Papers by Eduardo Blanco
Possessors Change Over Time: A Case Study with Artworks (D18-1)
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| Challenge: | Existing methods to extract possession relations from Wikipedia articles can be used to extract possessors over time. |
| Approach: | They propose to extract possession relations from Wikipedia articles and temporal information indicating when these relations are true. |
| Outcome: | The proposed annotation scheme yields many possessors over time for a given artwork, and an LSTM ensemble can automate the task. |
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. |
Predicting the Focus of Negation: Model and Error Analysis (2020.acl-main)
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| Challenge: | Experimental results show that a scope detector can predict the focus of negation . negation is a complex phenomenon present in all human languages . |
| Approach: | They propose to leverage a scope detector to introduce the scope of negation as an additional input to the neural network. |
| Outcome: | The proposed model obtains the best results to date, and analyzes errors depending on scope and context information. |
From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agents (2026.findings-acl)
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| Challenge: | Existing benchmarks frame long-term memory evaluation as fact retrieval from past conversations, providing limited insight into agents’ ability to consolidate memory over time or handle frequent knowledge updates. |
| Approach: | They propose a long-term memory benchmark that evaluates three memory-grounded tasks: remembering, reasoning, and recommending. |
| Outcome: | The proposed benchmarks evaluate three tasks: remembering, reasoning, and recommending. |
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)
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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin
| Challenge: | a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities . |
| Approach: | They present a comparative analysis to identify and distinguish LLM activities from human activities. |
| Outcome: | The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities. |
RobustSentEmbed: Robust Sentence Embeddings Using Adversarial Self-Supervised Contrastive Learning (2024.findings-naacl)
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| Challenge: | Existing pre-trained language models exhibit poor generalization and robustness in adversarial settings. |
| Approach: | They propose a self-supervised sentence embedding framework that improves generalization and robustness against adversarial attacks. |
| Outcome: | The proposed framework reduces the success rate of adversarial attacks by almost half . it also improves semantic text similarity tasks and various transfer tasks . |
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. |
Generating Uncontextualized and Contextualized Questions for Document-Level Event Argument Extraction (2024.naacl-long)
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| Challenge: | Existing methods for document-level argument extraction do not require human involvement and combine uncontextualized and contextualized questions. |
| Approach: | They propose multiple question generation strategies for document-level event argument extraction that do not require human involvement and combine uncontextualized and contextualized questions. |
| Outcome: | The proposed questions do not require human involvement and are suitable for document-level argument extraction. |
Detecting Negation Cues and Scopes in Spanish (2020.lrec-1)
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Salud María Jiménez-Zafra, Roser Morante, Eduardo Blanco, María Teresa Martín Valdivia, L. Alfonso Ureña López
| Challenge: | Negation is a phenomenon that "relates an expression e to another expression with a meaning that is in some way opposed to the meaning of e" previous work on negation in English has focused mostly and only recently on annotation tasks. |
| Approach: | They propose a machine learning system that processes negation in Spanish . they use a corpus from the SFU corpus to perform two tasks . |
| Outcome: | The proposed system outperforms state-of-the-art in negation cue detection and scope identification. |
Helpful or Hierarchical? Predicting the Communicative Strategies of Chat Participants, and their Impact on Success (2020.findings-emnlp)
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| Challenge: | a study of 5,500 chat interactions shows that successful communicators are successful in other domains. |
| Approach: | They annotate chat interactions with four dimensions of interaction styles to predict success . they find successful communicators are also successful in other domains . |
| Outcome: | The results show that successful communicators are successful in other domains. |
Echoes of Discord: Forecasting Hater Reactions to Counterspeech (2025.findings-naacl)
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| Challenge: | Hate speech (HS) online causes increased prejudice and discrimination, fostering an environment of hostility and social division. |
| Approach: | They analyze the Reddit Echoes of Hate dataset to assess the impact of counterspeech from the hater's perspective and focus on whether the counterspeak leads the reentry to be hateful. |
