Papers by Anthony Rios
Reflective Agreement: Combining Self-Mixture of Agents with a Sequence Tagger for Robust Event Extraction (2025.emnlp-main)
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| Challenge: | Existing methods for event extraction are limited in their ability to recall nuanced or rare events. |
| Approach: | They propose a hybrid approach that leverages a self-mixture of agents and a discriminative sequence tagger to resolve ambiguities and enhance overall event prediction quality. |
| Outcome: | The proposed approach outperforms existing state-of-the-art methods across three benchmark datasets. |
A Comprehensive Study of Gender Bias in Chemical Named Entity Recognition Models (2024.naacl-long)
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| Challenge: | Chemical named entity recognition (NER) models are used in many downstream tasks, but it is unknown whether they work the same for everyone. |
| Approach: | They develop a framework for measuring gender bias in chemical NER models . they analyze a corpus of 92,405 words with self-identified gender information from reddit . |
| Outcome: | The proposed framework measures gender bias in chemical NER models using synthetic data and a newly annotated corpus of over 92,405 words with self-identified gender information from Reddit. |
Does It Run and Is That Enough? Revisiting Text-to-Chart Generation with a Multi-Agent Approach (2025.findings-emnlp)
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| Challenge: | 15% of generated scripts fail to execute, even after supervised fine-tuning and reinforcement learning. |
| Approach: | They propose a lightweight multi-agent pipeline that separates drafting, execution, repair, and judgment . the system reduces execution errors to 4.5% within three repair iterations . |
| Outcome: | The proposed pipeline reduces execution errors to 4.5% within three repair iterations while requiring significantly less compute. |
EMR Coding with Semi-Parametric Multi-Head Matching Networks (N18-1)
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| Challenge: | Electronic medical record (EMR) coding is the process of extracting diagnosis and procedure codes from the digital record (the EMR) pertaining to a patient's visit. |
| Approach: | They propose a neural network architecture that combines ideas from few-shot learning matching networks, multi-label loss functions, and convolutional neural networks for text classification to significantly outperform other state-of-the-art models. |
| Outcome: | The proposed model outperforms existing models on a well known de-identified EMR dataset with multi-label performance measures. |
Telling Speculative Stories to Help Humans Imagine the Harms of Healthcare AI (2026.findings-acl)
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| Challenge: | Artificial intelligence (AI) is rapidly transforming healthcare but can also introduce risks, including bias, privacy violations, and unequal access. |
| Approach: | They propose a framework that generates speculative user stories and supports multi-agent discussions to help people reflect on potential benefits and harms of healthcare AI before deployment. |
| Outcome: | The framework generates speculative user stories and supports multi-agent discussions to help people reflect on potential benefits and harms of healthcare AI before deployment. |
Role-Conditioned Refusals: Evaluating Access Control Reasoning in Large Language Models (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) blur role boundaries by producing unrestricted responses. |
| Approach: | They propose to extend the Spider and BIRD text-to-SQL datasets with real-time PostgreSQl role-based policies at the table and column levels. |
| Outcome: | The proposed model improves refusal precision and lowers false permits. |
Measuring Geographic Performance Disparities of Offensive Language Classifiers (2022.coling-1)
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| Challenge: | Recent work shows that text classifiers are biased regarding different languages and dialects. |
| Approach: | They propose to use a dataset to examine whether language, dialect, and topical content vary across geographical regions to address these gaps. |
| Outcome: | The proposed dataset includes 14 thousand examples across 15 cities and shows that current models do not generalize across locations. |
An Empirical Study of the Downstream Reliability of Pre-Trained Word Embeddings (2020.coling-main)
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| Challenge: | Pre-trained word embeddings have been shown to improve the performance of neural networks across a wide variety of tasks. |
| Approach: | They propose two new metrics to understand the downstream reliability of word embeddings. |
