity compared with existing approaches. |
Effective and Efficient Query-aware Snippet Extraction for Web Search (2022.emnlp-main)
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| Challenge: | Existing methods to extract webpage snippets ignore contextual information of webpages, which may be sub-optimal. |
| Approach: | They propose a query-aware webpage snippet extraction method called DeepQSE that captures contextual information of webpages. |
| Outcome: | The proposed method can significantly improve the performance of DeepQSE without affecting its performance. |
Style versus Content: A distinction without a (learnable) difference? (2020.coling-main)
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| Challenge: | Textual style transfer assumes that it is possible to separate style from content . however, style transfer can provide insight into language more generally . |
| Approach: | They propose to use sentiment transfer to examine whether style transfer is possible . they employ adversarial encoder-decoder networks to analyze style-related features . |
| Outcome: | The proposed method combines style transfer with content preservation and fluency to show that style cannot be usefully separated from content within style transfer systems. |
Self-Induced Curriculum Learning in Self-Supervised Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Existing studies on curriculum learning focus on selecting the best distribution of data to train a system. |
| Approach: | They propose a self-supervised neural machine translation model that self-selects data without being told to do so. |
| Outcome: | The proposed model self-selects samples of increasing complexity and task relevance without being told to do so, and performs a denoising curriculum. |
A Robust Abstractive System for Cross-Lingual Summarization (N19-1)
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| Challenge: | We present a novel system for cross-lingual summarization that can be applied to low-resource languages. |
| Approach: | They propose a neural abstractive summarization system that can be applied to low-resource languages . they use machine translation and the New York Times summarizing corpus to create a corpus . |
| Outcome: | The proposed system achieves higher fluency than standard summarizers on translated documents . the proposed system can be easily applied to new low-resource languages . |
Visual Evidence Prompting Mitigates Hallucinations in Large Vision-Language Models (2025.acl-long)
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| Challenge: | LVLMs have shown impressive progress by integrating visual perception with linguistic understanding to produce contextually grounded outputs. |
| Approach: | They propose a visual evidence prompting method to mitigate hallucinations in large vision-language models by using small visual models to complement them. |
| Outcome: | The proposed method reduces hallucinations by reducing false activation and enhancing correct ones. |
Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization (D18-1)
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| Challenge: | Existing approaches to summarize documents are not extractive and require an abstractive approach. |
| Approach: | They propose a novel abstractive model which is conditioned on the article’s topics and based entirely on convolutional neural networks. |
| Outcome: | The proposed model outperforms an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans. |
Exploring the Impact of Personality Traits on LLM Toxicity and Bias (2025.emnlp-main)
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| Challenge: | anthropomorphic LLMs are being developed to serve diversified roles, but content safety concerns remain regarding their toxicity and toxicity. |
| Approach: | They propose to assign personality traits to large language models (LLMs) to reduce toxic language and social biases in their outputs by using the widely accepted HEXACO personality framework developed in social psychology. |
| Outcome: | The proposed model is able to perform on three toxic and bias benchmarks and shows that assigning personality traits reduces bias and toxicity similar to humans’ correlations between personality traits and toxic behaviors. |
Toxicity Detection is NOT all you Need: Measuring the Gaps to Supporting Volunteer Content Moderators through a User-Centric Method (2024.emnlp-main)
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| Challenge: | Existing efforts to automate content moderation have focused on identifying toxic, offensive, and hateful content . yet, it remains unclear whether improvements have addressed the needs of volunteer content moderators . |
| Approach: | They propose to use a model review to examine the availability of moderators' models to flag violations of various forum rules. |
| Outcome: | The proposed models perform poorly on a significant portion of the rules. |
Facts2Story: Controlling Text Generation by Key Facts (2020.coling-main)
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| Challenge: | Existing methods for story generation struggle with staying coherent for long periods of time. |
| Approach: | They propose a controlled generation task which expands a sequence of facts into a longer narrative. |
| Outcome: | The proposed model produces competitive fluency while adhering to the requested facts. |
GigaCheck: Detecting LLM-generated Content via Object-Centric Span Localization (2026.findings-acl)
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Irina Tolstykh, Aleksandra Tsybina, Sergey Yakubson, Aleksandr Gordeev, Vladimir Dokholyan, Maksim Kuprashevich
| Challenge: | GigaCheck is a framework for AI-generated text detection. |
| Approach: | They propose a dual-strategy framework for AI-generated text detection . they leverage representation learning of fine-tuned LLMs to discern authorship . |
| Outcome: | The proposed framework can detect LLM-generated content with high accuracy and accuracy . it can be used in mixed-authorship scenarios and in academic collaborations . |
EcoSafeRAG: Efficient Security through Context Analysis in Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Existing defense methods rely on internal knowledge of the model, which conflicts with the design concept of Retrieval-Augmented Generation (RAG). |
| Approach: | EcoSafeRAG uses sentence-level processing and bait-guided context diversity detection to identify malicious content . |
| Outcome: | EcoSafeRAG uses sentence-level processing and bait-guided context diversity detection to identify malicious content. |
CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction (2022.coling-1)
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| Challenge: | Existing solutions for quotation extraction use rule-based approaches and sequence labeling models. |
| Approach: | They propose a Context and Former-Label Enhanced Net for quotation extraction. |
| Outcome: | The proposed method achieves state-of-the-art performance on complicated quotation extraction on two public datasets and one proprietary dataset. |
Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversations (2023.acl-long)
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| Challenge: | Experimental results show the superiority of a mixed-initiative framework for emotional support conversation (ESC) ESC systems are emerging to provide prompt and convenient emotional support for helpseekers, including mental health support, counseling or motivational interviewing. |
| Approach: | They propose a knowledge-enhanced mixed-initiative framework that retrieves actual case knowledge from a large-scale mental health knowledge graph for generating mixed-initiative responses. |
| Outcome: | The proposed framework retrieves actual case knowledge from a large-scale mental health knowledge graph for generating mixed-initiative responses. |
Robustness of Multi-Source MT to Transcription Errors (2023.findings-acl)
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| Challenge: | In multilingual settings, the same content may be available in various languages via simultaneous interpreting, dubbing or subtitling. |
| Approach: | They hypothesize that leveraging multiple sources will improve translation quality if the sources complement one another in terms of correct information they contain. |
| Outcome: | The proposed method is robust to speech recognition errors on a 10-hour ESIC corpus. |
DITTO: A Spoofing Attack Framework on Watermarked LLMs via Knowledge Distillation (2026.eacl-long)
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| Challenge: | Large language models (LLMs) generate coherent, human-like text at scale, but raises concerns about authenticity and trust. |
| Approach: | They propose a threat of watermark spoofing that allows a malicious model to generate text containing the authentic-looking watermark of a trusted, victim model. |
| Outcome: | The proposed attack repurposes watermark radioactivity from a discoverable trait into an attack vector and replicates it. |
Persuasion at Play: Understanding Misinformation Dynamics in Demographic-Aware Human-LLM Interactions (2026.eacl-long)
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| Challenge: | Existing challenges in misinformation exposure and susceptibility vary across demographics. |
| Approach: | They propose a framework that investigates the bidirectional persuasion dynamics between LLMs and humans when exposed to misinformation. |
| Outcome: | The proposed framework analyzes the spread of misinformation under persuasion among demographic-oriented LLM agents. |
How emotional are you? Neural Architectures for Emotion Intensity Prediction in Microblogs (C18-1)
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| Challenge: | Social media based micro-blogging sites like Twitter are used for expressing emotions and opinions. |
| Approach: | They propose to combine convolutional and fully connected layers in a non-sequential manner to train deep multi-task learning models trained for all emotions at once in unified architecture. |
| Outcome: | The proposed model outperforms the previous system by 0.044 or 4.4% on the WASSA’17 EmoInt shared task dataset. |
Graph-tree Fusion Model with Bidirectional Information Propagation for Long Document Classification (2024.findings-emnlp)
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| Challenge: | Existing methods for document classification struggle with token limits and fail to adequately model hierarchical relationships within documents. |
| Approach: | They propose a novel model leveraging a graph-tree structure to capture local and global dependencies. |
| Outcome: | The proposed model captures syntactic relationships and broader document contexts without token limits and can handle arbitrarily long contexts. |
CDA: a Cost Efficient Content-based Multilingual Web Document Aligner (2021.eacl-main)
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| Challenge: | a Content-based document alignment approach is an efficient way to align multilingual web documents based on content. |
| Approach: | They propose a Content-based document alignment approach to align multilingual web documents based on content in parallel training data for machine translation systems. |
| Outcome: | The proposed method achieves comparable performance with state-of-the-art systems in the WMT-16 Bilingual Document Alignment Shared Task benchmark while operating in multilingual space. |
Human and LLM-Based Resume Matching: An Observational Study (2025.findings-naacl)
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Swanand Vaishampayan, Hunter Leary, Yoseph Berhanu Alebachew, Louis Hickman, Brent A. Stevenor, Weston Beck, Chris Brown
| Challenge: | Resume matching assesses the extent to which candidates qualify for jobs based on the content of resumes. |
| Approach: | They compare GPT-4 and human ratings for resumes submitted to job openings from diverse fields using real-world evaluation criteria. |
| Outcome: | The proposed model improves the quality of LLM ratings and does not show bias. |
An Empirical Study of Position Bias in Modern Information Retrieval (2025.findings-emnlp)
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| Challenge: | a new evaluation framework is used to assess the extent and impact of position bias in information retrieval. |
| Approach: | They introduce a position-aware retrieval benchmark and a diagnostic metric to quantify position bias . they compare models with BM25, dense embedding models, ColBERT-style late-interaction models . |
| Outcome: | The proposed framework evaluates retrieval models for position bias from a worst-case perspective. |
A Practical Examination of AI-Generated Text Detectors for Large Language Models (2025.findings-naacl)
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| Challenge: | Existing methods to detect large language models are prone to misuse, such as generating fake news articles, facilitating academic plagiarism or spamming. |
| Approach: | They evaluate several popular detectors to evaluate their effectiveness against a range of domains, datasets, and models. |
| Outcome: | The proposed methods perform poorly in certain settings, with TPR@.01 as low as 0%. |
Towards Domain-Independent Text Structuring Trainable on Large Discourse Treebanks (2020.findings-emnlp)
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| Challenge: | Text structuring is a fundamental step in natural language generation, especially when generating multi-sentential text. |
| Approach: | They propose a novel task that combines neural dependency tree induction with pointer networks to train on large discourse treebanks. |
| Outcome: | The proposed method outperforms existing content ordering metrics and outperformed existing ones. |
SEPS: A Separability Measure for Robust Unlearning in LLMs (2025.emnlp-main)
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| Challenge: | Existing unlearning metrics assess whether a model correctly answers retain queries and rejects forget queries, but they fail to capture real-world scenarios where forget queries rarely appear in isolation. |
| Approach: | They propose an evaluation framework that explicitly measures a model’s ability to both forget and retain information within a single prompt. |
| Outcome: | The proposed approach significantly improves unlearning effectiveness, demonstrating robustness even in complex settings with up to eight mixed forget and retain queries in a single prompt. |
From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization (2025.findings-naacl)
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| Challenge: | a recent study investigated hallucinations in multi-document summarization tasks . but, it is unclear how challenges arising from handling multiple documents affect outputs . |
| Approach: | They investigate how hallucinations manifest in large language models when summarizing topic-specific information from a set of documents. |
| Outcome: | The proposed benchmarks show that the models generate more hallucinations than baselines . the results highlight the need for more effective approaches to mitigate hallucinosity in MDS . |
HonestBait: Forward References for Attractive but Faithful Headline Generation (2023.findings-acl)
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| Challenge: | Current approaches to generating attractive headlines often learn directly from data based on clicks and views . clickbait models fail to reveal how much interest is raised by the writing style and how much is due to the event or topic itself . |
| Approach: | They propose a framework for generating headlines using forward references . they use a dataset containing pairs of fake news and verified news . |
| Outcome: | The proposed framework yields more attractive headlines while maintaining high veracity . the framework is based on a dataset containing fake news with verified news . |
LUQ: Long-text Uncertainty Quantification for LLMs (2024.emnlp-main)
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| Challenge: | Existing research on Uncertainty Quantification (UQ) predominantly targets short text generation, however, real-world applications often necessitate much longer responses. |
