Papers with Ethical NLP
PubHealthTab: A Public Health Table-based Dataset for Evidence-based Fact Checking (2022.findings-naacl)
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| Challenge: | Fact-checking is the task of establishing the veracity of factual information, commonly performed manually by journalists. |
| Approach: | They propose a table fact-checking dataset based on real world public health claims and noisy evidence tables from sources similar to those used by fact checkers. |
| Outcome: | The proposed dataset achieves an overall F1 score of 0.73 . |
LUX (Linguistic aspects Under eXamination): Discourse Analysis for Automatic Fake News Classification (2021.findings-acl)
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| Challenge: | Automated fact-checking is time-consuming and cannot scale due to a lack of suitable training data. |
| Approach: | They propose to use a dataset to automatically check facts and a text classifier to infer the likelihood of the input being a piece of fake-news. |
| Outcome: | The proposed dataset VERITAS and LUX use linguistic analysis to infer the likelihood of the input being a piece of fake-news. |
A Two-Sided Discussion of Preregistration of NLP Research (2023.eacl-main)
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| Challenge: | et al. (2021) suggest NLP research should adopt preregistration to prevent fishing expeditions and promote publication of negative results. |
| Approach: | et al. suggest NLP research should adopt preregistration to prevent fishing expeditions and promote publication of negative results. |
| Outcome: | The proposed approach solves many methodological problems with NLP research. |
What the #?*!: Disentangling Hate Across Target Identities (2025.naacl-long)
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| Challenge: | Hate speech classifiers do not perform equally well in detecting hateful expressions towards different target identities. |
| Approach: | They propose to use two recently proposed functionality test datasets to analyze the impact of different factors on HS prediction. |
| Outcome: | The proposed classifiers do not perform equally well across different datasets and different target identities. |
Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech (2021.findings-acl)
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| Challenge: | Off-the-shelf methods to generate hate speech are limited in that they generate repetitive and safe responses regardless of the hate speech. |
| Approach: | They propose a three-module pipeline approach to generate diverse and relevant counterspeech . they first generate various counterspeak candidates by a generative model, then filter ungrammatical ones using a BERT model . |
| Outcome: | The proposed pipeline generates diverse and relevant counterspeech responses on three datasets. |
FAKTA: An Automatic End-to-End Fact Checking System (N19-4)
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| Challenge: | Existing studies have investigated individual components of fact checking process but none offer such a capability. |
| Approach: | They propose a framework that integrates various components of a fact-checking process. |
| Outcome: | The proposed framework integrates various components of a fact-checking process to predict the factuality of claims and provide evidence at the document and sentence level to explain its predictions. |
Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification (D19-53)
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| Challenge: | Existing methods to distinguish between trusted and fake news articles lack feature engineering . et al. (2009) define fake news as the one which deliberately exposes real-world individuals, organisations and events to ridicule. |
| Approach: | They propose a graph neural network-based model which captures sentence interactions within a document. |
| Outcome: | The proposed model beats baselines and achieves state-of-the-art accuracy on existing datasets. |
Team GPLSI. Approach for automated fact checking (D19-66)
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| Challenge: | Automated fact checking is a task for proving news veracity by reliable sources. |
| Approach: | They propose to use triplets to extract sentences and compare them to Wikipedia articles using semantic similarity. |
| Outcome: | The proposed approach is satisfactory but there is room for improvement. |
How to be FAIR when you CARE: The DGS Corpus as a Case Study of Open Science Resources for Minority Languages (2022.lrec-1)
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| Challenge: | a corpus of recordings of members of the deaf community in germany is published under the FAIR principles . CARE principles are used to ensure data is open and respects indigenous and minority group stakeholders. |
| Approach: | This article describes how the DGS Corpus implemented the CARE principles . the principles were introduced as a guide to good open data practices . they also help identify how openness of data should be limited or adjusted . |
| Outcome: | The DGS Corpus is a large collection of recordings of members of the deaf community in germany . the CARE principles have been used to help the corpus achieve its goals . |
LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models (2025.emnlp-industry)
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| Challenge: | Large language models (LLMs) struggle with factual accuracy in knowledge-intensive domains like healthcare. |
| Approach: | They propose a framework for improving LLM factuality in medical question answering . RAFE, Fact-Check-then-RAG and Learning from Fact Check are components . |