| Outcome: | The proposed model outperforms the two-stage reaction predictor and the three-way classifier to predict haters' reactions to the reentry of the conversation and determines the type of resentment. |
Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding (2022.emnlp-main)
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| Challenge: | Negation poses a challenge in many natural language understanding tasks . leveraging sentences with negation and affirmative interpretations is beneficial for many tasks involving humans . |
| Approach: | They propose to collect negated sentences and their affirmative interpretations and leverage them to build a plug-and-play neural generator that generates an affirmative interpreter. |
| Outcome: | The proposed method does not require manual effort and does not impact other tasks. |
It’s not a Non-Issue: Negation as a Source of Error in Machine Translation (2020.findings-emnlp)
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| Challenge: | In this study, we focus on negation, a universal, core property of human language that affects the semantics of an utterance. |
| Approach: | They focus on negation, a universal, core property of human language that affects semantics of an utterance. |
| Outcome: | The proposed method improves translation quality by 60% in some cases . the authors also provide a linguistically motivated analysis that directly explains the majority of the results. |
Making Language Models Robust Against Negation (2025.naacl-long)
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| Challenge: | Negation is a semantic phenomenon that alters an expression to convey the opposite meaning. |
| Approach: | They propose a self-supervised method to make language models more robust against negation by pre-training models. |
| Outcome: | The proposed task outperforms the off-the-shelf versions on nine negation-related benchmarks. |
Beyond Possession Existence: Duration and Co-Possession (2020.acl-main)
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| Challenge: | Existing work on possession existence targets possession existence, but there is complementary information that can be extracted. |
| Approach: | They propose to use corpora to annotate possession existence and experimental results to determine possession duration and co-possessions. |
| Outcome: | The proposed annotations show that text is more useful than the image for solving these tasks. |
Extracting Adherence Information from Electronic Health Records (2020.coling-main)
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Jordan Sanders, Meghana Gudala, Kathleen Hamilton, Nishtha Prasad, Jordan Stovall, Eduardo Blanco, Jane E Hamilton, Kirk Roberts
| Challenge: | adherence is a critical factor in health outcomes, and is often modeled as a binary decision . adherence models include intentional and unintentional non-adherence, social support and other patient attributes such as age and time since diagnosis. |
| Approach: | They propose to extract adherence information from electronic health records using de-identified sentences and a corpus of 3,000 de-identified sentences. |
| Outcome: | The proposed framework extracts medication adherence information from electronic health records. |
Learning to Generate Rules for Realistic Few-Shot Relation Classification: An Encoder-Decoder Approach (2024.findings-emnlp)
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| Challenge: | a new approach to relation classification is proposed to use data-driven approaches to perform fewshot tasks with limited training data. |
| Approach: | They propose a neuro-symbolic approach for realistic few-shot relation classification via rules . they propose to generate rules that can be used to extract relations using custom T5-style models . |
| Outcome: | The proposed approach is interpretable and pliable and outperforms the state-of-the-art on TACRED and NYT29 benchmarks while maintaining pliability. |
Grammar Search for Multi-Agent Systems (2026.acl-long)
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Mayank Singh, Vikas Yadav, Shiva Krishna Reddy Malay, Shravan Nayak, Sai Rajeswar, Sathwik Tejaswi Madhusudhan, Eduardo Blanco
| Challenge: | Several prior approaches have relied on LLM-based free-form search over the code space. |
| Approach: | They propose a more structured framework that explores the same space through a fixed set of composable components. |
| Outcome: | The proposed framework outperforms existing approaches on most benchmarks across two backbone LLMs and two domains: mathematics and question answering. |
Not All Counterhate Tweets Elicit the Same Replies: A Fine-Grained Analysis (2023.starsem-1)
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| Challenge: | a recent survey found 41% of people reported online harassment on a personal level . a counterhate argument can effectively limit the spread of hate speech, but it can also exacerbate it . |
| Approach: | They analyze 2,621 replies to counterhate arguments countering hateful tweets and analyze their responses . they find that half of the replies disagree with the argument, and this kind of reply often supports the hateful Tweet . |
| Outcome: | The proposed method can anticipate the kind of replies a counterhate argument will elicit. |
Incorporating Emoji Descriptions Improves Tweet Classification (N19-1)
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| Challenge: | Tweets are short messages that often include specialized language such as hashtags and emojis. |