| Approach: | They propose a method that ensembles responses from multiple models and selects the response with the lowest uncertainty. |
| Outcome: | The proposed method outperforms baseline methods in correlating with the model’s factuality scores (negative coefficient of -0.85 observed for Gemini Pro). |
Jailbreak Open-Sourced Large Language Models via Enforced Decoding (2024.acl-long)
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Hangfan Zhang, Zhimeng Guo, Huaisheng Zhu, Bochuan Cao, Lu Lin, Jinyuan Jia, Jinghui Chen, Dinghao Wu
| Challenge: | Existing studies show that Large Language Models can be misused to generate undesired content. |
| Approach: | They propose to use large language models to manipulate the generation process to generate undesired content without heavy computations or prompt designs. |
| Outcome: | The proposed method shows that open-sourced large language models could be misused to generate undesired content without heavy computations or prompt designs. |
Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise: A Case Study on Chinese Legal Domain (2024.findings-acl)
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| Challenge: | Recent large language models like GPT-4 have demonstrated astonishing zero-shot capabilities in general domain tasks, but they often generate content with hallucinations in specific domains such as Chinese law. |
| Approach: | They propose a framework for adapting large language models (LLMs) to Chinese legal domains by reformulating generation as an adapt-retrieve-revise process. |
| Outcome: | The proposed framework outperforms existing models in the Chinese legal domain by +33.6 points in the zero-shot setting. |
CoANZSE Audio: Creation of an Online Corpus for Linguistic and Phonetic Analysis of Australian and New Zealand Englishes (2024.lrec-main)
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| Challenge: | CoANZSE Audio is a searchable online corpus of 195 million words of geo-located YouTube transcripts of local government channels. |
| Approach: | They describe the methods used to create the corpus from open-source tools and the architecture of the CoANZSE Audio website. |
| Outcome: | The corpus contains 195-million-word transcripts of local government channels . it is one of the first large, free, fully searchable online corpora containing data suitable for acoustic phonetic analyses in addition to lexical, grammatical, and discourse properties of Australian and New Zealand Englishes. |
LipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization (2022.coling-1)
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| Challenge: | Existing work has addressed each element individually, but this study focuses on LipKey, the largest news corpus with human-written abstractive summaries, absent keyphrases, and titles. |
| Approach: | They propose a novel news dataset that consists of highly absent keyphrases . they combine lips keyphrase and TF-IDF to obtain abstractive summaries . |
| Outcome: | The proposed dataset is the largest news corpus with human-written abstractive summaries, absent keyphrases, and titles. |
VideoEraser: Concept Erasure in Text-to-Video Diffusion Models (2025.emnlp-main)
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| Challenge: | Experimental results show that VideoEraser outperforms prior methods regarding efficacy, integrity, fidelity, robustness, and generalizability. |
| Approach: | They propose a training-free framework that prevents T2V diffusion models from generating videos with undesirable concepts even when explicitly prompted with those concepts. |
| Outcome: | The proposed framework outperforms existing methods in erasure, celebrity erasion, and explicit content erasing tasks. |
Enconter: Entity Constrained Progressive Sequence Generation via Insertion-based Transformer (2021.eacl-main)
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| Challenge: | Autoregressive language models do not perform well under hard lexical constraints as they lack fine control of content generation process. |
| Approach: | They propose a new insertion transformer that considers hard lexical constraints and imposes rules over objects in the generated text. |
| Outcome: | The proposed model outperforms baseline models in several performance metrics rendering it more suitable in practical applications. |
Are AI-Generated Text Detectors Robust to Adversarial Perturbations? (2024.acl-long)
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| Challenge: | Existing detectors for AI-generated text lack robustness against adversarial perturbations, with even minor changes in characters or words causing a reversal in distinguishing between human-created and AI-generated text. |
| Approach: | They propose a siamese calibration technique to train the model to make equally confident predictions under different noise, which improves the model’s robustness against adversarial perturbations. |
| Outcome: | The proposed detector outperforms baseline methods on four datasets and is more generalizable in cross-domain, cross-genre, and mixed-source scenarios. |
Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens (2026.findings-acl)
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Hellina Hailu Nigatu, Bethelhem Yemane Mamo, Bontu Fufa Balcha, Debora Taye Tesfaye, Elbethel Daniel Zewdie, Ikram Behiru Nesiru, Jitu Ewnetu Hailu, Senait Mengesha Yayo
| Challenge: | afan oromo, amharic, and tigrinya are low-resourced languages . they are used for training, benchmarks, news, health, and sports . afono o'mara: quantity does not guarantee quality of MT datasets . |
| Approach: | They investigate the quality of machine translation datasets for three low-resourced languages . they found a large skew towards the male gender in the datasets . |
| Outcome: | The results show that training data has large representation of political and religious text, but benchmark datasets focus on news, health, and sports. |
Language model acceptability judgements are not always robust to context (2023.acl-long)
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Koustuv Sinha, Jon Gauthier, Aaron Mueller, Kanishka Misra, Keren Fuentes, Roger Levy, Adina Williams
| Challenge: | a recent study found that models prefer acceptable inputs over acceptable ones. |
| Approach: | They find that model judgements are generally robust when placed in randomly sampled linguistic contexts, but unstable when contexts match the test stimuli in syntactic structure. |
| Outcome: | The proposed model performance improves when contexts match syntactic structure, and declines when they are unacceptable. |
OSC: Cognitive Orchestration through Dynamic Knowledge Alignment in Multi-Agent LLM Collaboration (2025.findings-emnlp)
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| Challenge: | Prior work has advanced agent selection and result aggregation, efficient linguistic interactions for deep collaboration among expert agents remain a critical bottleneck. |
| Approach: | They propose a knowledge-aware adaptive collaboration framework to enhance cognitive synergy in multi-agent systems with large language models. |
| Outcome: | The proposed framework improves synergy between agents and language models by enabling agents to dynamically perceive their collaborators’ cognitive states. |
Controllable Text Generation with Focused Variation (2020.findings-emnlp)
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Lei Shu, Alexandros Papangelis, Yi-Chia Wang, Gokhan Tur, Hu Xu, Zhaleh Feizollahi, Bing Liu, Piero Molino
| Challenge: | Focused-Variation Network (FVN) is a new model to control language generation. |
| Approach: | They propose a model that learns discrete latent spaces for each attribute inside codebooks and uses them to generate fluent text. |
| Outcome: | The proposed model can generate fluent and mostly coherent text on two text generation datasets with annotated content and style, and show state-of-the-art performance as assessed by automatic and human evaluations. |
WIKIGENBENCH:Exploring Full-length Wikipedia Generation under Real-World Scenario (2025.coling-main)
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Jiebin Zhang, Eugene J. Yu, Qinyu Chen, Chenhao Xiong, Dawei Zhu, Han Qian, Mingbo Song, Weimin Xiong, Xiaoguang Li, Qun Liu, Sujian Li
| Challenge: | Existing efforts to generate Wikipedia articles for new events fall short of real-world application. |
| Approach: | They propose a benchmark to generate Wikipedia articles for new events under real-world scenarios . they use systematic metrics and LLM-based metrics to assess verifiability, organization, and other aspects aligned with real-life scenarios. |
| Outcome: | The proposed benchmarks show that hierarchical-based methods generate more comprehensive content while fine-tuned methods achieve better verifiability. |
ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection (2026.findings-acl)
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| Challenge: | Existing toxic content detection methods focus on sentence-level classification but fail to provide readable and contiguous toxic evidence spans. |
| Approach: | They propose an explainability-oriented method for Chinese toxic content detection methods . they refine saliency cues into fine-grained toxic spans with lightweight LLM guidance . |
| Outcome: | The proposed method improves classification accuracy and toxic span extraction while preserving efficient encoder-based inference and producing more coherent explanations. |
Beyond the Textual: Generating Coherent Visual Options for MCQs (2025.findings-emnlp)
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| Challenge: | Multiple choice questions (MCQs) are crucial for deep thinking and knowledge integration in education. |
| Approach: | They propose a cross-modal options synthesis framework for generating MCQs with visual options. |
| Outcome: | The proposed framework produces a plausible and visually similar answer and distractor . it also includes a discrimination module to identify content suitable for visual options . |
SafeSwitch: Steering Unsafe LLM Behavior via Internal Activation Signals (2025.findings-emnlp)
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| Challenge: | Existing safety mechanisms for large language models (LLMs) are inadequate to fully leverage their internal cognitive processes. |
| Approach: | They propose a framework that regulates unsafe outputs by utilizing the prober-based internal state monitor that actively detects harmful intentions. |
| Outcome: | The proposed framework reduces harmful outputs by approximately 80% while maintaining strong utility. |
STAPI: An Automatic Scraper for Extracting Iterative Title-Text Structure from Web Documents (2022.lrec-1)
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| Challenge: | Formal documents are organized into sections of text, each with a title . but there is no corpus of web documents annotated with titles and prose texts . cnn.com's john mccarthy and daniel mclears are working on a new title-text dataset . |
| Approach: | They propose a first title-text dataset on web documents that incorporates a wide variety of domains to facilitate downstream training. |
| Outcome: | The proposed system outperforms baseline models in terms of title-text identification. |
Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning (2022.naacl-main)
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| Challenge: | Controlled table-to-text generation is a new approach to generate textual descriptions for highlighted subparts of a table. |
| Approach: | They propose an equivariance learning framework which encodes tables with a structure-aware self-attention mechanism and a positional encoding mechanism to preserve relative position of tokens in the same cell. |
| Outcome: | The proposed framework is free to be plugged into existing table-to-text generation models and has improved T5-based models to offer better performance on ToTTo and HiTab. |
Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts (2024.emnlp-main)
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| Challenge: | Experimental results show Per-Pcs outperforms non-personalized and PEFT retrieval baselines, offering performance comparable to OPPU with significantly lower resource use across six tasks. |
| Approach: | They propose a framework that allows users to safely share and assemble personalized large language models using their history data. |
| Outcome: | Experimental results show that Per-Pcs outperforms non-personalized and PEFT retrieval baselines with significantly lower resource use across six tasks. |
LRMM: Learning to Recommend with Missing Modalities (D18-1)
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| Challenge: | Existing methods for content-based recommendation with missing or corrupted modalities are lacking in learning multimodal models. |
| Approach: | They propose a multimodal multimodal autoencoder that learns multimodal representations for complementing and imputing missing modalities. |
| Outcome: | The proposed framework achieves state-of-the-art performance on rating prediction tasks and is more robust to previous methods in alleviating data-sparsity and the cold-start problem. |
PlagBench: Exploring the Duality of Large Language Models in Plagiarism Generation and Detection (2025.naacl-long)
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| Challenge: | Recent studies have raised concerns about the potential threats large language models pose to academic integrity and copyright protection. |
| Approach: | They propose a dataset of 46.5K synthetic text pairs that represent three major types of plagiarism: verbatim copying, paraphrasing, and summarization. |
| Outcome: | The proposed dataset shows that GPT-3.5 Turbo can produce high-quality paraphrases and summaries without significantly increasing text complexity compared to GPT-4 Turbo. |
Cross-Lingual Cross-Platform Rumor Verification Pivoting on Multimedia Content (D18-1)
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| Challenge: | rumors with multimedia content are becoming more and more common on social networks . a new feature set is proposed to verify rumors pivoting on multimedia content . |
| Approach: | They propose to use multimedia content to find external information on social media platforms . they propose to leverage semantic similarity between rumors and external information . |
| Outcome: | The proposed approach achieves state-of-the-art results on social networks . it leverages semantic similarity between rumors and external information . |
Automatic Detection of Vague Words and Sentences in Privacy Policies (D18-1)
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| Challenge: | a recent study has raised concerns over privacy policies' opaqueness . lack of clarity in privacy policies can lead to undesired ads and privacy breaches . |
| Approach: | They propose to analyze the semantics of vague words and sentences and use them to identify vague content in privacy policies. |
| Outcome: | The proposed methods are effective and provide suggestions for improving privacy policies. |
COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements (2023.findings-acl)
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Xuhui Zhou, Hao Zhu, Akhila Yerukola, Thomas Davidson, Jena D. Hwang, Swabha Swayamdipta, Maarten Sap
| Challenge: | Understanding the harms and offensiveness of statements requires reasoning about the social and situational context in which they are made. |
| Approach: | They propose a context-aware formalism for explaining the intents, reactions, and harms of offensive statements grounded in their social and situational contexts. |
| Outcome: | The proposed framework is the first context-aware formalism for explaining the intents, reactions, and harms of offensive statements grounded in their social and situational context. |
CCSRD: Content-Centric Speech Representation Disentanglement Learning for End-to-End Speech Translation (2023.findings-emnlp)
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| Challenge: | Existing speech-to-text translation models can extract features from speech inputs, but they may include non-linguistic speech factors such as pitch, timbre and speaker identity. |