| Outcome: | Experimental results show that LEAF outperforms Factcheck-GPT in detecting inaccuracies and corrects errors without labeling . the framework provides a scalable solution for industrial applications requiring high factuality scores. |
ARHNet - Leveraging Community Interaction for Detection of Religious Hate Speech in Arabic (P19-2)
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| Challenge: | Existing methods to detect hate speech in Arabic rely on textual cues and social network graphs. |
| Approach: | They propose to use Arabic word embeddings and social network graphs to profile hate speech in Arabic. |
| Outcome: | The proposed model incorporates Arabic Word Embeddings and Social Network Graphs for the detection of religious hate speech in Arabic. |
FakeFlow: Fake News Detection by Modeling the Flow of Affective Information (2021.eacl-main)
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| Challenge: | In short news articles, authors add exaggerations or fabricate events to manipulate readers' emotions. |
| Approach: | They propose to model the flow of affective information in fake news articles using a neural architecture and combine topic and affective data extracted from text. |
| Outcome: | The proposed model outperforms state-of-the-art methods on four real-world datasets and shows that it can capture the flow of affective information in fake news articles. |
Not that much power: Linguistic alignment is influenced more by low-level linguistic features rather than social power (P18-1)
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| Challenge: | linguistic alignment between interlocutors of higher power is attributed to their relative social power, but studies on low-level linguistic features do not account for these factors. |
| Approach: | They characterize the effect of power on alignment with logistic regression models in two datasets and find it vanishes after controlling for low-level features such as utterance length. |
| Outcome: | The proposed model shows that the effect vanishes or is reversed after controlling for low-level features such as utterance length. |
Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge (2021.acl-long)
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| Challenge: | Existing methods for fake news detection rely on linguistic and semantic features from news content and do not exploit external knowledge. |
| Approach: | They propose a graph neural model which compares news to knowledge base through entities for fake news detection. |
| Outcome: | The proposed model significantly outperforms state-of-the-art methods on two benchmark datasets. |
AgentReview: Exploring Peer Review Dynamics with LLM Agents (2024.emnlp-main)
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| Challenge: | Existing methods of peer review analysis do not address multivariate nature of the process, account for latent variables, and are constrained by privacy concerns due to the sensitive nature of data. |
| Approach: | They propose a large language model based peer review simulation framework which effectively disentangles the impacts of multiple latent factors and addresses the privacy issue. |
| Outcome: | The proposed framework disentangles the impacts of multiple latent factors and addresses privacy concerns. |
Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) can assist multimodal fake news detection by predicting pseudo labels, but their effective integration is non-trivial. |
| Approach: | They propose a global label propagation network with LLM-based pseudo labels for multimodal fake news detection which integrates LLM capabilities via label propagations. |
| Outcome: | The proposed model outperforms state-of-the-art models on benchmark datasets showing that it can propagate pseudo labels among all samples. |
Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe (2023.acl-long)
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Xiang Yue, Huseyin Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Hoda Shajari, Huan Sun, David Levitan, Robert Sim
| Challenge: | Privacy concerns have increased in data-driven products due to the tendency of machine learning models to memorize sensitive training data. |
| Approach: | They propose a method for generating useful synthetic text with a formal privacy guarantee by fine-tuning a pretrained generative language model with DP. |
| Outcome: | The proposed method produces synthetic text competitive in terms of utility with its non-private counterpart, while providing strong protection against potential privacy leakages. |
To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence (2021.findings-emnlp)
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| Challenge: | a new study examines the media coverage of police violence in the United States by examining the framing of 82k news articles spanning 7k police killings. |
| Approach: | They propose an NLP framework to measure entity-centric framing to understand media coverage on police violence in the United States in a new police violence frames corpus of 82k news articles spanning 7k police killings. |
| Outcome: | The proposed framework reveals significant differences in the way liberal and conservative news sources frame both the issue of police violence and the entities involved. |
Placing M-Phasis on the Plurality of Hate: A Feature-Based Corpus of Hate Online (2022.lrec-1)
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Dana Ruiter, Liane Reiners, Ashwin Geet D’Sa, Thomas Kleinbauer, Dominique Fohr, Irina Illina, Dietrich Klakow, Christian Schemer, Angeliki Monnier