| Outcome: | The proposed model can improve performance with slight changes to the training data, but it can also fail with multiple neural network architectures. |
How Many Users Are Enough? Exploring Semi-Supervision and Stylometric Features to Uncover a Russian Troll Farm (D19-50)
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| Challenge: | Social media has been used by troll farms to promote political agendas . trolled farms employ people to provoke conflict via the use of inflammatory or provocative comments. |
| Approach: | They analyze the use of self-supervision with less than 100 troll accounts as training data to determine whether a trolled account is labeled as a Russian trol farm. |
| Outcome: | The proposed methods improve classification performance by nearly 4% F1 and use self-supervision with less than 100 troll accounts as training data. |
Unmasking Database Vulnerabilities: Zero-Knowledge Schema Inference Attacks in Text-to-SQL Systems (2025.findings-naacl)
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| Challenge: | Existing text-to-SQL systems rely on database schema information for SQL generation, exposing them to security vulnerabilities. |
| Approach: | They propose a framework for reconstructing database schema of text-to-SQL models without prior knowledge of the database. |
| Outcome: | The proposed framework achieves high accuracy in reconstructing table names, with F1 scores of up to .99 for generative models and .78 for fine-tuned models, underscoring the severity of schema leakage risks. |
Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces (D18-1)
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| Challenge: | Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group) and labels that never appear in the training dataset (zero-shot groups). |
| Approach: | They perform a fine-grained evaluation to understand how state-of-the-art methods perform on infrequent labels. |
| Outcome: | The proposed methods improve on two publicly available datasets for multi-label text classification. |
A Multi-Agent Framework for Mitigating Dialect Biases in Privacy Policy Question-Answering Systems (2025.acl-long)
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Đorđe Klisura, Astrid R Bernaga Torres, Anna Karen Gárate-Escamilla, Rajesh Roshan Biswal, Ke Yang, Hilal Pataci, Anthony Rios
| Challenge: | Existing Privacy Policy Question Answering systems exhibit performance disparities across English dialects, disadvantaging speakers of non-standard varieties. |
| Approach: | They propose a framework that integrates a Dialect Agent and a Privacy Policy Agent to mitigate dialectal biases. |
| Outcome: | The proposed framework improves GPT-4o-mini’s zero-shot accuracy from 0.394 to 0.601 on PrivacyQA and 0.352 to 0.464 on PolicyQA. |
Detecting Bot-Generated Text by Characterizing Linguistic Accommodation in Human-Bot Interactions (2021.findings-acl)
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| Challenge: | Language generation models' democratization makes it easier to generate human-like text at-scale for nefarious activities, from spreading misinformation to targeting specific groups with hate speech. |
| Approach: | They propose to use linguistic alignment to detect bot-generated text rather than using it directly. |
| Outcome: | The proposed methods are more robust across datasets and models if they use information about how people respond to it rather than using the bot's text directly. |
Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations (2025.coling-main)
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| Challenge: | Existing evaluation methods rely on human judgment to assess data accuracy and visual communication, which is costly and unscalable. |
| Approach: | They propose a framework that leverages Visual Question Answering (VQA) models to automate the evaluation of LLM-generated data visualizations. |
| Outcome: | The proposed framework assesses data representation quality and communicative clarity of charts using two leading VQA benchmark datasets, ChartQA and PlotQA, with visualizations generated by OpenAI’s GPT-3.5 Turbo and Meta’s Llama 3.1 70B-Instruct models. |
Extracting Biomedical Entities from Noisy Audio Transcripts (2024.lrec-main)
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Nima Ebadi, Kellen Morgan, Adrian Tan, Billy Linares, Sheri Osborn, Emma Majors, Jeremy Davis, Anthony Rios
| Challenge: | Named Entity Recognition (NER) is particularly affected by noise, often termed the ASR-NLP gap. |
| Approach: | They propose a dataset to bridge the ASR-NLP gap in the biomedical domain by extracting adverse drug reactions and mentions of entities from the Brief Test of Adult Cognition by Telephone (BTACT) exam. |
| Outcome: | The proposed method can clean 2,000 clean and noisy recordings and eliminate errors using zero-shot and few-shot methods. |