| Approach: | They propose a simple strategy to replace emojis with their natural language description and use pretrained word embeddings to process tweets. |
| Outcome: | The proposed method is more effective than pretrained emoji embeddings for tweet classification. |
An Analysis of Natural Language Inference Benchmarks through the Lens of Negation (2020.emnlp-main)
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| Challenge: | Existing benchmarks for natural language inference ignore negations and can make inferences that are difficult to make. |
| Approach: | They propose a new benchmark for natural language inference in which negation plays a critical role. |
| Outcome: | The proposed benchmarks show that negation plays a critical role in inference judgments. |
Annotating If the Authors of a Tweet are Located at the Locations They Tweet About (L18-1)
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| Challenge: | a tweet's locations do not always indicate spatial information involving the author of the tweet . a corpus of 1,062 tweets contains 1,200 location named entities . |
| Approach: | They propose a corpus annotating whether tweet authors are located in locations . they use temporal tags centered around tweet timestamps to temporally anchor this information . |
| Outcome: | The proposed method annotates whether authors are located in tweet locations . it shows that no spatial relationship can be inferred in 21% of instances . |
Written Justifications are Key to Aggregate Crowdsourced Forecasts (2021.findings-emnlp)
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| Challenge: | aggregating crowdsourced forecasts benefits from modeling written justifications . a majority of respondents support the idea that crowds are more reliable than experts . |
| Approach: | They propose to model written justifications for crowdsourced questions by analyzing their results in a literature review. |
| Outcome: | The results show that the written justifications are beneficial to call a question throughout its life except in the last quarter. |
UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs’ Memorization (2025.acl-long)
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Md Nayem Uddin, Amir Saeidi, Divij Handa, Agastya Seth, Tran Cao Son, Eduardo Blanco, Steven Corman, Chitta Baral
| Challenge: | UnSeenTimeQA is a data contamination-free time-sensitive question-answering benchmark. |
| Approach: | They propose a data contamination-free time-sensitive question-answering benchmark that avoids web-searchable queries grounded in the real world. |
| Outcome: | The proposed benchmark avoids web-searchable queries grounded in the real world and enables on-demand generation of new samples, mitigating the risk of data leakage. |
Determining Event Outcomes: The Case of #fail (2020.findings-emnlp)
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| Challenge: | Experimental results show that edibility is easier to predict than outcome quality. |
| Approach: | They use tweets containing #cookingFail or #bakingFails to determine event outcomes in social media. |
| Outcome: | The results show that edibility is easier to predict than outcome quality. |
Mining Possessions: Existence, Type and Temporal Anchors (N18-1)
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| Challenge: | Existing annotations for possession relations can be used to predict possession existence, possession type and temporal anchors. |
| Approach: | They propose to use text annotations to mine possession relations from text . they assign temporal anchors indicating when possession holds between possessor and possessee . |
| Outcome: | The proposed task can predict possession existence, possession type and temporal anchors, and it can be automated. |
Interpreting Answers to Yes-No Questions in User-Generated Content (2023.findings-emnlp)
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| Challenge: | Existing studies on yes-no questions outside social media have found that yes and no keywords are rare in answers. |
| Approach: | They propose a corpus of 4,442 yes-no question-answer pairs from twitter . they find that yes and no keywords are rare in answers and poor indicators of correct interpretation . |
| Outcome: | The proposed corpus of 4,442 yes-no question-answer pairs shows that large language models are far from solving the problem. |
Paraphrasing in Affirmative Terms Improves Negation Understanding (2024.acl-short)
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| Challenge: | Negation is a common linguistic phenomenon in human languages . however, language models face challenges with negation in many tasks . |
| Approach: | They propose to incorporate affirmative interpretations into models to make them more robust against negation. |
| Outcome: | The proposed models are more robust against negation when negation is present in input . the proposed models can be used to analyze large corpus and natural language understanding tasks . |
A Dynamic Fusion Model for Consistent Crisis Response (2025.findings-emnlp)
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| Challenge: | a critical yet often overlooked factor is the consistency of response style . few studies have explored methods for maintaining stylistic consistency across generated responses . |
| Approach: | They propose a metric for evaluating style consistency and introduce a method for fusion-based generation . |