| Challenge: | HS-related corpora over-simplify the phenomenon of hate by labelling user content with binary classes, e.g., hate/neutral . this ignores the complex and subjective nature of HS, which limits the real-life applicability of classifiers trained on these corporales. |
| Approach: | They present a corpus of 9k German and french user comments from migration-related news articles. |
| Outcome: | The proposed corpus is annotated with 23 features that become descriptors of various types of speech, ranging from critical comments to implicit and explicit expressions of hate. |
Improving Fairness of Large Language Models in Multi-document Summarization (2025.acl-short)
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| Challenge: | Recent studies focus on summary-level fairness, while corpus-level focuses on corpus of summaries. |
| Approach: | They propose a preference tuning method that focuses on both summary-level and corpus-level fairness in MDS. |
| Outcome: | The proposed method outperforms baselines while maintaining critical qualities of summaries. |
Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models (2022.naacl-main)
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| Challenge: | Pre-trained language models encode correlations between social groups and traits, like associating the group with the group. |
| Approach: | They adapt the Agency-Belief-Communion (ABC) stereotype model to a language model and introduce the sensitivity test (SeT) to measure stereotypical associations. |
| Outcome: | The proposed framework is used to measure stereotyping of intersectional identities in language models. |
When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms (2026.acl-long)
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| Challenge: | Existing fact-checking pipelines focus on written claims, not on audio . authors argue that audio misinformation is structurally different because it is both spoken and conversational . |
| Approach: | They argue that audio misinformation is structurally different because it is both spoken and conversational . they argue that advancing fact-checking requires rethinking verification pipelines around spoken and conversations . |
| Outcome: | The proposed method fails on audio because it is both spoken and conversational . podcasts exceed 4.3 million distinct shows, reaching an estimated 500 million listeners globally . |
SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking (2026.acl-industry)
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| Challenge: | Existing methods struggle with semantic ambiguity, homonyms, and complex linguistic structures, often trading accuracy for efficiency. |
| Approach: | They propose a Vietnamese fact-checking framework that integrates SER and TVC to achieve 78.97% strict accuracy. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy with 78.97% strict accuracy on ISE-DSC01 and 80.82% on ViWikiFC while maintaining competitive accuracy. |
Can AI Relate: Testing Large Language Model Response for Mental Health Support (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) are already being piloted for clinical use in hospitals . recent failures of the Tessa chatbot have led to doubts about their reliability in high-stakes settings. |
| Approach: | They propose safety guidelines for the potential deployment of large language models for mental health response. |
| Outcome: | The proposed framework measures equity in empathy and adherence of LLM responses to motivational interviewing theory. |
Oddballs and Misfits: Detecting Implicit Abuse in Which Identity Groups are Depicted as Deviating from the Norm (2024.emnlp-main)
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| Challenge: | Abusive language is often defined as hurtful, derogatory or obscene utterances made by one person to another. |
| Approach: | They propose to use a dataset to detect abusive sentences in identity groups . they also report on classification experiments. |
| Outcome: | The proposed dataset includes 7 identity groups and includes classification experiments. |
Measuring the Impact of Readability Features in Fake News Detection (2020.lrec-1)
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Roney Santos, Gabriela Pedro, Sidney Leal, Oto Vale, Thiago Pardo, Kalina Bontcheva, Carolina Scarton
| Challenge: | Recent efforts to detect fake news use language-based approaches to detect news articles . authors show that readability features can improve classification accuracy . |
| Approach: | They propose to use readability features to detect fake news in the Brazilian Portuguese language . they show that such features can achieve up to 92% classification accuracy . |
| Outcome: | The proposed features achieve up to 92% accuracy and may improve previous classification results. |
CluSanT: Differentially Private and Semantically Coherent Text Sanitization (2025.naacl-long)
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| Challenge: | Existing implementations of Differential Privacy (DP) in NLP typically degrade semantic integrity and readability for humans, posing significant challenges for applications requiring high-quality, coherent text processing. |
| Approach: | They propose a text sanitization framework based on Metric Local Differential Privacy (MLDP) that uses large language models to create a set of potential substitute tokens and a parameterized cluster embedding to samaritize/substitute sensitive tokens. |
| Outcome: | The proposed framework can be tuned with parameters such that existing state-of-the-art token sanitization algorithms can be described and improved. |
Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets (2020.emnlp-main)
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| Challenge: | a recent study has shown that data collection is neglected by ignoring the quality of data. |
| Approach: | They propose to use latent semantics to evaluate selection bias in hate speech . they compare latent Dirichlet Allocation (LDA) to eleven hate speech corpora . |
| Outcome: | The proposed method could be revisable before focusing on classification performance. |
Selective Differential Privacy for Language Modeling (2022.naacl-main)
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| Challenge: | Existing methods to protect sensitive data from leaking are over-pessimistic and undifferentiated. |
| Approach: | They propose a new privacy notion, selective differential privacy, to provide rigorous privacy guarantees on the sensitive portion of the data to improve model utility. |
| Outcome: | The proposed privacy-preserving mechanism achieves better utility while remaining safe under various privacy attacks compared to baselines. |
Challenges in Pre-Training Graph Neural Networks for Context-Based Fake News Detection: An Evaluation of Current Strategies and Resource Limitations (2024.lrec-main)
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| Challenge: | Graph Neural Networks (GNNs) are used to train neural networks to detect fake news based on context-based methods. |
| Approach: | They propose to combine the two by applying pre-training of Graph Neural Networks (GNNs) in the domain of context-based fake news detection. |
| Outcome: | The proposed methods show that transfer learning does not lead to significant improvements over training a model from scratch in the domain of context-based fake news detection. |
D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research (2022.lrec-1)
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| Challenge: | DBLP is the largest open-access repository of scientific articles on computer science and provides metadata associated with publications, authors, and venues. |
| Approach: | They extracted metadata from more than 6 million DBLP publications to create the DB3 Discovery Dataset (D3) . they found that computer science is a growing research field (15% annually), with an active and collaborative researcher community. |
| Outcome: | The DBLP Discovery Dataset (D3) can be used to identify trends in research activity, productivity, focus, bias, accessibility, and impact of computer science research. |
MentSum: A Resource for Exploring Summarization of Mental Health Online Posts (2022.lrec-1)
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| Challenge: | Mental health remains a significant challenge of public health worldwide . many use online platforms to share their mental health conditions and seek help . |
| Approach: | They analyze a dataset of over 24k user posts from Reddit and 43 mental health subreddits to generate a short summarization. |
| Outcome: | The proposed dataset compared over 24k user posts and 43 mental health subreddits . it shows that the summarization of these posts is faster and more accurate than previous studies. |
GenderQuant: Quantifying Mention-Level Genderedness (N19-1)
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| Challenge: | Existing approaches to detect gendered language require considerable annotation efforts for each language, domain, and author, and often require handcrafted lexicons and features. |
| Approach: | They use existing NLP pipelines to automatically annotate gender of mentions in the text and train a supervised classifier to predict the gender of any mention from its context and evaluate it on unseen text. |
| Outcome: | The proposed method can detect gendered language on movie summaries, movie reviews, news articles, and fiction novels. |
PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training (2022.emnlp-main)
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| Challenge: | Table-based fact verification has attracted a lot of attention recently due to the lack of datasets that can be used to pre-train language models to be aware of common table operations. |
| Approach: | They propose a table-based fact verification tool that pre-trains language models to be aware of common table operations such as aggregating a column or comparing tuples. |
| Outcome: | The proposed method outperforms previous methods on two table-based fact verification datasets TabFact and SEM-TAB- FACTS. |
FairPrism: Evaluating Fairness-Related Harms in Text Generation (2023.acl-long)
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Eve Fleisig, Aubrie Amstutz, Chad Atalla, Su Lin Blodgett, Hal Daumé III, Alexandra Olteanu, Emily Sheng, Dan Vann, Hanna Wallach
| Challenge: | FairPrism dataset provides a framework for measuring and mitigating fairness-related harms caused by AI text generation systems. |
| Approach: | They propose a dataset of 5,000 examples of AI-generated English text with detailed human annotations covering a diverse set of harms relating to gender and sexuality. |
| Outcome: | FairPrism is a dataset of 5,000 examples of AI-generated English text with detailed human annotations covering harms relating to gender and sexuality. |
FACTOID: A New Dataset for Identifying Misinformation Spreaders and Political Bias (2022.lrec-1)
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| Challenge: | Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society. |