| Approach: | They propose a content-centric speech representation disentanglement learning framework for speech translation that decomposes speech representations into content representations and non-linguistic representations via representation disentanglement learning. |
| Outcome: | The proposed framework outperforms state-of-the-art speech translation models and cascaded models on five translation directions. |
Adversarial Concept Erasure in Kernel Space (2022.emnlp-main)
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| Challenge: | Large neural networks in NLP produce real-valued representations that encode the bit of human language that they were trained on. |
| Approach: | They propose a kernelization of the recently-proposed linear concept-removal objective and propose to remove linear subspaces from the representation space. |
| Outcome: | The proposed kernelization protects against the ability of nonlinear adversaries to recover the concept. |
ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning (2025.emnlp-main)
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Ziqing Qiao, Yongheng Deng, Jiali Zeng, Dong Wang, Lai Wei, Guanbo Wang, Fandong Meng, Jie Zhou, Ju Ren, Yaoxue Zhang
| Challenge: | Existing methods for fine-tuning-based compression suffer from verbose outputs, increasing computational overhead. |
| Approach: | They propose a framework to generate concise reasoning chains using Confidence Injection and Early Stopping. |
| Outcome: | The proposed framework reduces the length of the model by up to 50% while maintaining high task accuracy. |
A Mousetrap: Fooling Large Reasoning Models for Jailbreak with Chain of Iterative Chaos (2025.findings-acl)
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| Challenge: | Large Reasoning Models (LRMs) have advanced beyond traditional Large Language Models, yet they pose heightened safety risks. |
| Approach: | They propose a first jailbreak attack targeting Large Reasoning Models . they exploit a Chaos Machine component to transform attack prompts with diverse one-to-one mappings based on the reasoning chain . |
| Outcome: | The proposed attack exploits the unique vulnerabilities of LRMs by integrating a Chaos Machine. success rates of the mousetrap attack are as high as 96%, 86% and 98% respectively. |
REInstruct: Building Instruction Data from Unlabeled Corpus (2024.findings-acl)
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| Challenge: | Existing methods for annotating instruction data are expensive and difficult to scale. |
| Approach: | They propose a method to automatically build instruction data from an unlabeled corpus without heavy reliance on proprietary LLMs and human annotation. |
| Outcome: | The proposed method outperforms existing methods on AlpacaEval leaderboard and other open-source methods. |
Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models (2023.acl-long)
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| Challenge: | generative large language models (LLMs) are widely used but fine-tuned to improve performance on downstream applications leads to violations of model licenses, model theft, and copyright infringement. |
| Approach: | They propose to trace back the origin of a model trained to its pre-trained base model . they use different knowledge levels and attribution strategies to find out how the model was trained . |
| Outcome: | The proposed method can trace back 8 out of 10 fine tuned models with different knowledge levels and attribution strategies. |
Nutri-bullets Hybrid: Consensual Multi-document Summarization (2021.naacl-main)
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| Challenge: | Existing methods for generating comparative summaries that highlight similarities and contradictions in input documents are lacking large parallel training data for their training. |
| Approach: | They propose a method for generating comparative summaries that highlight similarities and contradictions in input documents by using a neural interpretation of traditional concept-to-text generation systems. |
| Outcome: | The proposed model is compared with conventional methods in the domain of nutrition and health, where the existing models lack large parallel training data. |
SCALE: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human Intervention (2025.acl-long)
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| Challenge: | Content analysis is labor-intensive and time-consuming process that requires multiple rounds of manual annotation, domain expert discussion, and rule-based refinement. |
| Approach: | They propose a multi-agent framework that effectively Simulates Content Analysis via Large language model (LLM) ag Ents. |
| Outcome: | The proposed framework achieves human-approximated performance across various content analysis tasks. |
Language Technology Programme for Icelandic 2019-2023 (2020.lrec-1)
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Anna Nikulásdóttir, Jón Guðnason, Anton Karl Ingason, Hrafn Loftsson, Eiríkur Rögnvaldsson, Einar Freyr Sigurðsson, Steinþór Steingrímsson
| Challenge: | a new national language technology programme for Icelandic is described . the programme aims to make Icelandic usable in communication and interactions in the digital world . |
| Approach: | They describe a new national language technology programme for Icelandic . the programme aims to make Icelandic usable in communication and interactions in the digital world . |
| Outcome: | The proposed programme aims to make Icelandic usable in communication and interactions in the digital world. |
Predicting Long-Term Citations from Short-Term Linguistic Influence (2022.findings-emnlp)
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| Challenge: | Existing methods to quantify linguistic influence in timestamped documents are not informative about extent to which a paper affected subsequent publications. |
| Approach: | They propose to quantify linguistic influence in timestamped document collections by estimating a Hawkes process with a low-rank parameter matrix and identify lexical and semantic changes using contextual embeddings and word frequencies. |
| Outcome: | The proposed method is based on an online evaluation with incremental temporal training/test splits, in comparison with a strong baseline that includes predictors for initial citation counts, topics, and lexical features. |
CKnowEdit: A New Chinese Knowledge Editing Dataset for Linguistics, Facts, and Logic Error Correction in LLMs (2025.acl-long)
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| Challenge: | CKnowEdit is the first-ever knowledge editing dataset designed to correct linguistic, factual, and logical errors in Large Language Models. |
| Approach: | They propose a Chinese knowledge editing dataset to correct linguistic, factual, and logical errors in Large Language Models. |
| Outcome: | The proposed dataset highlights the challenges that LLMs face in mastering Chinese . CKnowEdit can correct linguistic, factual, and logical errors in Chinese, the authors show . |
VLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training (2025.findings-emnlp)
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| Challenge: | a significant drawback of Vision-language Models is their reliance on static training data, leading to outdated information and limited contextual awareness. |
| Approach: | They propose a framework with knowledge-enhanced reranking and noise-injected training to improve the VLM's ranking ability. |
| Outcome: | The proposed framework is based on a simple yet effective instruction template and is able to induce its ranking ability and serve it as a reranker to precisely filter the top-k retrieved images. |
On A Scale From 1 to 5: Quantifying Hallucination in Faithfulness Evaluation (2025.findings-naacl)
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| Challenge: | Hallucination is a popular topic in natural language generation (NLG). |
| Approach: | They propose to use large language models to evaluate faithfulness of guided NLGs by a rubric template and large language inference models to score the generation on quantifiable scales. |
| Outcome: | The proposed system can provide accurate judgement and explain whether a source and generation are factually consistent. |
Contextual Knowledge Learning for Dialogue Generation (2023.acl-long)
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| Challenge: | Incorporating conversational context and knowledge into dialogue generation models has been essential for improving the quality of the generated responses. |
| Approach: | They propose a method to incorporate conversational context and knowledge into dialogue generation models . they use Latent Vectors to capture the relationship between context and knowing . |
| Outcome: | The proposed approach improves performance with two standard datasets and human evaluations. |
On Positional Bias of Faithfulness for Long-form Summarization (2025.naacl-long)
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| Challenge: | Large language models exhibit positional bias in long-context settings, under-attending to information in the middle. |
| Approach: | They compile eight human-annotated long-form summarization datasets to evaluate faithfulness . they find that LLMs faithfully summarize beginning and end of documents but neglect middle content . |
| Outcome: | The proposed methods show that LLMs under-attend to information in the middle of inputs. |
AffectCodec: Emotion-Preserving Neural Speech Codec for Expressive Speech Modeling (2026.findings-acl)
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| Challenge: | Existing codecs optimize acoustic reconstruction, leaving emotion expressiveness insufficiently modeled at the representation level. |
| Approach: | They propose an emotion-guided neural speech codec that preserves emotional information while maintaining semantic fidelity and prosodic naturalness. |
| Outcome: | The proposed codec preserves emotional cues while maintaining semantic fidelity and prosodic naturalness. |
RtGender: A Corpus for Studying Differential Responses to Gender (L18-1)
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| Challenge: | Prior work on linguistic gender difference and communications about gender has focused on language about or portraying persons of a particular gender. |
| Approach: | They present a multi-genre corpus of 25M comments from five socially and topically diverse sources tagged for the gender of the addressee and 30k annotations for sentiment and relevance of these responses. |
| Outcome: | The proposed dataset shows that responses to women are more emotive and about the speaker as an individual (rather than about the content being responded to). |
Imitation Attacks and Defenses for Black-box Machine Translation Systems (2020.emnlp-main)
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| Challenge: | Using simulated experiments, we demonstrate that MT systems can be stolen even when imitation models have different input data or architectures than their target models. |
| Approach: | They propose a defense that modifies translation outputs to misdirect optimization of imitation models. |
| Outcome: | The proposed defense degrades the adversary’s BLEU score and attack success rate at some cost in the defender’s performance and inference speed. |
GreekMMLU: A Native-Sourced Multitask Benchmark for Evaluating Language Models in Greek (2026.findings-acl)
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Yang Zhang, Mersin Konomi, Christos Xypolopoulos, Konstantinos Divriotis, Konstantinos Skianis, Giannis Nikolentzos, Giorgos Stamou, Guokan Shang, Michalis Vazirgiannis
| Challenge: | Existing evaluation benchmarks for large language models are limited for Greek . Existing datasets are often machine-translated from English, failing to capture Greek linguistic and cultural characteristics. |
| Approach: | They propose a native-sourced benchmark for massive multitask language understanding in Greek . they publicize 16,857 samples and reserve 4,948 samples for a private leaderboard . |
| Outcome: | The proposed model is based on 21,805 multiple-choice questions across 45 subject areas . the model is publicly released and reserved for a private leaderboard . |
AttnComp: Attention-Guided Adaptive Context Compression for Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Existing methods for augmented large language models suffer from irrelevant retrieved content . existing methods struggle to adapt compression rates for different context, maintain low latency . |
| Approach: | We propose an adaptive, efficient and context-aware compression framework to reduce retrieved content . AttnComp uses a top-p compression algorithm to retain the minimal set of documents whose attention weights exceed a threshold. |
| Outcome: | Experiments show that AttnComp outperforms existing compression methods and uncompressed baselines in achieving higher accuracy with substantial compression rates and lower latency. |
A Fine-Grained Taxonomy of Replies to Hate Speech (2023.emnlp-main)
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| Challenge: | a new corpus of responses to hate speech is developed to counter hate speech . authors work with real, user-generated hate speech and all the replies it elicits . counterspeech refers to a "direct response that counters hate speech" |
| Approach: | They propose a taxonomy of responses to hate speech and a new corpus to analyze responses . they find that responses to user-generated hate speech are more effective than replies generated by a third party . |
| Outcome: | The proposed taxonomy of responses to hate speech and a new corpus provide insights into content real users reply with and which replies are empirically most effective. |
Data Curation Alone Can Stabilize In-context Learning (2023.acl-long)
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| Challenge: | In-context learning (ICL) is a new paradigm for few-shot learning with pretrainable large language models . however, randomly sampling examples from a training set leads to high variance in performance . |
| Approach: | They propose two methods to select training examples from a training set and then carefully curate them from corresponding subsets. |
| Outcome: | The proposed method improves accuracy over sampling from the entire training set. |
Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification (2021.findings-acl)
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| Challenge: | a new study examines the use of content addition in text simplification when complex concepts need to be explained. |
| Approach: | They present a data-driven study of content addition in text simplification . they analyze 1.3K instances of elaborative simplification in the Newsela corpus . |
| Outcome: | The proposed study shows that contextual specificity can improve elaboration generation performance. |
Culture Matters in Toxic Language Detection in Persian (2025.acl-long)
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| Challenge: | Toxic language detection is crucial for creating safer online environments and limiting the spread of harmful content. |
| Approach: | They compare different methods for toxic language detection in Persian to fine-tune, enrich data, and cross-lingual transfer learning. |
| Outcome: | The language of a country with cultural similarities to Persian yields better results in transfer learning. |
Controversy and Conformity: from Generalized to Personalized Aggressiveness Detection (2021.acl-long)
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Kamil Kanclerz, Alicja Figas, Marcin Gruza, Tomasz Kajdanowicz, Jan Kocon, Daria Puchalska, Przemyslaw Kazienko
| Challenge: | a new method to personalize documents that are perceived differently by users is needed . a recent study found that only a few annotations of controversial documents outperform classic methods . |
| Approach: | They propose to use some known, most controversial texts whose offensiveness is very ambiguous . they use user conformity-based measures or embeddings of their previous annotations to improve personalized reasoning . |