| Outcome: | The proposed method outperforms baselines in response quality and stylistic uniformity. |
An Analysis of Negation in Natural Language Understanding Corpora (2022.acl-short)
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| Challenge: | Using annotator-generated examples, one can evaluate systems with synthetic language that is not representative of language in the wild. |
| Approach: | They analyze negation in eight popular corpora spanning six natural language understanding tasks. |
| Outcome: | The proposed corpora have few negations compared to general-purpose English and are often unimportant . state-of-the-art transformers obtain significantly worse results with instances that contain negation, especially if the negations are important. |
Structured Semantic Information Helps Retrieve Better Examples for In-Context Learning Applied to Few-Shot Relation Extraction (2026.acl-long)
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| Challenge: | Existing approaches to relation extraction require many training examples per relation, resulting in low results. |
| Approach: | They propose a strategy where new examples are selected based on their similarity to the provided 1-shot example. |
| Outcome: | The proposed strategy outperforms other methods on FS-TACRED and FS - FewRel subsets and achieves state-of-the-art performance on both datasets. |
Characterizing Interactions and Relationships between People (D18-1)
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| Challenge: | Existing methods to characterize the association between two people do not account for nuances in the relationship between two individuals. |
| Approach: | They propose to use a set of dimensions to characterize the association between two people. |
| Outcome: | The proposed model can be automated using dialogue scripts from the TV show Friends. |
Outcome-Constrained Large Language Models for Countering Hate Speech (2024.emnlp-main)
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| Challenge: | Existing research focuses on generating counterspeech with linguistic attributes such as being polite, informative, and intent-driven. |
| Approach: | They develop automatic counterspeech generation methods that incorporate two desired conversation outcomes into the text generation process: low conversation incivility and non-hateful hater reentry. |
| Outcome: | The proposed methods incorporate two desired conversation outcomes: low conversation incivility and non-hateful hater reentry. |
Determining Event Durations: Models and Error Analysis (N18-2)
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| Challenge: | a crucial piece of information regarding events is their duration, a rarely mentioned attribute . core tasks such as temporal understanding and reasoning would benefit from knowing the expected duration of events. |
| Approach: | They introduce aspectual features that capture deeper linguistic information . they also experiment with neural networks to predict event durations . |
| Outcome: | The proposed models capture deeper linguistic information than previous work and provide useful clues. |
Asking and Answering Questions to Extract Event-Argument Structures (2024.lrec-main)
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| Challenge: | Traditionally, corpora are limited to arguments within the same sentence, and inter-sentential arguments are more challenging and have received less attention. |
| Approach: | They propose a question-answering approach to extract document-level event-argument structures by automating questions for each argument type an event may have. |
| Outcome: | The proposed model outperforms previous models and is especially beneficial to extract arguments that appear in different sentences than the event trigger. |
Commonsense Knowledge with Negation: A Resource to Enhance Negation Understanding (2026.findings-acl)
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| Challenge: | Negation is a common and important semantic feature in natural language, yet Large Language Models struggle when negation is involved in natural learning tasks. |
| Approach: | They propose to augment existing corpora with negation by automatically augmenting existing ones with negations by combining multiple triples with if-then relations. |
| Outcome: | The proposed approach yields two new corpora containing over 2M triples with if-then relations. |
RobustEmbed: Robust Sentence Embeddings Using Self-Supervised Contrastive Pre-Training (2023.findings-emnlp)
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| Challenge: | Existing PLMs suffer from poor robustness in adversarial scenarios, despite their success with unseen samples. |
| Approach: | They propose a self-supervised sentence embedding framework that enhances generalization and robustness in various text representation tasks and against diverse adversarial attacks. |
| Outcome: | The proposed framework improves generalization and robustness in various representation tasks and against diverse adversarial attacks. |
WikiPossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts (2020.lrec-1)
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| Challenge: | a new corpus of articles is created for the task of temporally-oriented possession . the task is open-domain and can be used to track possession in other texts . |