| Approach: | They propose a new reddit dataset for fake news spreader analysis, called FACTOID, which tracks political discussions on Reddit since the beginning of 2020. |
| Outcome: | The proposed dataset contains over 4K users with 3.4M posts and includes their credibility level (very low to very high) and political bias strength (extreme right to extreme left). |
He said “who’s gonna take care of your children when you are at ACL?”: Reported Sexist Acts are Not Sexist (2020.acl-main)
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Patricia Chiril, Véronique Moriceau, Farah Benamara, Alda Mari, Gloria Origgi, Marlène Coulomb-Gully
| Challenge: | Sexism is prejudice or discrimination based on a person's gender. |
| Approach: | They propose to use a French dataset annotated for sexism detection to characterize sexist content and to train deep learning experiments on tweets. |
| Outcome: | The proposed dataset is the first to be used for sexism detection in France and constitutes a first step towards offensive content moderation. |
Exploring Self-Identified Counseling Expertise in Online Support Forums (2021.findings-acl)
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Allison Lahnala, Yuntian Zhao, Charles Welch, Jonathan K. Kummerfeld, Lawrence C An, Kenneth Resnicow, Rada Mihalcea, Verónica Pérez-Rosas
| Challenge: | Increasing number of people engage in online health forums, making it important to understand the quality of the advice they receive. |
| Approach: | They examine the role of expertise in responses to help-seeking posts . they find that a classifier can distinguish between peer and self-identified mental health professionals' interactions . |
| Outcome: | The findings show that experts' language use differs between groups, and that their comments engage the support-seeker further. |
Dataset for Identification of Homophobia and Transphobia for Telugu, Kannada, and Gujarati (2024.lrec-main)
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| Challenge: | There has been a rise in homophobic and transphobic content targeting LGBT+ individuals on social media platforms. |
| Approach: | They propose to use a dataset to automatically identify homophobic and transphobic content within comments collected from YouTube for three languages. |
| Outcome: | The proposed dataset will identify homophobic and transphobic content within comments collected from YouTube in Telugu, Kannada, and Gujarati. |
DeFaktS: A German Dataset for Fine-Grained Disinformation Detection through Social Media Framing (2024.lrec-main)
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| Challenge: | Distinctively curated across various news topics, DeFaktS offers an unparalleled insight into disinformation’s diverse characteristics. |
| Approach: | They propose to annotate every structural component and semantic element of a news piece, eliminating the need for external knowledge sources. |
| Outcome: | The proposed dataset contains 105,855 posts with 20,008 meticulously labeled tweets and eliminates the need for external knowledge sources. |
MisgenderMender: A Community-Informed Approach to Interventions for Misgendering (2024.naacl-long)
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| Challenge: | Misgendering is the act of incorrectly addressing someone’s gender and is pervasive in everyday use platforms and technologies. |
| Approach: | They propose a task and evaluation dataset to assess the effectiveness of automated misgendering interventions for text-based misgending in the US. |
| Outcome: | The proposed dataset includes 3790 instances of social media content and LLM-generations about non-cisgender public figures, annotated for the presence of misgendering, with additional annotations for correcting misgending in LLM generated text. |
Evaluation of African American Language Bias in Natural Language Generation (2023.emnlp-main)
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| Challenge: | Existing studies have shown that large language generation models disadvantaging African American Language (AAL) can be biased for certain language varieties, but there is little research on the impact of these biases on other languages. |
| Approach: | They evaluate how well LLMs understand African American Language (AAL) in comparison to white Mainstream English (WME) using a dataset of AAL texts from a variety of regions and contexts, they find dialectal bias in six pre-trained LLM. |
| Outcome: | The proposed models understand African American language in comparison to white mainstream English (WME) the proposed models have performance gaps on two tasks that are not matched by the model. |
Meaning Beyond Truth Conditions: Evaluating Discourse Level Understanding via Anaphora Accessibility (2025.acl-long)
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| Challenge: | Existing assessments of understanding at the lexical and sentence levels are limited to lexica and sentence level, but few of them target whether LLMs accurately represent and update states of natural language discourse. |
| Approach: | They propose anaphora accessibility as a diagnostic for assessing discourse understanding . they use a dataset inspired by theoretical research in dynamic semantics to evaluate human and LLM performance. |
| Outcome: | The proposed dataset shows that humans and LLMs align on some tasks and diverge on others. |
Hate Speech and Counter Speech Detection: Conversational Context Does Matter (2022.naacl-main)
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| Challenge: | Existing datasets and models target hate speech but ignore context . Existing models target either hate speech or hate and counter speech but disregard context - a new study shows that context is critical to identify hate and anti-hate speech. |
