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Toxicity text detectors can be vulnerable to adversarial examples - small perturbations to input text that fool the systems into wrong detection. Existing attack algorithms are time-consuming and often produce invalid or ambiguous adversarial examples, making them less useful for evaluating or improving real-world toxicity content moderators. This paper proposes an annotation pipeline for quality control of generated toxic adversarial examples (TAE). We design model-based automated annotation and human-based quality verification to assess the quality requirements of \tae. Successful \tae should fool a target toxicity model into making benign predictions, be grammatically reasonable, appear natural like human-generated text, and exhibit semantic toxicity. When applying these requirements to more than 20 state-of-the-art (SOTA) TAE attack recipes, we find many invalid samples from a total of $940k$ raw TAE attack generations. We then utilize the proposed pipeline to filter and curate a high-quality TAE dataset we call TaeBench (of size $264k$). Empirically, we demonstrate that TaeBench can effectively transfer-attack SOTA toxicity content moderation models and services. Our experiments also show that TaeBench with adversarial training achieve significant improvements of the robustness of two toxicity detectors.

NAACL 2025

TaeBench: Improving Quality of Toxic Adversarial Examples

transfer attack

toxicity detection

benchmark

adversarial training

adversarial robustness

adversarial attack

dataset

poster

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

While knowledge evaluation in large language models has predominantly focused on academic subjects like math and physics, these assessments often fail to capture the practical demands of real-world professions. In this paper, we introduce IndoCareer, a dataset comprising 8,834 multiple-choice questions designed to evaluate performance in vocational and professional certification exams across various fields. With a focus on Indonesia, IndoCareer provides rich local contexts, spanning six key sectors: (1) healthcare, (2) insurance and finance, (3) creative and design, (4) tourism and hospitality, (5) education and training, and (6) law. Our comprehensive evaluation of 27 large language models shows that these models struggle particularly in fields with strong local contexts, such as insurance and finance. Additionally, while using the entire dataset, shuffling answer options generally maintains consistent evaluation results across models, but it introduces instability specifically in the insurance and finance sectors.

Cracking the Code: Multi-domain LLM Evaluation on Real-World Professional Exams in Indonesia

Medication Extraction and Mining play an important role in healthcare NLP research due to its practical applications in hospital settings, such as their mapping into standard clinical knowledge bases (SNOMED-CT, BNF, etc.).
In this work, we investigate state-of-the-art LLMs in text mining tasks on medications and their related attributes such as dosage, route, strength, and adverse effects. In addition, we explore different ensemble learning methods (\textsc{Stack-Ensemble} and \textsc{Voting-Ensemble}) to augment the model performances from individual LLMs. Our ensemble learning result demonstrated better performances than individually fine-tuned base models BERT, RoBERTa, RoBERTa-L, BioBERT, BioClinicalBERT, BioMedRoBERTa, ClinicalBERT, and PubMedBERT across general and specific domains. 
Finally, we build up an entity linking function to map extracted medical terminologies into the SNOMED-CT codes and the British National Formulary (BNF) codes, which are further mapped to the Dictionary of Medicines and Devices (dm+d), and ICD.

InsightBuddy-AI: Medication Extraction and Entity Linking using Large Language Models and Ensemble Learning

Most studies on the linguistic information used by BERT have focused on English. This study examines a monolingual German BERT model using a semantic classification task on newspaper articles, analyzing the linguistic features influencing classification decisions through SHAP values. We use a corpus with gold-standard annotations for a set of linguistic features, including POS, inflectional morphology, phrasal, clausal, and dependency structures. Semantic features of nouns are evaluated via the GermaNet ontology using shared hypernyms. Our results indicate that the features identified in English also affect classification of the German BERT model but suggests important language- and task-specific features as well.

Linguistic Features in German BERT: The Role of Morphology, Syntax, and Semantics in Multi-Class Text Classification

Neural codec language models (or codec LMs) are emerging as a powerful framework for audio generation tasks like text-to-speech (TTS). These models leverage advancements in language modeling and residual vector quantization (RVQ)-based audio codecs, which compress audios into discrete codes for LMs to process. Despite the close interdependence of codecs and LMs in these systems, research on codecs and LMs has largely remained siloed. In this work, we propose three techniques for better codec-LM co-design: (i) a frame-wise codec encoder that improves both LM log-likelihood and end-to-end TTS metrics, (ii) LM codebook level dropout, a method to efficiently navigate a portion of the codec-LM design space by training a single LM, and (iii) increased codec frame duration, which we show can accelerate inference while maintaining end-to-end performance. Our experiments demonstrate that combining all three co-design techniques results in doubled inference speed, and improvements in intelligibility, audio quality, and speaker control in TTS relative to a siloed baseline.