| Outcome: | The proposed methods outperform standard methods in document controversy and user nonconformity . the more controversial the content, the greater the gain, the authors say . |
Enhancing LLM Knowledge Learning through Generalization (2025.findings-emnlp)
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| Challenge: | Continued pre-training on paraphrased data has shown empirical promise for enhancing knowledge acquisition, but this approach is costly and unreliable as it relies on external models or manual effort for rewriting. |
| Approach: | They propose formatting-based data augmentation which diversifies documents conveying the same knowledge by altering document formats rather than their content. |
| Outcome: | The proposed methods improve generalization to diverse paraphrased contexts and enhance pre-training and instruction tuning. |
Multilingual Offensive Language Identification with Cross-lingual Embeddings (2020.emnlp-main)
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| Challenge: | Several studies investigating methods to detect offensive content in social media use English data. |
| Approach: | They apply cross-lingual contextual embeddings and transfer learning to make predictions in languages with less resources. |
| Outcome: | The proposed method compares favorably to the best systems submitted to recent shared tasks on Bengali, Hindi, and Spanish. |
CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs (2020.emnlp-main)
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| Challenge: | Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. |
| Approach: | They exploit the signals embedded in URLs to label web documents at scale with an average precision of 94.5% across different language pairs. |
| Outcome: | The proposed method can label documents at 94.5% across languages with high precision . the proposed method is useful for low-resource languages with limited resources . |
Perhaps PTLMs Should Go to School – A Task to Assess Open Book and Closed Book QA (2021.emnlp-main)
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| Challenge: | Taking the exam closed book, but having read the textbook, yields at best minor improvement (56%), suggesting that the PTLM may not have “understood” the textbook (or perhaps misundersttoo the questions). |
| Approach: | They propose to use pre-trained language models to answer questions from introductory college textbooks and hundreds of true/false statements based on review questions written by the authors. |
| Outcome: | The proposed task includes two college-level introductory texts in the social sciences (American Government 2e) and humanities (U.S. History). |
FC-Attack: Jailbreaking Multimodal Large Language Models via Auto-Generated Flowcharts (2025.findings-emnlp)
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| Challenge: | Recent research shows that multimodal large language models are vulnerable to jailbreak attacks . |
| Approach: | They propose a jailbreak attack method based on auto-generated flowcharts . the flowchartings are then combined with a benign textual prompt to execute the attack . |
| Outcome: | The proposed method achieves an attack success rate of up to 96% via images and 78% via videos across multiple MLLMs. |
Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network (D19-1)
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| Challenge: | Existing methods to learn user and item representations from review texts do not take into account the user-user and item-item relatedness of the user. |
| Approach: | They propose to use review content and user-item graphs to integrate them as different views. |
| Outcome: | The proposed approach can learn user and item representations from review content and user-item graphs. |
Stereotypes and Smut: The (Mis)representation of Non-cisgender Identities by Text-to-Image Models (2023.findings-acl)
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| Challenge: | Initial studies have pointed to the potential for harm due to predictive bias, reflecting and potentially reinforcing cultural stereotypes. |
| Approach: | They conduct a survey among non-cisgender individuals and interviews to establish which harms affected individuals anticipate, and how they would like to be represented. |
| Outcome: | The results show that certain non-cisgender identities are consistently (mis)represented as less human, more stereotyped and more sexualised. |
Qorǵau: Evaluating Safety in Kazakh-Russian Bilingual Contexts (2025.findings-acl)
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Maiya Goloburda, Nurkhan Laiyk, Diana Turmakhan, Yuxia Wang, Mukhammed Togmanov, Jonibek Mansurov, Askhat Sametov, Nurdaulet Mukhituly, Minghan Wang, Daniil Orel, Zain Muhammad Mujahid, Fajri Koto, Timothy Baldwin, Preslav Nakov
| Challenge: | Large language models (LLMs) have the potential to generate harmful content, posing risks to users. |
| Approach: | They propose a dataset specifically designed for safety evaluation in Kazakh and Russian . they use a bilingual context in Kazakhstan where both Kazakh (a low-resource language) and Russian (a high-resourced language) |
| Outcome: | The proposed dataset is designed for safety evaluation in Kazakh and Russian . it shows that both multilingual and language-specific LLMs perform better than others . |
Can Large Language Model Summarizers Adapt to Diverse Scientific Communication Goals? (2024.findings-acl)
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| Challenge: | Recent work on the evaluation of large language models (LLMs) has shown unprecedented performance on diverse language generation tasks. |
| Approach: | They investigate the controllability of large language models on scientific summarization tasks by controlling stylistic and content coverage factors. |
| Outcome: | The proposed model outperforms humans on the MuP review generation task in terms of similarity to reference summaries and human preferences. |
SA-DETR:Span Aware Detection Transformer for Moment Retrieval (2025.coling-main)
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| Challenge: | Moment Retrieval aims to locate video segments related to text. |
| Approach: | They propose a method that leverages the importance of instance related span anchors . they initialize span anchor using instance related fuse token and supervise them with GT labels . |
| Outcome: | The proposed method achieves competitive results on QVHighlights, Charades-STA and TACoS. |
Learn and Unlearn: Addressing Misinformation in Multilingual LLMs (2025.emnlp-main)
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| Challenge: | Existing methods to unlearning large language models (LLMs) focus on English data, but they ignore multilingual contexts and can produce misleading, offensive, or otherwise fake content. |
| Approach: | They investigate the propagation of information in multilingual large language models and evaluate unlearning methods to address harmful content in multi-lingual contexts. |
| Outcome: | The proposed methods can effectively eliminate harmful content for all languages by addressing both English and the original language of the harmful data. |
DecipherPref: Analyzing Influential Factors in Human Preference Judgments via GPT-4 (2023.emnlp-main)
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| Challenge: | Human preference judgments are important in large language models to produce outputs that align with human values. |
| Approach: | They conduct an in-depth examination of pairwise human judgments released by OpenAI . they find that most favored factors vary across tasks and genres . |
| Outcome: | The proposed model reveals that most favored factors vary across tasks and genres . the findings have implications on the construction of balanced datasets in human preference evaluations - crucial step in shaping behavior of future LLMs. |
A Grounded Typology of Word Classes (2025.naacl-long)
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| Challenge: | Using captioned images, we can quantify language function and semantics using a grounded typology approach . linguistic typology is the study of patterns and variation across the world's languages . |
| Approach: | They propose a grounded typology approach that uses images captioned across languages to quantify meaning and semantics. |
| Outcome: | The proposed approach can quantify language function and semantics using images captioned across languages. |
Bayesian Optimization for Controlled Image Editing via LLMs (2025.findings-acl)
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Chengkun Cai, Haoliang Liu, Xu Zhao, Zhongyu Jiang, Tianfang Zhang, Zongkai Wu, John Lee, Jenq-Neng Hwang, Lei Li
| Challenge: | achieving precise control over generated content and maintaining semantic consistency remain significant limitations, particularly concerning grounding techniques and the necessity for model fine-tuning. |
| Approach: | They propose an off-the-shelf approach that integrates Large Language Models with Bayesian Optimization to facilitate precise and user-friendly image editing. |
| Outcome: | The proposed approach outperforms existing methods in editing accuracy and semantic preservation, as validated using different LLMs including Claude3 and GPT-4. |
Multi-Hop Question Generation via Dual-Perspective Keyword Guidance (2025.findings-acl)
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| Challenge: | Existing work fails to fully utilize the guiding potential of keywords and neglect to differentiate the distinct roles of question-specific and document-specific keywords. |
| Approach: | They propose a dual-perspective keyword-guided framework that integrates question and document keywords into the multi-hop question generation process. |
| Outcome: | The proposed framework integrates question and document keywords into the multi-hop question generation process. |
Substance over Style: Document-Level Targeted Content Transfer (2020.emnlp-main)
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| Challenge: | Existing language models excel at writing from scratch, but many real-world scenarios require rewriting an entire document to fit a set of constraints. |
| Approach: | They propose a document-level targeted content transfer task that addresses the challenge of rewriting an entire document coherently by generating coherent and diverse rewrites that obey a constraint while remaining close to the original document. |
| Outcome: | The proposed model outperforms existing methods by generating coherent and diverse rewrites that obey the constraint while remaining close to the original document. |
Latent Inter-User Difference Modeling for LLM Personalization (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are increasingly integrated into users’ daily lives, leading to a growing demand for personalized outputs. |
| Approach: | They propose a framework that models inter-user differences in the latent space instead of relying on language-based prompts. |
| Outcome: | The proposed framework outperforms baseline methods on personalized review generation. |
SelfRACG: Enabling LLMs to Self-Express and Retrieve for Code Generation (2025.emnlp-main)
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Qian Dong, Jia Chen, Qingyao Ai, Hongning Wang, Haitao Li, null Yiwu, Yao Hu, Yiqun Liu, Shaoping Ma
| Challenge: | Existing retrieval-augmented code generation methods fail to accurately fetch the knowledge required for code generation for consecutive code fragments. |
| Approach: | They propose a paradigm that enables large language models to Self-express their information needs to enhance retrieval-augmented code generation methods. |
| Outcome: | Experiments show that SelfRACG can retrieve external knowledge that better aligns with the LLM’s own information needs, resulting in superior generation performance compared to vanilla RACG. |
Multi-Document Scientific Summarization from a Knowledge Graph-Centric View (2022.coling-1)
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| Challenge: | Multi-Document Scientific Summarization (MDSS) aims to produce concise and concise summaries for clusters of topic-relevant scientific papers. |
| Approach: | They propose a model that incorporates knowledge graphs into paper encoding and decoding processes and propose 'decoder' for generating knowledge graph information of summary in the form of descriptive sentences. |
| Outcome: | The proposed architecture improves on baselines on the Multi-Xscience dataset. |
On the Vulnerability of Safety Alignment in Open-Access LLMs (2024.findings-acl)
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Jingwei Yi, Rui Ye, Qisi Chen, Bin Zhu, Siheng Chen, Defu Lian, Guangzhong Sun, Xing Xie, Fangzhao Wu
| Challenge: | Large language models (LLMs) are susceptible to malicious exploitation, but are often rejected and limited harmfulness is limited. |
| Approach: | They propose two types of reverse alignment techniques: reverse supervised fine-tuning (RSFT) and reverse preference optimization (RPO). |
| Outcome: | The proposed methods can significantly enhance the success rate and harmfulness of jailbreak attacks, but they face high rejection rates and limited harmfulness. |
Characterizing Selective Refusal Bias in Large Language Models (2026.findings-acl)
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| Challenge: | a recent study shows that safety guardrails in large language models can inadvertently introduce or reflect new biases as they may refuse to generate harmful content targeting some demographic groups and not others. |
| Approach: | They examine the selective refusal bias in large language models by examining demographics and responses. |
| Outcome: | The proposed model fails to defend against an indirect attack on previously refused groups in 89% of the trials. |
Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction (2023.acl-long)
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| Challenge: | sarcasm is a form of irony conveying mockery and contempt . social media has become increasingly popular for identifying sarcasm . |
| Approach: | They develop a method to detect sarcasm from social media using augmented potentials. |
| Outcome: | The proposed method outperforms baselines on benchmark datasets. |
Chain-of-Jailbreak Attack for Image Generation Models via Step by Step Editing (2025.findings-acl)
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Wenxuan Wang, Kuiyi Gao, Youliang Yuan, Jen-tse Huang, Qiuzhi Liu, Shuai Wang, Wenxiang Jiao, Zhaopeng Tu
| Challenge: | Text-based image generation models, such as Stable Diffusion and DALL-E 3, hold significant potential in content creation and publishing workflows . however, considerable efforts are being made to prevent the generation of harmful content, such abusive, violent, or pornographic material. |
| Approach: | They propose a chain-of-jailbreak method which decomposes malicious queries into multiple sub-queries and iteratively edits images based on these sub-questions. |
| Outcome: | The proposed method can bypass safeguards of image generation models for over 60% cases, significantly outperforms other jailbreaking methods (14%) |
HateCheckHIn: Evaluating Hindi Hate Speech Detection Models (2022.lrec-1)
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| Challenge: | Hate speech detection models are evaluated on a held-out test data, but they are incapable of identifying weaknesses. |
| Approach: | They propose to use multilingual hate speech detection models to evaluate their performance on social media conversation. |
| Outcome: | The proposed model can detect hate speech in multiple languages using a real-world conversation on social media. |
A Survey on Multimodal Disinformation Detection (2022.coling-1)
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Firoj Alam, Stefano Cresci, Tanmoy Chakraborty, Fabrizio Silvestri, Dimiter Dimitrov, Giovanni Da San Martino, Shaden Shaar, Hamed Firooz, Preslav Nakov
| Challenge: | Recent years have witnessed the proliferation of offensive content online such as fake news, propaganda, misinformation, and disinformation. |
| Approach: | They propose to tackle online multimodal offensive content using different modalities and combinations thereof. |