| Approach: | They propose to use context to identify hate and counter speech in a reddit conversation thread. |
| Outcome: | The proposed model improves when and why context is taken into account. |
Semantic Topology: a New Perspective for Communication Style Characterization (2025.findings-acl)
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| Challenge: | a new framework for discourse analysis uses Circuit Topology to quantify the semantic arrangement of sentences in textual structure. |
| Approach: | They propose a framework that leverages Circuit Topology to quantify the semantic arrangement of sentences in a text. |
| Outcome: | The proposed framework can quantify the semantic arrangement of sentences in a text. |
Words Matter: Reducing Stigma in Online Conversations about Substance Use with Large Language Models (2024.emnlp-main)
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| Challenge: | Only 7% of people living with an SUD receive any form of treatment, with stigma reported as a major barrier. |
| Approach: | They propose a computational framework for analyzing stigma and de-stigmatizing online content and delving into the linguistic features that propagate stigma towards PWUS. |
| Outcome: | The proposed model transforms stigmatizing language into more empathetic language and analyzes over 1.2 million posts on social media . |
Evaluating Bias and Fairness in Gender-Neutral Pretrained Vision-and-Language Models (2023.emnlp-main)
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| Challenge: | Pretrained machine learning models perpetuate and even amplify existing biases in data . this can result in unfair outcomes that ultimately impact user experience . |
| Approach: | They quantify bias amplification in pretraining and after fine-tuning on vision-and-language models. |
| Outcome: | The results show that pretrained models can perpetuate and even amplify biases in data without compromising performance. |
APPDIA: A Discourse-aware Transformer-based Style Transfer Model for Offensive Social Media Conversations (2022.coling-1)
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| Challenge: | Using style-transfer models to reduce offensiveness of social media comments is difficult because of limited labeled data. |
| Approach: | They propose two methods to integrate discourse relations with pretrained style-transfer models and evaluate them on a reddit dataset. |
| Outcome: | The proposed models can reduce offensiveness while preserving original meaning . they are the first to examine inferential links between comment and original text . |
STATE ToxiCN: A Benchmark for Span-level Target-Aware Toxicity Extraction in Chinese Hate Speech Detection (2025.findings-acl)
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| Challenge: | Existing studies on Chinese hate speech detection lack span-level fine-grained annotations. |
| Approach: | They construct a Span-level target-aware Toxicity Extraction dataset and evaluate existing models for Chinese hateful slang. |
| Outcome: | The proposed dataset is the first span-level Chinese hate speech dataset and evaluates the ability of existing models to understand hate semantics. |
BD-SHS: A Benchmark Dataset for Learning to Detect Online Bangla Hate Speech in Different Social Contexts (2022.lrec-1)
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Nauros Romim, Mosahed Ahmed, Md Saiful Islam, Arnab Sen Sharma, Hriteshwar Talukder, Mohammad Ruhul Amin
| Challenge: | Social media platforms and online streaming services have spawned a new breed of Hate Speech (HS) due to the massive amount of user-generated content, modern machine learning techniques are feasible and cost-effective to tackle this problem. |
| Approach: | They propose to use a large manually labeled Bangla HS dataset to train generalizable models. |
| Outcome: | The proposed dataset includes more than 50,200 offensive comments crawled from online social networking sites and is at least 60% larger than existing Bangla HS datasets. |
Generating Biographies on Wikipedia: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographies (2022.acl-long)
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| Challenge: | Existing efforts to encourage article creation focus on reducing the gender gap in Wikipedia articles. |
| Approach: | They propose a model that retrieves web evidence and generates biographies section by section . they analyze available web evidence to determine the accuracy of the generated text . |
| Outcome: | The proposed model can generate biographies section by section, including citation information, using retrieval mechanisms and a cache-based pre-trained encoder-decoder. |
Annotating for Hate Speech: The MaNeCo Corpus and Some Input from Critical Discourse Analysis (2020.lrec-1)
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| Challenge: | Existing methods for detecting hate speech are based on the problem of identification, but there is no clear definition of hate speech. |
| Approach: | They propose a multi-layer annotation scheme for the detection of hate speech in a web 2.0 corpus . they propose to use a binary hate speech classification to identify hate speech . |
| Outcome: | The proposed scheme is piloted against a binary hate speech classification and appears to yield higher inter-annotator agreement. |
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models (2023.acl-long)
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| Challenge: | Hundreds of studies have highlighted ethical issues in NLP models . |
