United States

Crosswalks, which map one classification system to another, are critical tools for harmonizing data across time, countries, or frameworks. However, constructing crosswalks is labor-intensive and often requires domain expertise. This paper investigates the potential of Large Language Models (LLMs) to assist in creating crosswalks, focusing on two Danish occupational classification systems from different time periods as a case study. We propose a two-stage, prompt-based framework for this task, where LLMs perform similarity assessments between classification codes and identify final mappings through a guided decision process. Using four instruction-tuned LLMs and comparing them against an embedding-based baseline, we evaluate the performance of different models in crosswalks. Our results highlight the strengths of LLMs in crosswalk creation compared to the embedding-based baseline, showing the effectiveness of the interactive prompt-based framework for conducting crosswalks by LLMs. Furthermore, we analyze the impact of model combinations across two interactive rounds, highlighting the importance of model selection and consistency. This work contributes to the growing field of NLP applications for domain-specific knowledge mapping and demonstrates the potential of LLMs in advancing crosswalk methodologies.

NAACL 2025

Can Large Language Models Advance Crosswalks? The Case of Danish Occupation Codes

occupation code

crosswalks

danish

large language models

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2022

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. 
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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Defectivity such as morphological gaps represents an intriguing and underexplored phenomenon in the study of language. Addressing defectivity, where expected inflectional forms are absent, is essential for improving the accuracy of NLP tools in morphologically rich languages. However, traditional linguistic resources often lack coverage of morphological gaps as such knowledge requires significant human expertise and effort to document and verify. For scarce linguistic phenomena in under-explored languages, Wikipedia and Wiktionary often serve as among the few accessible resources. Despite their extensive reach, their reliability has been a subject of controversy. This study customizes a novel neural morphological analyzer to annotate Latin and Italian corpora. Using the massive annotated data, crowd-sourced lists of defective verbs compiled from Wiktionary are validated computationally to assess their reliability. Our results show that while Wiktionary provides a reliable account of Italian morphological gaps, at least 7% of Latin lemmata listed as defective are unlikely to be truly defective. This discrepancy highlights potential limitations of crowd-sourced wikis as definitive sources of linguistic knowledge, particularly for less-studied phenomena and languages, despite their value as resources for rare linguistic features. By providing scalable tools and methods for quality assurance of crowd-sourced data, this work advances computational morphology and expands linguistic knowledge of morphological gaps in non-English, morphologically rich languages.

Mind the Gap: Computational Validation of Crowd-Sourced Linguistic Knowledge on Morphological Gaps of Two Related Languages

To improve the performance of sentence pair modeling tasks, we propose an additional pre-training method, also known as transfer fine-tuning, for pre-trained masked language models.
Pre-training for masked language modeling is not necessarily designed to bring semantically similar sentences closer together in the embedding space.
Our proposed method aims to improve the performance of sentence pair modeling by applying contrastive learning to pre-trained masked language models, in which sentence embeddings of paraphrase pairs are made similar to each other.
While natural language inference corpora, which are standard in previous studies on contrastive learning, are not available on a large-scale for non-English languages, our method can construct a training corpus for contrastive learning from a raw corpus and a paraphrase dictionary at a low cost.
Experimental results on four sentence pair modeling tasks revealed the effectiveness of our method in both English and Japanese.

Paraphrase-based Contrastive Learning for Sentence Pair Modeling

This paper examines how well visual language models (VLMs) understand video question answering (VideoQA) tasks and generate responses accordingly. Recently, VLMs based on Large Language Models (LLMs) have shown remarkable performance, but the processes of understanding and reasoning in VLMs remain under-explored. To tackle this challenge, we propose Video Understanding and Response Consistency Assessment, VURCA, a framework that incorporates a fine-grained question generation and answering process to measure how well the responses generated by VLMs align with what the model understands. In addition, we introduces an extended benchmark dataset, FgNExT-QA, which builds upon NExT-QA by incorporating more fine-grained VideoQA tasks. FgNExT-QA is designed to evaluate fine-grained understanding in video question answering. Through experiments, we found that VLMs demonstrate a significant lack of understanding of both videos and questions, despite their strong QA performance.

Do Video Language Models really understand the video contexts?

Artificial intelligence (AI) is transforming healthcare by improving patient care and access, especially in remote and resource-limited areas, but it also introduces risks such as biased recommendations, misdiagnoses, and inequities that disproportionately affect underserved groups. These issues often arise when AI fails to consider diverse patient characteristics and social determinants of health. To address this, we propose a human-AI storytelling framework that uses speculative narratives, similar to Black Mirror, to simulate potential unintended harms in AI-driven healthcare systems before development or deployment in the real world. By crafting realistic user stories, our approach reveals hidden biases, flawed data assumptions, and over-reliance on automation, helping developers, clinicians, and stakeholders identify risks early. These narrative ``stress tests'' improve AI fairness, transparency, and responsiveness to real-world needs, ultimately enhancing AI safety, promoting health equity, and guiding better system design.