| Approach: | They propose a new corpus for the task of temporally-oriented possession . they annotate Wikipedia articles for 90 different well-known artifacts . |
| Outcome: | The proposed task is based on annotated Wikipedia articles for 90 artifacts, including paintings, diamonds, and archaeological artifos. |
A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal Negations (2022.findings-naacl)
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Md Mosharaf Hossain, Luke Holman, Anusha Kakileti, Tiffany Kao, Nathan Brito, Aaron Mathews, Eduardo Blanco
| Challenge: | Negations carry affirmative meanings, which are difficult to process and understand by humans. |
| Approach: | They propose a question-answer driven approach to reveal affirmative interpretations from verbal negations. |
| Outcome: | The proposed approach is based on a natural language inference task . it shows that state-of-the-art transformers are insufficient to reveal affirmative interpretations . |
Studying Rhetorically Ambiguous Questions (2025.emnlp-main)
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| Challenge: | Existing datasets do not contain many rhetorical questions that can be rhetorical or informational depending on context. |
| Approach: | They propose a dataset explicitly constructed to support the study of rhetorical ambiguity . they evaluate the performance of state-of-the-art language models on the dataset . |
| Outcome: | The proposed dataset shows that state-of-the-art language models struggle to recognize rhetorical questions. |
ALIGN-SIM: A Task-Free Test Bed for Evaluating and Interpreting Sentence Embeddings through Semantic Similarity Alignment (2024.findings-emnlp)
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| Challenge: | Sentence embeddings play a pivotal role in a wide range of NLP tasks . evaluating and interpreting these dense vectors remains an open challenge to date . |
| Approach: | They propose a task-free test bed for evaluating and interpreting sentence embeddings . they examined five classical and eight LLM-induced sentence embedders based on semantic similarity alignment criteria . |
| Outcome: | The proposed test bed consists of five semantic similarity alignment criteria . it shows that none of the embeddings aligned with the criteria compared to other benchmarks . |
BEMEAE: Moving Beyond Exact Span Match for Event Argument Extraction (2025.naacl-long)
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| Challenge: | Event Argument Extraction (EAE) is a complex task that requires deep comprehension of text to accurately identify and classify event arguments. |
| Approach: | They propose a new evaluation metric that integrates deterministic components with a semantic matching component for more accurate assessment. |
| Outcome: | The proposed evaluation metric leads to higher F1 scores and significant changes in model rankings, underscoring ESM’s inadequacy for comprehensive evaluation of EAE. |
Interpreting Answers to Yes-No Questions in Dialogues from Multiple Domains (2024.findings-naacl)
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| Challenge: | Existing models for yes-no questions are challenging, but they still face challenges. |
| Approach: | They propose an approach grounded on distant supervision and blended training to quickly adapt to a new dialogue domain. |
| Outcome: | The proposed approach improves F1 performance in movie scripts, tennis interviews, and airline customer service domains. |
Analyzing Large Language Models’ Capability in Location Prediction (2024.lrec-main)
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| Challenge: | Large language models (LLMs) are underutilized in the field of location prediction due to the sparsity of geotagged tweets. |
| Approach: | They present experimental results with four large language models in various instruction finetuning and exemplar settings and analyze whether taking into account the context is beneficial. |
| Outcome: | The proposed model is able to predict location in a variety of settings, including fine tuning and exemplar settings, and it is compared with the best model in the literature. |
Finding Authentic Counterhate Arguments: A Case Study with Public Figures (2023.emnlp-main)
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| Challenge: | Existing attempts to generate fake counterhate arguments for hateful content are limited to hallucinate unsupported arguments. |
| Approach: | They propose a method that assures the authenticity of the counter argument and its specificity to the individual of interest. |
| Outcome: | The proposed method assures the authenticity of the counter argument and its specificity to the individual of interest. |
Interpreting Indirect Answers to Yes-No Questions in Multiple Languages (2023.findings-emnlp)
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Zijie Wang, Md Hossain, Shivam Mathur, Terry Melo, Kadir Ozler, Keun Park, Jacob Quintero, MohammadHossein Rezaei, Shreya Shakya, Md Uddin, Eduardo Blanco
| Challenge: | Existing models for Yes-no questions skip polar keywords and instead use long explanations that must be interpreted. |
| Approach: | They propose a distant supervision approach to collect training data and show that direct answers are useful to train models to interpret indirect answers. |
| Outcome: | The proposed model achieves a 68% to 76% F1-score on multilingual Question-Answering benchmarks. |