| Outcome: | The proposed approach combines factuality and harmfulness in a framework that can be used for multiple modalities and combinations of modality. |
COUNT: COntrastive UNlikelihood Text Style Transfer for Text Detoxification (2023.findings-emnlp)
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Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Manasa Bharadwaj, Nikhil Verma, Ali Pesaranghader, Scott Sanner
| Challenge: | Text detoxification is a task to ensure the generation of non-toxic and safe text. |
| Approach: | They propose a novel contrastive unlikelihood objective that combines rephrasing and identity mapping to effectively isolate and focus learning on non-toxic style transfer. |
| Outcome: | The proposed method achieves significant improvements in fluency, content preservation, and detoxification on two parallel datasets. |
Enhancing Language Model Factuality via Activation-Based Confidence Calibration and Guided Decoding (2024.emnlp-main)
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| Challenge: | Existing methods to calibrate language models are limited in inference-time efficiency or fail to provide informative signals. |
| Approach: | They propose an activation-based calibration method, ActCab, which trains a linear layer on top of the LM’s last-layer activations. |
| Outcome: | The proposed method improves on five popular QA benchmarks and reduces the average expected calibration error (ECE) score by up to 39%. |
Leveraging Entity Information for Cross-Modality Correlation Learning: The Entity-Guided Multimodal Summarization (2024.findings-acl)
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| Challenge: | Multimodal Summarization with Multimodal Output (MSMO) is a new approach to produce a multimodal summary that integrates both text and relevant images. |
| Approach: | They propose an Entity-Guided Multimodal Summarization model that integrates both text and relevant images to produce a multimodal summary. |
| Outcome: | The proposed model integrates text-image and entity-image information and refines image selection through knowledge distillation from a pre-trained vision-language model. |
Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification (2025.acl-long)
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Chengwu Liu, Ye Yuan, Yichun Yin, Yan Xu, Xin Xu, Zaoyu Chen, Yasheng Wang, Lifeng Shang, Qun Liu, Ming Zhang
| Challenge: | Chain-of-Thought prompting is a de facto method to elicit reasoning capabilities from large language models (LLMs). |
| Approach: | They propose a step-aware formal verification framework Safe to address hallucinations in CoT prompting . they propose 'formal step' as a benchmark for step correctness theorem proving with 30,809 formal statements. |
| Outcome: | The proposed framework shows significant performance improvement while offering interpretable and verifiable evidence. |
Large Language Models Are Natural Video Popularity Predictors (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) can better capture cultural and social factors such as viewing intensity and geographic spread of video content. |
| Approach: | They propose to use Large Language Models to capture cultural and social factors that influence video popularity and generate interpretable, attribute-based explanations. |
| Outcome: | The proposed model captures both engagement intensity and geographic spread on 13,639 popular videos, while the neural network's predictions reach 82% without fine-tuning. |
DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM (2023.findings-emnlp)
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| Challenge: | Neural Topic Models and Large Language Models (LLMs) primarily use contextual embeddings from LLMs, which are not optimal for clustering or topic generation. |
| Approach: | They propose a framework that leverages Encoder-Decoders to generate highly clusterable embeddings that could generate topics that exhibit enhanced clusterability and enhanced semantic coherence compared to existing methods. |
| Outcome: | The proposed framework is efficient to train and exhibits high adaptability, demonstrating its potential for a wide array of applications. |
LEGO: A Multi-agent Collaborative Framework with Role-playing and Iterative Feedback for Causality Explanation Generation (2023.findings-emnlp)
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| Challenge: | Causality explanation generation is a generative task that aims to explain why a given cause-effect pair is true using natural language. |
| Approach: | They propose a multi-agent framework with role-playing and iterative feedback for causality explanation generation. |
| Outcome: | The proposed framework is superior to existing frameworks on WIKIWHY and e-CARE datasets. |
TeSum: Human-Generated Abstractive Summarization Corpus for Telugu (2022.lrec-1)
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| Challenge: | a number of recent datasets for summarisation, scraped the web-content relying on the assumption that summary is made available with the article by the publishers. |
| Approach: | They propose a pipeline that crowd-sources summarization data and then aggressively filters the content via: automatic and partial expert evaluation. |
| Outcome: | The proposed pipeline can be applied to scraped datasets to extract better quality articles-summaries pairs. |
SPORTSINTERVIEW: A Large-Scale Sports Interview Benchmark for Entity-centric Dialogues (2022.lrec-1)
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| Challenge: | Existing knowledge grounded dialogue datasets only contain external knowledge from one dimension, which limits the diversity of knowledge sources and may contain unwanted bias. |
| Approach: | They propose to use two types of external knowledge sources as knowledge grounding in an interview dataset to model human dialogues. |
| Outcome: | The proposed dataset contains 150K interviews and 34K interviewees . it is larger in size and has more than one dimension of external knowledge linking . however, the performance of the proposed models is far from humans . |
Offensive Language Identification in Greek (2020.lrec-1)
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| Challenge: | a gap in the literature on offensive language has been addressed with studies on Spanish, Hindi, and German. |
| Approach: | They present a Greek annotated dataset for offensive language identification . it contains 4,779 tweets annotating offensive and not offensive posts from Twitter . they evaluate several computational models trained and tested on the dataset . |
| Outcome: | The proposed dataset contains 4,779 tweets annotated as offensive and not offensive . the authors show that the proposed dataset is similar to the OLID dataset for English . |
Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective (2024.acl-long)
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| Challenge: | Extensive research has shed light on the origins of multimodal hallucinations, including the inability of vision encoders to represent finegrained visual details and model reliance on inherent parametric knowledge such as language priors and statistical biases. |
| Approach: | They propose to use EOS to terminate generation of large multimodal models by comparing the generated text with the image to mitigate multimodal hallucinations. |
| Outcome: | The proposed method significantly improves the hallucination performance of Large Multimodal Models without additional data or knowledge. |
Few-Shot Multimodal Named Entity Recognition Based on Mutlimodal Causal Intervention Graph (2024.lrec-main)
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| Challenge: | Existing methods for multimodal named entity recognition are limited due to limited resources. |
| Approach: | They propose a Few-shot Multimodal Named Entity Recognition task to address these relation types by constructing a multimodal graph and a new multimodal causal intervention strategy. |
| Outcome: | The proposed model improves on two multimodal named entity recognition datasets. |
Pointwise Mutual Information Based Metric and Decoding Strategy for Faithful Generation in Document Grounded Dialogs (2023.emnlp-main)
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| Challenge: | Existing metrics for faithfulness of response are not aligned with human judgments. |
| Approach: | They propose a new metric that utilizes (Conditional) Point-wise Mutual Information (PMI) between the generated response and the source document, conditioned on the dialogue. |
| Outcome: | The proposed metric improves on BEGIN benchmarks and shows that it generates more faithful responses than standard decoding techniques. |
doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset (2020.emnlp-main)
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| Challenge: | doc2dial dataset is a goal-oriented document-grounded dialogue model . it is based on how the authors compose documents for guiding end users . |
| Approach: | They propose a dataset of goal-oriented dialogues grounded in documents . they use annotated conversations with an average of 14 turns to generate conversational utterances . |
| Outcome: | The proposed dataset includes over 4500 annotated conversations with an average of 14 turns grounded in over 450 documents from four domains. |
ToxVidLM: A Multimodal Framework for Toxicity Detection in Code-Mixed Videos (2024.findings-acl)
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| Challenge: | Using a dataset of 931 videos with 4021 code-mixed Hindi-English utterances, we find that video content with multiple modalities is more accurate and more accurate than textual content. |
| Approach: | They propose to use a dataset to analyze toxic content in video content in non-English languages by leveraging language models. |
| Outcome: | The proposed framework achieves an Accuracy and Weighted F1 score of 94.29% and 94.35% for the first time in its class. |
In Plain Sight: Media Bias Through the Lens of Factual Reporting (D19-1)
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| Challenge: | lexical bias stems from content realization, or how things are said, but other forms of bias stem from content selection and organization. |
| Approach: | They use a dataset to analyze news articles annotated with 1,727 bias spans to investigate informational bias. |
| Outcome: | The proposed model shows that informational bias appears more frequently than lexical bias. |
PsyScam: A Benchmark for Psychological Techniques in Real-World Scams (2025.findings-emnlp)
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| Challenge: | PTs are employed by scammers to manipulate victims and cause lasting psychological trauma. |
| Approach: | They propose a benchmark to capture the PTs employed in real-worldscam reports and investigate how LLMs can be utilized to generate variants of scams based on the pts and the contexts provided by thesescams. |
| Outcome: | The proposed model can generate variants of scams based on the PTs employed in real-world scam reports and the contexts provided by these scams. |
German Also Hallucinates! Inconsistency Detection in News Summaries with the Absinth Dataset (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have made significant progress on a wide range of natural language processing tasks, but they still suffer from hallucinating information in their output. |
| Approach: | They propose to use an annotated dataset to detect hallucinations in german news summarization and open-source it to foster further research on hallucinosity detection in german. |
| Outcome: | The proposed model can detect hallucinations in the output and evaluate the faithfulness of the summaries. |
Simulating Crisis Cognition: A Computational Framework for Hypothesis Generation in Crisis Communication (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable fidelity in simulating social dynamics, yet using them to inform high-stakes crisis policy requires rigorous causal evaluation. |
| Approach: | They propose a framework that functions as an in-silico hypothesis generator to evaluate communication strategies by coupling real-world telemetry with 1,813 agents. |
| Outcome: | The proposed framework provides a rigorous testbed for evaluating strategies before human-subject trials. |
WordNet-Shp: Towards the Building of a Lexical Database for a Peruvian Minority Language (L18-1)
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| Challenge: | WordNet-like resources are lexical databases with highly relevance information and data that could be exploited in more complex computational linguistics research and applications. |
| Approach: | They propose to build a WordNet database for a low-resourced and indigenous language in Peru . they propose to use word2vec similarity to compare definition glosses in a dictionary with the content of a Spanish WordNet . |
| Outcome: | The proposed database is based on a bilingual dictionary written in Spanish and an automatic evaluation process using a manually annotated Gold Standard in Shipibo-Koniba. |
G2: Guided Generation for Enhanced Output Diversity in LLMs (2025.emnlp-main)
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| Challenge: | Existing approaches to enhance output diversity but compromise quality of outputs. |
| Approach: | They propose a training-free plug-and-play method that enhances output diversity while preserving generation quality. |
| Outcome: | The proposed method enhances output diversity while maintaining an optimal balance between diversity and quality. |
Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive (2023.emnlp-main)
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Tharindu Weerasooriya, Sujan Dutta, Tharindu Ranasinghe, Marcos Zampieri, Christopher Homan, Ashiqur KhudaBukhsh
| Challenge: | a paper examines how machine and human moderators disagree on offensive speech . offensive speech detection is a key component of content moderation . |
| Approach: | They propose a large-scale noise audit and a vicarious offense dataset to investigate disagreement on social web political discourse. |
| Outcome: | The proposed dataset reveals that moderation outcomes vary wildly across different machine moderators. |
Learning Personalized Alignment for Evaluating Open-ended Text Generation (2024.emnlp-main)
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| Challenge: | Traditional evaluation metrics rely heavily on lexical similarity with human-written references, showing poor correlation with human judgments and failing to account for alignment with the diversity of human preferences. |
| Approach: | They propose an interpretable evaluation framework that evaluates alignment with specific human preferences by providing detailed comments and fine-grained scoring. |
| Outcome: | The proposed framework outperforms GPT-4 in Kendall correlation and accuracy with zero-shot reviewers. |
The Mirage of Model Editing: Revisiting Evaluation in the Wild (2025.acl-long)
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| Challenge: | despite near-perfect results, effectiveness of model editing in real-world applications remains unclear. |
| Approach: | They propose QAEdit and WILD to better reflect real-world use of model editing . they propose a benchmark aligned with widely used question answering datasets and a task-agnostic evaluation framework . |
| Outcome: | The proposed QAEdit benchmark and WILD evaluation framework show that current models perform worse than previously reported. |
TheRuSLan: Database of Russian Sign Language (2020.lrec-1)
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| Challenge: | The database is the first of a kind for Russian sign language and is intended for use in machine learning, gesture recognition and sign language linguistics. |
| Approach: | They present a Russian sign language multimedia database called TheRuSLan . the database includes lexical units from Russian sign languages within one subject area . |
| Outcome: | The proposed database includes lexical units from Russian sign language within one subject area. |
Demonstrations Are All You Need: Advancing Offensive Content Paraphrasing using In-Context Learning (2024.findings-acl)