| Approach: | They propose to measure media biases in LMs trained on diverse data sources . they focus on hate speech and misinformation detection . |
| Outcome: | The proposed methods quantify the fairness of downstream NLP models trained on politically biased LMs. |
Structure-aware Propagation Generation with Large Language Models for Fake News Detection (2025.findings-emnlp)
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| Challenge: | propagation-based methods for fake news detection often lack structural data . authors propose a structure-aware synthetic propagation enhanced detection framework . |
| Approach: | They propose a structure-aware synthetic propagation enhanced detection framework to capture real-world propagation. |
| Outcome: | The proposed framework captures structural dynamics from real propagation, while ignoring structural patterns. |
FedLEKE: Federated Locate-then-Edit Knowledge Editing for Multi-Client Collaboration (2025.findings-acl)
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| Challenge: | Existing methods for updating large language models are inefficient in multi-client scenarios . Existing approaches assume a single-user setting and are ineffective in multiclient scenarios. |
| Approach: | They propose a new task that enables multiple clients to perform LEKE while preserving privacy and reducing computational overhead. |
| Outcome: | The proposed framework outperforms existing LEKE frameworks on two benchmark datasets and retains 96% of performance. |
Human vs. Machine Perceptions on Immigration Stereotypes (2024.lrec-main)
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| Challenge: | a growing number of natural language processing models leave aside the language itself . a recent paradigm in the computational linguistics community is training models on specific perspectives of a segment of the population or an individual. |
| Approach: | They propose to use BERT-based classification models to detect stereotypes related to immigrants . they compare models with predictions from GPT-4 and annotated tweets from Spanish Twitter . |
| Outcome: | The proposed models are compared with predictions from the dataset of Spanish Twitter posts containing stereotypes . the models are confident in their predictions and more accurate for implicit stereotypes, the authors show . |
Using Sociolinguistic Variables to Reveal Changing Attitudes Towards Sexuality and Gender (2021.emnlp-main)
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| Challenge: | Existing studies show that word choice is driven by demographics within the United States. |
| Approach: | They develop computational methods to study word choice within a sociolinguistic lexical variable . they use two variables to test for attitudes towards sexuality and gender in the u.s. |
| Outcome: | The proposed methods allow us to examine attitudes towards sexuality and gender in the United States through two lexical variables. |
COLD: A Benchmark for Chinese Offensive Language Detection (2022.emnlp-main)
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| Challenge: | Offensive language detection is crucial for maintaining a civilized social media platform and deploying pre-trained language models. |
| Approach: | They propose a benchmark benchmark for Chinese offensive language analysis including a Chinese Offensive Language Dataset and a baseline detector which is trained on the dataset. |
| Outcome: | The proposed benchmark contributes to Chinese offensive language detection which is challenging for existing resources. |
DPGA-TextSyn: Differentially Private Genetic Algorithm for Synthetic Text Generation (2025.findings-acl)
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Zhonghao Sun, Zhiliang Tian, Yiping Song, Yuyi Si, Juhua Zhang, Minlie Huang, Kai Lu, Zeyu Xiong, Xinwang Liu, Dongsheng Li
| Challenge: | Existing methods to fine-tune large language models pose privacy risks . researchers have synthesized data with strong generation capabilities closed-source LLMs to alleviate this problem . |
| Approach: | They propose to combine general LLMs with genetic algorithm to produce relevant and diverse synthetic text under differential privacy constraints. |
| Outcome: | The proposed method significantly improves the performance of the model in downstream tasks while ensuring privacy. |
Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features (2023.findings-emnlp)
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| Challenge: | Current knowledge is limited on whether cultural features can predict cross-cultural transfer learning success for subjective tasks. |
| Approach: | They advocate integration of cultural information into datasets and cultural adaptability . findings suggest cultural features can predict cross-cultural transfer learning success . |
| Outcome: | The findings suggest that cultural features can predict cross-cultural transfer learning success in OLD tasks. |
Knowledge Planning in Large Language Models for Domain-Aligned Counseling Summarization (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) exhibit remarkable capabilities in various generative tasks, but their adaptation to domain-specific intricacies remains challenging. |
| Approach: | They propose to use a planning engine to orchestrate structuring knowledge alignment to achieve high-order planning by encapsulating domain knowledge and leveraging sheaf convolution learning to enhance its understanding of the dialogue’s structural nuances. |
| Outcome: | The proposed framework improves on existing LLMs and shows that it can generate better summaries with better quality and better execution. |