Towards Codec-LM Co-design for Neural Codec Language Models

Machine translation for low-resource language pairs is a challenging task. This task could become extremely difficult once a speaker uses code switching. We present the first code-switching Kazakh-Russian parallel corpus.
Additionally, we propose a method to build a machine translation model for code-switched Kazakh-Russian language pair with no labeled data. Our method is basing on generation of synthetic data. This method results in a model beating an existing commercial system by human evaluation.

Low-resource Machine Translation for Code-switched Kazakh-Russian Language Pair

In recommender systems, users often seek the~\textit{best} products through indirect, vague, or under-specified queries, such as "best shoes for trail running". Such queries, also referred to as, implicit superlative queries pose a significant challenge for standard retrieval and ranking systems, as they lack an explicit mention of attributes and require identifying and reasoning over complex attributes. In this work, we explore if Large Language Models (LLMs) can improve product recommendation for such queries. As a first step, we propose a novel four-point schema for annotating the best product candidates for superlative queries and introduce a dataset, SUPPER, paired with LLM-based product annotations. We then empirically evaluate several existing retrieval and ranking approaches on our new dataset, providing insights and discussing their integration into real-world e-commerce production systems.

Show me the best! Generative Product Recommendation for Implicit Superlative Queries

This study examines performance of Large Language Models (LLMs) in Aspect-Based Sentiment Analysis (ABSA), focusing on implicit aspect extraction in novel domains. Using a synthetic sports feedback dataset, we evaluate open-weight LLMs' ability to extract aspect-polarity pairs and propose a metric for implicit aspect extraction. Our findings reveal both the promise and limitations of LLMs in ABSA tasks.

From Annotation to Adaptation: Metrics, Synthetic Data, and Aspect Extraction for Aspect-Based Sentiment Analysis with Large Language Models

We consider the well-known and important tasks of clone detection and information retrieval for source code. The most standard setup is to search clones inside the same language code snippets. But it is also useful to find code snippets with identical behaviour in different programming languages. Nevertheless multi- and cross-lingual clone detection has been little studied in literature. We present a novel training procedure, cross-consistency training (CCT) leveraging cross-lingual similarity, that we apply to train language models on source code in various programming languages. We show that this training is effective both for encoder- and decoder-based models.
The trained encoder-based CCT-LM model
%and fine-tuned with CCT,
achieves a new state of the art on POJ-104 (monolingual C++ clone detection benchmark) with 96.73\% MAP and AdvTest (monolingual Python code search benchmark) with 47.18\% MRR. The decoder-based CCT-LM model shows comparable performance in these tasks. In addition, we formulate the multi- and cross-lingual clone detection problem and present XCD, a new benchmark dataset produced from CodeForces submissions.

CCT-Code: Cross-Consistency Training for Multilingual Clone Detection and Code Search

We introduce **SPY Dataset**: a novel synthetic dataset for the task of **Personal Identifiable Information (PII) detection**, underscoring the significance of protecting PII in modern data processing. Our research innovates by leveraging Large Language Models (LLMs) to generate a dataset that emulates real-world PII scenarios. Through evaluation, we validate the dataset's quality, providing a benchmark for PII detection. Comparative analyses reveal that while PII and Named Entity Recognition (NER) share similarities, **dedicated NER models exhibit limitations** when applied to PII-specific contexts. This work contributes to the field by making the generation methodology and the generated dataset publicly [accessible](https://anonymous.4open.science/r/SPY_Dataset), thereby enabling further research and development in this field.

SPY: Enhancing Privacy with Synthetic PII Detection Dataset

Code-generating Large Language Models (LLMs) have become essential tools in modern software development, enhancing productivity and accelerating development. This paper aims to investigate the fine-tuning of code-generating LLMs using Reinforcement Learning and Direct Preference Optimization, further improving their performance. To achieve this, we enhance the training data for the reward model with the help of symbolic execution techniques, ensuring more comprehensive and objective data. With symbolic execution, we create a custom dataset that better captures the nuances in code evaluation. Our reward models, fine-tuned on this dataset, demonstrate significant improvements over the baseline, CodeRL, in estimating the quality of generated code. Our code-generating LLMs, trained with the help of reward model feedback, achieve similar results compared to the CodeRL benchmark.

TaeBench: Improving Quality of Toxic Adversarial Examples

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