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| Challenge: | Paraphrasing of offensive content is a better alternative to content removal, but supervised methods often retain a large portion of the offensiveness of the original content. |
| Approach: | They propose to use In-Context Learning (ICL) to generate usable offensive paraphrases by using large language models. |
| Outcome: | The proposed framework is better than supervised methods on human evaluation and lower toxicity by 76%. |
Do Children Texts Hold The Key To Commonsense Knowledge? (2022.emnlp-main)
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| Challenge: | Existing approaches to compiling commonsense knowledge (CSK) struggle with reporting bias, i.e., frequency in text sources is not a good proxy for relevance or truth. |
| Approach: | They propose that children's texts make fewer assumptions on the reader's knowledge and therefore spell out commonsense more explicitly. |
| Outcome: | The proposed approach can be leveraged in language-model-based commonsense knowledge extraction settings, where task-unspecific fine-tuning on small amounts of children texts yields significant improvements. |
Improving Faithfulness of Large Language Models in Summarization via Sliding Generation and Self-Consistency (2024.lrec-main)
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| Challenge: | Abstractive summarization models (LLMs) have demonstrated impressive performance in various tasks, but they are still suffering from factual inconsistency problem called hallucination. |
| Approach: | They propose to improve the faithfulness of large language models by impelling them to process the entire article more fairly and faithfully. |
| Outcome: | The proposed strategy improves the faithfulness of large language models in summarization while maintaining their fluency and informativeness. |
CURE: Controlled Unlearning for Robust Embeddings — Mitigating Conceptual Shortcuts in Pre-Trained Language Models (2025.findings-emnlp)
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| Challenge: | Pre-trained language models are susceptible to spurious, concept-driven correlations that impair robustness and fairness. |
| Approach: | They propose a framework that disentangles and suppresses conceptual shortcuts while preserving essential content information. |
| Outcome: | The proposed framework improves on IMDB and Yelp datasets with minimal computational overhead. |
Human-Inspired Obfuscation for Model Unlearning: Local and Global Strategies with Hyperbolic Representations (2025.findings-emnlp)
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| Challenge: | Existing methods for unlearning large language models struggle to balance effective forgetting with maintaining model utility. |
| Approach: | They propose a human-inspired unlearning framework that simulates forgetting on fuzzy data and represents them in hyperbolic and Euclidean spaces. |
| Outcome: | The proposed framework is able to forget sensitive content while maintaining the model’s language understanding, fluency, and benchmark performance. |
Multiview Clickbait Detection via Jointly Modeling Subjective and Objective Preference (2023.findings-emnlp)
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| Challenge: | Existing clickbait detection models rely on analyzing the objective semantics of posts or correlating posts with article content only, but fail to identify and exploit the manipulation intention of clickbaiting from a user’s subjective perspective. |
| Approach: | They propose a multiview clickbait detection model to model subjective and objective preferences simultaneously to capture clickbaiting from a user's subjective perspective. |
| Outcome: | The proposed model outperforms state-of-the-art models on two real-world datasets and shows that it integrates subjective and objective preferences simultaneously. |
Reconstruct Before Summarize: An Efficient Two-Step Framework for Condensing and Summarizing Meeting Transcripts (2023.emnlp-main)
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| Challenge: | Existing approaches to meeting summarization are limited due to noise, lengthy transcripts, and scattered salient information. |
| Approach: | They propose a two-step framework for meeting summarization that leverages a self-supervised paradigm to reconstruct transcripts and a relative positional bucketing algorithm to equip models to generate the summary. |
| Outcome: | The proposed method significantly reduces memory consumption and processing time on two meeting summarization datasets. |
How Do Multilingual Language Models Remember Facts? (2025.findings-acl)
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| Challenge: | Prior research has focused on English monolingual models, but how these mechanisms generalize to non-English languages remains unexplored. |
| Approach: | They analyze three multilingual LLMs to find out how they can generalize recall mechanisms . they find that subject enrichment is language-independent, object extraction is language dependent . |
| Outcome: | The proposed model performs better in multilingual contexts than in English models . the model is more efficient in multi-lingual context, but it is more complex in multilinguistic models compared to English models. |
Tox-BART: Leveraging Toxicity Attributes for Explanation Generation of Implicit Hate Speech (2024.findings-acl)
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| Challenge: | Existing language models to generate implicit hate explanations are lacking in many fields. |
| Approach: | They propose to use language models to generate explicit hate posts to make it clear . they find that simpler models incorporating external toxicity signals outperform KG-infused models . |
| Outcome: | The proposed setup produces more precise explanations than zero-shot GPT-3.5, highlighting the intricate nature of the task. |
Can’t See the Forest for the Trees: Benchmarking Multimodal Safety Awareness for Multimodal LLMs (2025.acl-long)
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Wenxuan Wang, Xiaoyuan Liu, Kuiyi Gao, Jen-tse Huang, Youliang Yuan, Pinjia He, Shuai Wang, Zhaopeng Tu
| Challenge: | Multimodal Large Language Models (MLLMs) have expanded the capabilities of traditional language models by enabling interaction through both text and images. |
| Approach: | They propose a multimodal safety awareness benchmark to evaluate MLLMs across 29 safety scenarios with 1,500 carefully curated image-prompt pairs. |
| Outcome: | The proposed model is able to identify unsafe content and avoid over-sensitivity that can hinder helpfulness. |
Language Models in Dialogue: Conversational Maxims for Human-AI Interactions (2024.findings-emnlp)
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Erik Miehling, Manish Nagireddy, Prasanna Sattigeri, Elizabeth Daly, David Piorkowski, John Richards
| Challenge: | Modern language models exhibit some inherent shortcomings, particularly in conversational settings. |
| Approach: | They propose a set of maxims for describing effective human-AI conversation that include quantity, quality, relevance, manner, benevolence, and transparency. |
| Outcome: | The proposed maxims are applied to human-AI interactions and are based on extensive research from the social science and AI communities. |
DisCo_Speech: Controllable Zero-Shot Speech Generation with A Disentangled Speech Codec (2026.acl-long)
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Tao Li, Wenshuo Ge, Zhichao Wang, Zihao Cui, Yong Ma, Yingying Gao, Chao Deng, Shilei Zhang, Junlan Feng
| Challenge: | DisCo-Speech is a zero-shot controllable text-to-speech framework . standard codecs entangle timbre and prosody, which hinders independent control in continuation-based LMs. |
| Approach: | They propose a disentangled speech codec and an LM-based generator to solve this problem . they propose fusion and reconstruction that merges content and prosody into unified tokens . |
| Outcome: | DisCo-Speech achieves competitive voice cloning and superior zero-shot prosody control. |
Media Source Matters More Than Content: Unveiling Political Bias in LLM-Generated Citations (2025.emnlp-main)
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| Challenge: | generative search engines rely on in-line citations as the key gateway to original webpages . a recent study shows that LLMs tend to cite left-leaning sources at higher rates compared to traditional retrieval systems . |
| Approach: | They construct a dataset of news articles labeled with left- or right-leaning stances . they find that LLMs tend to cite left-leansing sources at higher rates than traditional retrieval systems . |
| Outcome: | The proposed dataset shows that LLMs tend to cite left-leaning sources at higher rates than traditional retrieval systems. |
Mixture of Ordered Scoring Experts for Cross-prompt Essay Trait Scoring (2025.acl-long)
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| Challenge: | Existing approaches to automate essay scoring overlook critical information, authors say . evaluators often limit their performance to unseen topics, resulting in incomplete assessment perspectives. |
| Approach: | They propose a framework that integrates information from prompts and essays into an AES framework. |
| Outcome: | The proposed framework achieves state-of-the-art in cross-prompt scoring and multi-trait scoring on the ASAP++ dataset. |
Distract Large Language Models for Automatic Jailbreak Attack (2024.emnlp-main)
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| Challenge: | Commercial large language models (LLMs) have made great progress in various NLP tasks. |
| Approach: | They propose a black-box jailbreak framework for automated red teaming of Large language models using an iterative optimization algorithm to conceal malicious content and memory reframing. |
| Outcome: | The proposed framework outperforms existing jailbreak defense methods and highlights the need to develop more effective and practical defense strategies. |
RARR: Researching and Revising What Language Models Say, Using Language Models (2023.acl-long)
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Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, Kelvin Guu
| Challenge: | Language models (LMs) excel at many tasks but often produce unsupported or misleading content. |
| Approach: | They propose a system that finds attribution for any text generation model and post-edits it to fix unsupported content. |
| Outcome: | The proposed system improves attribution while preserving the original output. |
Unveiling the Essence of Poetry: Introducing a Comprehensive Dataset and Benchmark for Poem Summarization (2023.emnlp-main)
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| Challenge: | Summarization of poetry is a challenging task as it can be easily lost if only the literal meaning is considered. |
| Approach: | They propose to use poetry as a model to summarize poetry and provide a dataset to evaluate their creative language interpretation capacity. |
| Outcome: | The proposed dataset consisting of 3011 samples and its corresponding summarized interpretation in the English language provides an opportunity to evaluate the creative language interpretation capacity of the proposed models. |
PunMemeCN: A Benchmark to Explore Vision-Language Models’ Understanding of Chinese Pun Memes (2025.emnlp-main)
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| Challenge: | Pun memes combine wordplay with visual elements to create humor, irony, or other rhetorical effects. |
| Approach: | They propose a benchmark to assess Chinese pun memes' processing capabilities across three progressive tasks: pun meme detection, sentiment analysis, and chat-driven meme response. |
| Outcome: | The proposed model can detect pun memes, analyze sentiments, and respond to chats, while ignoring homophone wordplay. |
Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements (2024.findings-acl)
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| Challenge: | Existing LLMs lack sufficient controllability to generate statements supporting diverse or even controversial perspectives. |
| Approach: | They develop a pipeline that fine tunes LLMs to generate statements generated via debate. |
| Outcome: | The proposed pipeline improves the controllability of LLMs in generating statements supporting an argument the user defined in the prompt. |
MiRAGeNews: Multimodal Realistic AI-Generated News Detection (2024.findings-emnlp)
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| Challenge: | inflammatory “fake” news content is increasingly common, but it is also difficult to detect by humans. |
| Approach: | They propose a dataset of 12,500 high-quality real and AI-generated image-caption pairs from state-of-the-art generators to combat the spread of fake news. |
| Outcome: | The proposed dataset improves on image-caption pairs from out-of-domain image generators and news publishers. |
MccSTN: Multi-Scale Contrast and Fine-Grained Feature Fusion Networks for Subject-driven Style Transfer (2024.lrec-main)
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| Challenge: | Stylistic style transfer is an important part of the image processing field . due to the low semantic similarity between the original image and the style image, many fine-grained style features are discarded. |
| Approach: | They propose a new style representation and transfer framework that can be adapted to existing image style transfers. |
| Outcome: | The proposed framework can be adapted to existing image style transfers. |
The Lies Characters Tell: Utilizing Large Language Models to Normalize Adversarial Unicode Perturbations (2025.findings-acl)
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| Challenge: | Homoglyphs are visually homogeneous to Latin letters and are used to mask offensive content. |
| Approach: | They propose two methods to normalize homoglyphs by replacing non-Latin characters with a delimiter and using large language models to determine which characters should be replaced with Latin letters. |
| Outcome: | The proposed methods normalize homoglyphs by replacing non-Latin characters with a delimiter and prompting large language models to "fill in the blanks" the authors found that the proposed methods produced normalized text with an average cosine similarity score of 0.91 to the original tweets and 0.96 to the tweets using the direct method. |
USB: A COMPREHENSIVE AND UNIFIED SAFETY EVALUATION BENCHMARK FOR MULTIMODAL LARGE LANGUAGE MODELS (2026.acl-long)
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Baolin Zheng, Guanlin Chen, Qingyang Teng, Hongqiong Zhong, Yingshui Tan, Zhendong Liu, Weixun Wang, Jiaheng Liu, Jian Yang, Huiyun Jing, Jincheng Wei, Wenbo Su, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang
| Challenge: | Existing safety benchmarks fail to provide reliable assessments due to limited risk coverage, insufficient scale and the oversight of complex modality combinations. |
| Approach: | They propose a framework that covers 61 risk categories across four modality interactions to address this gap. |
| Outcome: | The proposed framework covers 61 risk categories across four distinct modality interactions. |
Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training (2026.acl-long)
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| Challenge: | Large language models (LLMs) have impressive capabilities across a wide range of domains, but their generalpurpose pre-training objectives often leave them illsuited for specialized applications such as healthcare. |
| Approach: | They propose a perplexity-aware data scaling law that establishes a predictive relationship between the perplexities of domain-specific data and the test loss. |
| Outcome: | Experiments on medical and general-domain benchmarks show that the proposed scaling law consistently identifies near-optimal training subsets with significantly reduced data consumption. |
Infogen: Generating Complex Statistical Infographics from Documents (2025.acl-long)
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| Challenge: | Existing efforts to generate simple charts have focused on generating simple infographics from text-heavy documents. |