From Descriptive Richness to Bias: Unveiling the Dark Side of Generative Image Caption Enrichment (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have enhanced the capacity of vision-language models to caption visual text. |
| Approach: | They compare standard-format captions and recent GCE processes from the perspectives of gender bias and hallucination. |
| Outcome: | The proposed methods amplify gender bias by 30.9% and increase hallucination by 59.5%. |
Automated Fact-Checking in Dialogue: Are Specialized Models Needed? (2023.emnlp-main)
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| Challenge: | Prior work has shown that typical fact-checking models struggle with claims made in conversation. |
| Approach: | They propose to fine-tune models for dialogue on conversational data to improve performance on typical fact-checking. |
| Outcome: | The proposed models perform better on stand-alone claims than state-of-the-art models for dialogue while maintaining their performance on standalone claim. |
Who is better at math, Jenny or Jingzhen? Uncovering Stereotypes in Large Language Models (2024.emnlp-main)
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| Challenge: | Existing research on stereotypes in large language models is limited and focuses on African Ameri- F. |
| Approach: | They propose to use global bias to probe a set of large language models via perplexity to determine how certain stereotypes are represented in the model's internal representations. |
| Outcome: | The proposed model amplifys harmful stereotypes and shows that the demographic groups associated with stereotypes remain consistent across model likelihoods and outputs. |
QUEEREOTYPES: A Multi-Source Italian Corpus of Stereotypes towards LGBTQIA+ Community Members (2024.lrec-main)
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Alessandra Teresa Cignarella, Manuela Sanguinetti, Simona Frenda, Andrea Marra, Cristina Bosco, Valerio Basile
| Challenge: | a dataset of social media texts addressing LGBTQIA+ individuals is presented in this paper . the dataset is based on two sources in italian: Facebook and Twitter . |
| Approach: | They describe a dataset composed of two sub-corpora from two different sources in Italian . the dataset includes social media texts regarding LGBTQIA+ individuals, behaviors, ideology and events . |
| Outcome: | The QUEEREOTYPES dataset includes social media texts regarding LGBTQIA+ individuals, behaviors, ideology and events. |
Local Contrastive Editing of Gender Stereotypes (2024.emnlp-main)
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| Challenge: | Stereotypical bias encoded in language models (LMs) poses a threat to safe language technology . current research lacks a thorough understanding of manifestations of biases in specific model weights. |
| Approach: | They propose a method that localizes and edits weights associated with gender bias . they use local contrastive editing to localize and control a small subset of weights . |
| Outcome: | The proposed method localizes and controls a small subset of weights that encode gender bias. |
Estimating Privacy Leakage of Augmented Contextual Knowledge in Language Models (2025.acl-long)
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| Challenge: | Prior work has shown that privacy leakage of parametric knowledge often occurs from memorized pre-training data. |
| Approach: | They propose a metric that builds on differential privacy to estimate the privacy leakage of contextual knowledge during decoding by comparing parametric and contextual knowledge. |
| Outcome: | The proposed method overestimates the privacy leakage of parametric knowledge while separating parametric and contextual knowledge. |
Style-Shifting Behaviour of the Manosphere on Reddit (2024.emnlp-main)
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| Challenge: | Hate speech groups (HSGs) may negatively influence online platforms through their distinctive language, which may affect the tone and topic of discussion in other spaces if spread beyond the HSGs. |
| Approach: | They explore the linguistic style of the Manosphere on reddit and how it reflects their linguistic styles across communities. |
| Outcome: | The linguistic style of the Manosphere on Reddit is studied to determine whether it is harmful to health and community health. |
DiNaM: Disinformation Narrative Mining with Large Language Models (2025.emnlp-main)
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| Challenge: | Disinformation is a powerful force in digital media, posing serious threats such as physical harm and the erosion of democracy. |
| Approach: | They propose to use a multi-step approach to uncover disinformation narratives by using Large Language Models to detect false information and then using clustering techniques to identify underlying disinformation stories. |
| Outcome: | The proposed algorithm outperforms general-purpose narrative mining methods by 16.4–24.7%. |
VeriTaS: The First Dynamic Benchmark for Multimodal Automated Fact-Checking (2026.acl-long)
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| Challenge: | Existing benchmarks for evaluating AFC systems are limited in terms of task scope, modalities, domain, language diversity, realism, or coverage of misinformation types. |
| Approach: | They propose to use Verified Theses and Statements (VeriTaS) to evaluate AFC systems that are static and subject to data leakage as claims enter pretraining corpora. |
| Outcome: | The proposed system is robust under large-scale pretraining of foundation models and can be updated in the future. |