Identifying and Answering Questions with False Assumptions: An Interpretable Approach (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) generate misleading answers because of hallucinations . despite their capabilities, LLMs suffer from hallucinisms, which leads to unfaithful answers . |
| Approach: | They propose a method to identify and answer questions with false assumptions . they first investigate whether the problem reduces to fact verification . then, they leverage external evidence to mitigate hallucinations . |
| Outcome: | The proposed approach reduces the problem to fact verification and provides interpretable answers by pinpointing the false assumptions. |
Are People Located in the Places They Mention in Their Tweets? A Multimodal Approach (2022.coling-1)
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| Challenge: | Experimental results show that a neural architecture that combines both modalities yields better results. |
| Approach: | They propose a neural architecture that combines both modalities to solve the problem of determining whether people are located in tweets. |
| Outcome: | The proposed model combines both modalities to produce better results . |
Can LLMs Judge Debates? Evaluating Non-Linear Reasoning via Argumentation Theory Semantics (2025.findings-emnlp)
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| Challenge: | Large Language Models excel at linear reasoning tasks but are underexplored on non-linear structures such as natural debates. |
| Approach: | They evaluate whether Large Language Models can approximate structured reasoning from Computational Argumentation Theory. |
| Outcome: | The proposed model performs well on dialogue-formatted debates without access to the underlying graph. |
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. |
NEXUS: Network Exploration for eXploiting Unsafe Sequences in Multi-Turn LLM Jailbreaks (2025.emnlp-main)
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| Challenge: | Existing approaches to large language models suffer from limited exploration of adversarial space . multi-turn jailbreaks that distribute malicious intent across benign exchanges are vulnerable . NEXUS aims to exploit the adversarials of LLMs for maximum effectiveness in jailbreak scenarios . |
| Approach: | They propose a framework for constructing, refining, and executing optimized multi-turn attacks . NEXUS builds a semantic network of thought that captures a comprehensive representation of the adversarial space . |
| Outcome: | NEXUS can achieve higher attack success rate than state-of-the-art approaches . it builds a semantic network of thought that captures a comprehensive representation of the adversarial space . |
Disentangling Indirect Answers to Yes-No Questions in Real Conversations (2022.naacl-main)
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Krishna Sanagavarapu, Jathin Singaraju, Anusha Kakileti, Anirudh Kaza, Aaron Mathews, Helen Li, Nathan Brito, Eduardo Blanco
| Challenge: | Existing models with synthetic indirect answers to yes-no questions are not beneficial when working with real conversations. |
| Approach: | They propose to annotate the underlying direct answers to yes-no questions in real conversations. |
| Outcome: | The proposed model outperforms the majority baseline but the task remains a challenge. |
Annotating Temporally-Anchored Spatial Knowledge by Leveraging Syntactic Dependencies (L18-1)
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| Challenge: | Existing approaches to extract spatial knowledge focus on extracting locations of events, someone or something. |
| Approach: | They propose a method to annotate temporally-anchored spatial knowledge on top of OntoNotes by crowdsourcing annotations. |
| Outcome: | The proposed method can be automated and validated using syntactic dependencies and crowdsourced annotations. |
Extracting Possessions from Social Media: Images Complement Language (D19-1)
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| Challenge: | Existing studies show that authors of tweets possess objects they tweet about. |
| Approach: | They propose a dataset and experiments to determine whether tweet authors possess objects they tweet about. |
| Outcome: | The proposed strategy incorporates visual information into any neural network beyond weights from pretrained networks. |
Hiding in Plain Sight: Tweets with Hate Speech Masked by Homoglyphs (2023.findings-emnlp)
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| Challenge: | Currently, there are almost 150,000 Unicode characters, which presents extensive substitution possibilities. |
| Approach: | They develop a character substitution scraping method to collect hate speech . they use an annotated dataset with 1,281 non-Latin characters to scrape out offensive words . |
| Outcome: | The proposed method can detect hate speech with annotated data, but it performs poorly in a zero-shot setting. |
Assessing the Human Likeness of AI-Generated Counterspeech (2025.coling-main)
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| Challenge: | Existing studies have focused on relevance, surface form, and other shallow linguistic characteristics. |
| Approach: | They propose to evaluate the human likeness of AI-generated counterspeech . they implement and evaluate several LLM-based generation strategies . |
| Outcome: | The proposed models show that human-written counterspeech can be distinguished by both simple classifiers and humans. |