| Approach: | They propose to generate statistical infographics composed of multiple sub-charts that are contextually accurate, insightful, and visually aligned. |
| Outcome: | The proposed framework outperforms both open-source and closed LLMs in text-to-statistical infographic generation. |
Exploring Context-Aware Evaluation Metrics for Machine Translation (2023.findings-emnlp)
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| Challenge: | Existing studies on machine translation evaluation focused on quality of individual sentences, while neglecting the importance of contextual information. |
| Approach: | They propose a context-aware machine translation evaluation metric called Cont-COMET . they use the COMET framework to consider the preceding and subsequent contexts of the sentence . |
| Outcome: | The proposed metric improves system-level and segment-level evaluations on the official WMT framework. |
Learning Subjective Label Distributions via Sociocultural Descriptors (2025.emnlp-main)
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| Challenge: | Conventional approaches aggregate annotator judgements (labels), ignoring minority perspectives, and overlooking the influence of sociocultural context behind such annotations. |
| Approach: | They propose a framework where subjectivity in binary labels is modeled as an empirical distribution accounting for the variation in annotators through human values extracted from sociocultural descriptors using a language model. |
| Outcome: | The proposed model yields well-calibrated toxicity distribution predictions across binary toxicity labels, which are further used for majority label prediction across cultural subgroups. |
Tales of Morality: Comparing Human- and LLM-Generated Moral Stories from Visual Cues (2025.findings-emnlp)
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| Challenge: | a recent study has found that stories are central to how humans communicate moral values . |
| Approach: | They compare human- and LLM-generated moral narratives based on images annotated by humans for moral content . authors propose a framework for evaluating moral storytelling in vision-language models . |
| Outcome: | The proposed model compared human- and LLM-generated narratives on images . human stories reflect a balanced distribution of moral foundations and coherent narrative arcs, but LLMs emphasize Care foundation and lack emotional resolution. |
Decoding the Rule Book: Extracting Hidden Moderation Criteria from Reddit Communities (2025.emnlp-main)
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| Challenge: | Existing methods to train classifiers that predict norm violations are often opacity-prone . a new approach to identify and extract these implicit criteria from historical moderation data is proposed . |
| Approach: | They propose to extract implicit criteria from historical moderation data using an interpretable architecture. |
| Outcome: | The proposed model replicates neural moderation models while providing transparent insights into decision-making processes. |
ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are limited by knowledge cutoff and can generate factual hallucinations when handling time-sensitive news. |
| Approach: | They propose a two-stage zero-shot fake news detection framework that uses a hierarchical salience and saliency-calibrated minimum margin of relevance algorithm to extract core entities accurately. |
| Outcome: | The proposed framework outperforms existing zero-shot baselines and even most few-shot methods on two public datasets. |
Decoupling the Effect of Chain-of-Thought Reasoning: A Human Label Variation Perspective (2026.findings-acl)
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| Challenge: | Reasoning-tuned large language models (LLMs) with long Chain-of-Thought excel at single-answer tasks, yet their ability to model Human Label Variation remains underexplored. |
| Approach: | They conduct systematic disentanglement experiments to isolate the effect of reasoning text from intrinsic model priors on distribution-based tasks. |
| Outcome: | The proposed model improves distributional alignment, but distributional ranking is governed by model priors. |
Unsupervised Hallucination Detection by Inspecting Reasoning Processes (2025.emnlp-main)
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| Challenge: | Unsupervised hallucination detection aims to identify hallucines generated by large language models without relying on labeled data. |
| Approach: | They propose an unsupervised method to detect hallucinated content by large language models . they use internal representations intrinsic to factual correctness to prompt the model to verify the truthfulness of a given statement . |
| Outcome: | The proposed framework outperforms existing unsupervised methods and is fully unsupervised and low cost. |
Unveiling Multi-level and Multi-modal Semantic Representations in the Human Brain using Large Language Models (2024.emnlp-main)
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Yuko Nakagi, Takuya Matsuyama, Naoko Koide-Majima, Hiroto Yamaguchi, Rieko Kubo, Shinji Nishimoto, Yu Takagi
| Challenge: | Recent studies have assessed different levels of semantic content, such as speech, objects, and stories, separately. |
| Approach: | They used functional magnetic resonance imaging to record brain activity while watching 8.3 hours of dramas and movies. |
| Outcome: | The findings show that LLMs predict human brain activity more accurately than traditional language models, particularly for complex background stories. |
BiasWipe: Mitigating Unintended Bias in Text Classifiers through Model Interpretability (2024.emnlp-main)
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| Challenge: | Existing methods to mitigate unintended bias in social media platforms are re-training and adding extra parameters to the model. |
| Approach: | They propose a technique to mitigate unintended bias in language models by pruning the neuron weights responsible for univ bias. |
| Outcome: | The proposed technique achieves fairness by pruning the neuron weights responsible for unintended bias without loss of original performance. |
Quantifying the Impact of Disfluency on Spoken Content Summarization (2024.lrec-main)
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| Challenge: | a recent study has found that disfluencies negatively impact spoken content summarization . |
| Approach: | They aim to quantify the impact of disfluency on spoken content summarization . they also investigate two methods towards improving summarizing in the presence of disflouencies . |
| Outcome: | The proposed methods improve summarization quality in the presence of disfluencies. |
Enhancing Retrieval-Augmented Generation via Evidence Tree Search (2025.acl-long)
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| Challenge: | Evidence retrieval is used to enhance Large Language Models (LLMs) but in real-world applications, it often returns lengthy documents with redundant or irrelevant content, confusing downstream readers. |
| Approach: | They propose a framework that reformulates evidence retrieval as a dynamic tree expansion process. |
| Outcome: | The proposed framework outperforms existing methods on five datasets. |
CrossGuard: Safeguarding MLLMs against Joint-Modal Implicit Malicious Attacks (2026.acl-long)
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| Challenge: | Existing methods for MLLMs are weak on explicit attacks, but weak on implicit ones. |
| Approach: | They propose an automated red-teaming pipeline that leverages reinforcement learning with tailored reward modules to generate diverse implicit samples across 14 domains. |
| Outcome: | The proposed method outperforms existing methods in implicit and explicit attacks while maintaining high utility. |
Personalized Generation In Large Model Era: A Survey (2025.acl-long)
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Yiyan Xu, Jinghao Zhang, Alireza Salemi, Xinting Hu, Wenjie Wang, Fuli Feng, Hamed Zamani, Xiangnan He, Tat-Seng Chua
| Challenge: | Recent advances in large generative models have catalyzed a paradigm shift in content generation to Personalized Generation (PGen). |
| Approach: | They propose a multi-level taxonomy that systematically formalizes PGen's key components, core objectives, and abstract workflows. |
| Outcome: | The proposed taxonomy bridging PGen research across multiple modalities highlights open challenges and promising directions for future exploration. |
PCoT: Persuasion-Augmented Chain of Thought for Detecting Fake News and Social Media Disinformation (2025.acl-long)
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| Challenge: | Psychological studies have shown that infusing persuasion knowledge enhances disinformation detection. |
| Approach: | They introduce a persuasion-augmented chain of thought approach that leverages persulasion to improve disinformation detection in zero-shot classification. |
| Outcome: | The proposed approach outperforms competitive methods by 15% on online news and social media posts. |
Mapping the Podcast Ecosystem with the Structured Podcast Research Corpus (2025.acl-long)
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| Challenge: | a dataset of over 1.1M podcast transcripts is largely comprehensive of all English language podcasts available through public RSS feeds from May and June of 2020. |
| Approach: | They propose to build a large-scale open dataset of podcast transcripts that includes metadata, speaker roles, audio features and speaker turns for a subset of 370K episodes. |
| Outcome: | The proposed dataset is largely comprehensive of all English language podcasts available through public RSS feeds from May and June of 2020. |
EVA: Evolving Semantic Adversaries for Red-Teaming GUI Agents Against Environmental Injection Attacks (2026.findings-acl)
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Yijie Lu, Manman Zhao, Tianjie Ju, Zihe Yan, Xinbei Ma, Yuan Guo, Daizong Ding, Gongshen Liu, Zhuosheng Zhang
| Challenge: | Existing methods for red-teaming face a trade-off between requiring target-specific knowledge and incurring prohibitive computational costs. |
| Approach: | They propose a framework that evolves payloads exclusively on the semantic dimension via a discovery-deployment pipeline. |
| Outcome: | Experiments show that EVA outperforms baselines in terms of attack success rate while evolving benign seeds into successful attacks within 1.18 to 1.71 iterations. |
OmniCharacter: Towards Immersive Role-Playing Agents with Seamless Speech-Language Personality Interaction (2025.acl-long)
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Haonan Zhang, Run Luo, Xiong Liu, Yuchuan Wu, Ting-En Lin, Pengpeng Zeng, Qiang Qu, Feiteng Fang, Min Yang, Lianli Gao, Jingkuan Song, Fei Huang, Yongbin Li
| Challenge: | Existing methods focus on replicating dialogues in textual form, neglecting the role’s voice traits as a crucial effect in interaction, which tends to be more immersive experiences in realistic scenarios. |
| Approach: | They propose a first seamless speech-language personality interaction model to achieve immersive RPAs with low latency. |
| Outcome: | The proposed model exhibits role-specific personality traits and vocal traits throughout the interaction, enabling a mixture of speech and language responses. |
D2CS - Documents Graph Clustering using LLM supervision (2025.findings-emnlp)
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| Challenge: | Document clustering does not inherently ensure thematic consistency. |
| Approach: | They propose a framework that constructs a similarity graph over document embeddings and applies iterative graph-based clustering algorithms to partition the corpus into initial clusters. |
| Outcome: | The proposed framework constructs a similarity graph over document embeddings and applies iterative graph-based clustering algorithms to partition the corpus into initial clusters. |
The RAG Paradox: A Black-Box Attack Exploiting Unintentional Vulnerabilities in Retrieval-Augmented Generation Systems (2025.findings-emnlp)
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| Challenge: | Existing approaches to degrade retrieval-augmented generation systems rely on unrealistic assumptions in which external attackers have access to internal components such as the retriever. |
| Approach: | They propose a black-box attack based on the RAG paradox, a structural vulnerability that emerges from the system’s effort to enhance trust by revealing both the retrieved documents and their sources to users. |
| Outcome: | The proposed method significantly degrades system performance without internal access while generating natural-looking poisoned documents. |
Harmful Prompt Laundering: Jailbreaking LLMs with Abductive Styles and Symbolic Encoding (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities but their misuse for harmful purposes remains a concern. |
| Approach: | They propose a jailbreaking technique that exploits weaknesses in LLMs' architecture . they propose abductive framing and symbolic encoding to bypass safeguards . |
| Outcome: | The proposed technique achieves over 95% attack success rate on GPT-series models and 70% across all targets. |
SOBR: A Corpus for Stylometry, Obfuscation, and Bias on Reddit (2024.lrec-main)
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| Challenge: | Existing corpora are limited in scope and can be used to collect data on author attributes. |
| Approach: | They propose to use subreddits, flairs, and self-reports as distant labels for author attributes (age, gender, nationality, personality, and political leaning) . |
| Outcome: | The proposed method could be used to infer author attributes from public posts despite their discreetness and anonymity . |
From Lists to Emojis: How Format Bias Affects Model Alignment (2025.acl-long)
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| Challenge: | Format biases in reinforcement learning from human feedback are underexplored . despite its effectiveness, RLHF faces challenges, including policy and regulatory constraints . |
| Approach: | They extend the study of preference biases beyond verbosity bias to a wider range of format biase . they show that with a small amount of biased data, they can inject significant bias into the reward model . |
| Outcome: | The proposed approach can be easily exploited by large language models to achieve higher rankings on popular benchmarks like AlpacaEval and LMSYS Chatbot Arena. |
Human Bias in the Face of AI: Examining Human Judgment Against Text Labeled as AI Generated (2025.findings-acl)
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| Challenge: | Prior research on AI mistrust focused primarily on AI's bias towards different human pop-ups. |
| Approach: | They examine how bias shapes the perception of AI versus human generated content . they found that raters favored content labeled "Human Generated" even when labels were deliberately swapped . |
| Outcome: | The findings highlight the limitations of human judgment in interacting with AI and offer a foundation for improving human-AI collaboration. |
Modal Dependency Parsing as Structured Prediction over Source-Cue Scope (2026.acl-long)
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| Challenge: | Existing work on identifying sources only focuses on defining source-introducing cues . a structured model focuses learning at the source-cue level and constrains event-level decisions to a small, scope-defined candidate set. |
| Approach: | They propose a framework that leverages large language models to explicitly identify source-cue pairs and their respective scope to define modal contexts. |
| Outcome: | The proposed framework surpasses state-of-the-art results by 3 and 4% for English and Chinese datasets. |
RAC: Efficient LLM Factuality Correction with Retrieval Augmentation (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) exhibit impressive results across a wide range of tasks, yet they can often produce factually incorrect outputs. |
| Approach: | They propose a low-latency post-correction method that decomposes the LLM’s output into atomic facts and applies a fine-grained verification and correction process with retrieved content to verify and correct the Llm-generated output. |
| Outcome: | The proposed method has greatly reduced latency and token consumption up to 7x compared to previous state-of-the-art methods with similar or better performance. |
Summarizing Speech: A Comprehensive Survey (2025.emnlp-main)
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Fabian Retkowski, Maike Züfle, Andreas Sudmann, Dinah Pfau, Shinji Watanabe, Jan Niehues, Alexander Waibel
| Challenge: | Podcasts and other audiovisual content are becoming more and more a part of everyday communication and the digital age is changing from text to voice. |
| Approach: | They synthesize the current state of the field and highlight the need for realistic evaluation benchmarks and multilingual datasets. |
| Outcome: | The proposed frameworks are based on evaluation protocols and datasets and highlight the need for realistic benchmarks and multilingual datasets. |
Principled Content Selection to Generate Diverse and Personalized Multi-Document Summaries (2025.acl-long)
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| Challenge: | Large language models exhibit the _”lost in the middle” phenomenon when they are unevenly attending to different parts of the provided context. |
| Approach: | They propose principled content selection as a way to increase source coverage . they use determinantal point processes to prioritize diverse content . |
| Outcome: | The proposed method improves source coverage on the DiverseSumm benchmark. |
Fairness Evaluation and Inference Level Mitigation in LLMs (2026.findings-acl)
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| Challenge: | Large language models display undesirable behaviors embedded in their internal representations, undermining fairness, inconsistency drift, and the propagation of unwanted patterns during extended dialogues. |
| Approach: | They propose a pruning-based framework that detects context-aware neuron activations and applies adaptive masking to modulate their influence during generation. |
| Outcome: | The proposed framework detects context-aware neuron activations and applies adaptive masking to modulate their influence during generation. |
Label Effects: Shared Heuristic Reliance in Trust Assessment by Humans and LLM-as-a-Judge (2026.acl-long)
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| Challenge: | Large language models (LLMs) are increasingly used as automated evaluators . et al., 2024: strong labels can foster trust but also undermine it . |
| Approach: | They show that LLMs' source labels bias trust judgments by humans . they use eye-tracking data to analyze LLM internal states during judgment . |
| Outcome: | The proposed model is biased by disclosed source labels, the authors show . eye-tracking data show humans rely heavily on source labels for judgments . |
GPT-4 as a Homework Tutor Can Improve Student Engagement and Learning Outcomes (2025.acl-long)
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| Challenge: | a recent study has shown that homework is never graded or is done superficially. |
| Approach: | They propose a prompting strategy that enables GPT-4 to conduct interactive homework sessions for high school students learning English as a second language. |
| Outcome: | The proposed solution improves homework in high school students learning English as a second language with minimal effort in content preparation, one of the key challenges of alternative methods. |
HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claims (2025.acl-long)
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| Challenge: | Identifying checkworthy claims is the first step, but detection methods struggle with content that is (1) multimodal, (2) from diverse domains, and (3) synthetic. |
| Approach: | They propose a dataset for multimodal checkworthiness detection with 27K real-world and synthetic image/claim pairs. |
| Outcome: | The proposed dataset compares lightweight text-based encoders to multimodal models but only focus on claim-like content. |
GRAID: Synthetic Data Generation with Geometric Constraints and Multi-Agentic Reflection for Harmful Content Detection (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are expensive to run within a large-scale system and not ideal for low-latency use cases. |
| Approach: | They propose a pipeline that leverages Large Language Models (LLMs) for dataset augmentation. |
| Outcome: | The proposed pipeline improves the performance of a harmful text classification dataset using Large Language Models (LLMs). |
MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Knowledge Poisoning Attacks (2026.acl-long)
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Hyeonjeong Ha, Qiusi Zhan, Jeonghwan Kim, Dimitrios Bralios, Saikrishna Sanniboina, Nanyun Peng, Kai-Wei Chang, Daniel Kang, Heng Ji
| Challenge: | Existing research exposes multimodal large language models to knowledge poisoning attacks . localized poisoning attack achieves up to 56% success rate even under restricted access . globalized poison attack completely disrupts model generation to 0% accuracy with just one poisoned content. |
| Approach: | They propose a framework to study the vulnerability of multimodal RAG under knowledge poisoning attacks. |
| Outcome: | The proposed framework exploits two new attack strategies on multimodal RAGs under knowledge poisoning. |
VCB Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents (2026.findings-acl)
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Jiliang Hu, Wenfu Wang, Zuchao Li, Chenxing Li, Yiyang Zhao, Hanzhao Li, Liqiang Zhang, Meng Yu, Dong Yu
| Challenge: | despite advances in multimodal conversational systems, current benchmarks lack comprehensive evaluation across key dimensions. |
| Approach: | They propose a Chinese benchmark built exclusively on real human speech to fill this gap . they assess LALMs across three complementary axes: instruction following, knowledge understanding, robustness . |
| Outcome: | VCB Bench assesses LALMs across three complementary axes: instruction following, knowledge understanding, and robustness . VCBM Bench provides reproducible and fine-grained framework for Chinese voice chat bots . results show significant performance disparities and offer tangible insights for future improvements . |
From Detection to Understanding: Multi-Turn Reasoning for Video Misinformation Analysis (2026.acl-long)
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| Challenge: | Existing benchmarks focus on binary veracity judgments and do not evaluate process-level justifications for misinformation models. |
| Approach: | They propose a video misinformation analysis benchmark that assesses reasoning in video misinterpretation. |
| Outcome: | The proposed framework improves reasoning accuracy and explanation quality compared to existing models . it covers 12 fine-grained deception categories and progresses from perceptual attribution to intent and persuasion analysis. |
Personalization up to a Point: Why Personalized Content Moderation Needs Boundaries, and How We Can Enforce Them (2025.emnlp-main)
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| Challenge: | Personalized content moderation can protect users from harm while facilitating free expression . however, it can also allow highly harmful and even illegal hate speech to spread . |
| Approach: | They propose to enforce legal boundaries on personalized content moderation models to reduce legal violations while maintaining user welfare. |
| Outcome: | The proposed approach reduces legal violations while maintaining user welfare while maintaining a high degree of model performance. |
From TDMA to CDMA: A Multi-bit Watermark for Diffusion Language Models (2026.acl-long)
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| Challenge: | Existing multi-bit watermarking schemes cannot be directly applied to DLMs. |
| Approach: | They propose a multi-bit watermarking framework that encodes the entire watermark message holographically. |
| Outcome: | The proposed framework encodes the entire watermark message across all tokens holographically. |
Beyond Static Benchmarks: Synthesizing Harmful Content via Persona-based Simulation for Robust Evaluation (2026.acl-long)
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| Challenge: | Existing static benchmarks for harmful content detection face limitations in scalability and diversity. |
| Approach: | They propose a framework for synthesizing harmful content using persona-guided large language model agents. |
| Outcome: | The proposed framework achieves a high success rate in harmful generation tests across multiple detection systems. |
Making Revisions Understandable: A Survey of Edit Intentions, Methods, and Applications (2026.findings-acl)
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| Challenge: | Text revision is a core process in document creation, capturing how authors iteratively refine, reorganize, and improve written content. |
| Approach: | They synthesize text revision research through the lens of edit intentions . they review prior work across the revision workflow including corpus construction, edit intention taxonomies, edit intentions, and edit intention identification. |
| Outcome: | The proposed approach synthesizes datasets, taxonomies, identification methods, and applications and highlights key open research directions. |
Cross-Examination Framework: A Task-Agnostic Diagnostic for Information Fidelity in Text-to-Text Generation (2026.acl-long)
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Tathagata Raha, Clement Christophe, Nada Saadi, Hamza A Javed, Marco AF Pimentel, Ronnie Rajan, Praveenkumar Kanithi
| Challenge: | Traditional metrics like BLEU and BERTScore fail to capture semantic fidelity in generative text-to-text tasks. |
| Approach: | They propose a cross-examination framework that generates verifiable questions from each text and performs a Cross-exam to derive three interpretable scores: Coverage, Conformity, and Consistency. |
| Outcome: | The proposed framework detects critical errors across translation, summarization and clinical note-generation and human expert validation shows it is reliable without gold references. |
DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection (2026.acl-long)
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Junchao Wu, Yefeng Liu, Chenyu Zhu, Hao Zhang, Zeyu Wu, Tianqi Shi, Yichao Du, Longyue Wang, Weihua Luo, Jinsong Su, Derek F. Wong
| Challenge: | Existing detectors are limited in their ability to detect large language models generated content in multilingual environments. |
| Approach: | They propose a multilingual benchmark to evaluate advanced detectors across 8 dimensions to better align with real-world applications. |
| Outcome: | The proposed benchmark encompasses 8 languages commonly used in commercial contexts and collects human-written texts from 6 domains highly susceptible to LLM misuse. |
A Simple Yet Effective Method for Non-Refusing Context Relevant Fine-grained Safety Steering in LLMs (2025.emnlp-main)
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| Challenge: | Existing methods for fine-tuning large language models to meet safety policies are costly and impractical. |
| Approach: | They propose a method to fine-tune large language models to meet evolving safety policies by applying a gradient-free, unsupervised approach. |
| Outcome: | The proposed method provides precise control, avoids blanket refusals, and directs models to generate safe, relevant content. |
How Do LLMs "Trust" Unknown Knowledge? An Unknown Knowledge Based Jailbreak Attack (2026.findings-acl)
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| Challenge: | Existing research on how to effectively utilize unknown knowledge has focused on how it can be used to enhance LLMs' performance in specialized fields. |
| Approach: | They propose a completely unrestricted and fully randomized jailbreak attack that embeds malicious queries within trust-enhanced unknown knowledge. |
| Outcome: | The proposed method achieves 99% to 100% ASR on all tested LLMs, including the latest GPT-5.1, and becomes SOTA. |
TAIGR: Towards Modeling Influencer Content on Social Media via Structured, Pragmatic Inference (2026.acl-long)
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| Challenge: | Health influencers are often conveyed through conversational narratives and rhetorical strategies rather than explicit factual claims. |
| Approach: | They propose a framework to analyze influencer discourse using takeaway argumentation inference with Grounded References. |
| Outcome: | The proposed framework is based on a content validation task over influencer video transcripts on health, showing that accurate validation requires modeling the discourse’s pragmatic and argumentative structure rather than treating transcripts as flat collections of claims. |
Prior Beliefs Prejudice LLM-as-Judge: Evidence from Persuasion Evaluation (2026.findings-acl)
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| Challenge: | Large Language Models are increasingly used as judges to evaluate text quality, content and assess arguments. |
| Approach: | They propose to exploit belief-conditioned rating inflation by using persuasion-based probing to examine persuasive arguments. |
| Outcome: | The proposed model fails to evaluate persuasive arguments based on belief alignment . the model fails in three of the three tasks, with belief-conditioned rating inflation accounting for 88% of cases. |
Rethinking Composed Image Retrieval Evaluation: A Fine-Grained Benchmark from Image Editing (2026.acl-long)
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Tingyu Song, Yanzhao Zhang, Mingxin Li, Zhuoning Guo, Dingkun Long, Pengjun Xie, Siyue Zhang, Yilun Zhao, Shu Wu
| Challenge: | Composed Image Retrieval (CIR) is a complex task in multimodal understanding . current CIR benchmarks lack a robust evaluation pipeline and limited query categories . |
| Approach: | They construct a fine-grained CIR benchmark that allows for precise control over modification types and content. |
| Outcome: | The proposed benchmark covers 5,000 high-quality queries structured across five main categories and fifteen subcategories. |
DiNO: Disinformation Narrative Observer (2026.acl-long)
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| Challenge: | Disinformation is an escalating global threat, making it essential to understand its content, dissemination, and evolution. |
| Approach: | They propose a method to extract disinformation narratives from news articles . they evaluated how well their topics and stances aligned with a recognized disinformation dataset. |
| Outcome: | The proposed method outperforms other narrative mining methods in analyzing disinformation narratives. |
Jailbreak-Zero: A Path to Pareto Optimal Red Teaming for Large Language Models (2026.acl-long)
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Kai Hu, Abhinav Aggarwal, Mehran Khodabandeh, David Zhang, Eric Hsin, Li Chen, Ankit Jain, Matt Fredrikson, Akash Bharadwaj
| Challenge: | Existing approaches to red teaming are based on example-based evaluation, where a static list of specific prompts is used to define and measure "unsafe content" |
| Approach: | They propose a new automated red teaming framework that shifts from example-based to policy-based evaluation that focuses on risk coverage, semantic diversity, and fidelity. |
| Outcome: | The proposed method achieves superior, human-readable attacks against open-source and proprietary models even for unseen safety policies. |
SciPedia: Unlocking the Value of Scientific Data for Pre-training (2026.acl-long)
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| Challenge: | High-quality scientific data is critical for advancing LLMs, yet academic literature remains underutilized. |
| Approach: | They construct a large-scale raw scientific corpus but identify a critical Learnability Gap . they develop a multi-stage pipeline featuring content cleaning and pedagogical augmentation . |
| Outcome: | The proposed approach boosts average performance by +2.12 (3B) and +2.95 (7B) on in-domain tasks. |