Towards Safer AI in Healthcare: Using Storytelling to Educate About Unintended Harms of AI-Powered Tools

Text Style Transfer (TST) is the task of transforming a text to reflect a particular style while preserving its original content. Evaluating TST outputs is a multidimensional challenge, requiring the assessment of style transfer accuracy, content preservation, and naturalness. Using human evaluation is ideal but costly, same as in other NLP tasks; however, automatic metrics for TST have not received as much attention as metrics for, e.g., machine translation or summarization.
In this paper, we examine both set of existing and novel metrics from broader NLP tasks for TST evaluation, focusing on two popular subtasks—sentiment transfer and detoxification—in a multilingual context comprising English, Hindi, and Bengali. By conducting meta-evaluation through correlation with human judgments, we demonstrate the effectiveness of these metrics when used individually and in ensembles. Additionally, we investigate the potential of Large Language Models (LLMs) as tools for TST evaluation.
Our findings highlight that certain advanced NLP metrics and experimental-hybrid-techniques provide better insights than existing TST metrics for delivering more accurate, consistent, and reproducible TST evaluations.

Evaluating Text Style Transfer Evaluation: Are There Any Reliable Metrics?

The rapid spread of misinformation on social media underscores the need for effective tools to analyze public discourse. In this thesis proposal, we focus on the concept of truthfulness stance and propose a truthfulness stance detection framework that leverages large language models (LLMs) with retrieval-augmented generation (RAG) to enhance the contextual understanding of tweets in relation to claims. The framework is evaluated on a newly developed dataset and established benchmark datasets.
Experimental results demonstrate that our approach outperforms state-of-the-art methods, significantly improving the Macro-F1 score. To highlight the practical impact of truthfulness stance detection in mitigating misinformation, we showcase several real-world applications and potential future directions.

Thesis Proposal: Understanding Misinformation on Social Media Through Truthfulness Stance

We introduce CLEAR (Contrasting Textual Feedback with Experts and Amateurs for Reasoning), a novel approach to language model reasoning that leverages the strengths of a larger (expert) model and smaller (amateur) model. The expert and amateur models each provide feedback on a model's initial output and are contrasted with each other into refined feedback. This feedback is subsequently applied to iteratively improve CLEAR's responses. Across several challenging tasks, CLEAR outperforms other state-of-the-art reasoning methods, notably achieving a state-of-the-art score on the GSM8K benchmark (97.2%).

CLEAR: Contrasting Textual Feedback with Experts and Amateurs for Reasoning

In multi-turn dialogues, large language models face a critical challenge of ensuring coherence while adapting to user-specific information.. This study introduces the persona knowledge gap, the discrepancy between a model’s internal understanding and the knowledge required for coherent, personalized conversations. While prior research has recognized these gaps, computational methods for their identification and resolution remain underexplored. We propose Conversation Preference Elicitation and Recommendation (CPER), a novel framework that dynamically detects and resolves persona knowledge gaps using intrinsic uncertainty quantification and feedback-driven refinement. CPER consists of three key modules: a Contextual Understanding Module for preference extraction, a Dynamic Feedback Module for measuring uncertainty and refining persona alignment, and a Persona-Driven Response Generation module for adapting responses based on accumulated user context. We evaluate CPER on two real-world datasets: CCPE-M for preferential movie recommendations and ESConv for mental health support. Using A/B testing, human evaluators preferred CPER's responses 42% more often than baseline models in CCPE-M and 27% more often in ESConv. A qualitative human evaluation confirms that CPER's responses are preferred for maintaining contextual relevance and coherence, particularly in longer (12+ turn) conversations.

(CPER) From Guessing to Asking: An Approach to Resolving Persona Knowledge Gap in LLMs during Multi-Turn Conversations

The widespread integration of large language models (LLMs) across diverse domains, from entertainment to healthcare and banking, has underscored the pressing need to address the risks associated with artificial intelligence (AI). Indeed, as LLMs have grown in scale, new challenges have emerged, including privacy breaches, dissemination of misinformation, issues related to robustness and alignment, and concerns regarding the societal biases ingrained in these models. In response, there has been a surge in research focused on responsible AI and AI safety. To address these challenges, recent literature has introduced novel techniques for mitigating the risks associated with LLMs and testing the efficacy of safety protocols through red-teaming. Despite these efforts, concerns persist regarding the perpetuation of biases in next-generation LLMs and the lack of robustness in existing safety measures. Additionally, recent studies have highlighted the tendency of safety safeguards to over-fit certain keywords or identity terms, raising questions about their effectiveness in real-world scenarios. In this thesis proposal, we look at common evaluation practices in LLMs safety and their potential pitfalls.

Rethinking Safety Evaluation in Large Language Models: A Research Proposal

Large language models (LLMs) like GPT-4 and LLaMA-3 offer transformative potential across industries, e.g., enhancing customer service, revolutionizing medical diagnostics, or identifying crises in news articles. However, deploying LLMs faces challenges such as limited training data, high computational costs, and issues with transparency and explainability. Our research focuses on distilling compact, parameter-efficient tailored language models (TLMs) from LLMs for domain-specific tasks with comparable performance. Current approaches like knowledge distillation, fine-tuning, and model parallelism address computational efficiency but lack hybrid strategies to balance efficiency, adaptability, and accuracy. We present ANON - an adaptive knowledge distillation framework integrating knowledge distillation with adapters to generate computationally efficient TLMs without relying on labeled datasets. ANON uses cross-entropy loss to transfer knowledge from the teacher's outputs and internal representations while employing adaptive prompt engineering and a progressive distillation strategy for phased knowledge transfer. We evaluated ANON's performance in the crisis domain, where accuracy is critical and labeled data is scarce. Experiments showed that ANON outperforms recent approaches of knowledge distillation, both in terms of the resulting TLM performance and in reducing the computational costs for training and maintaining accuracy compared to LLMs for domain-specific applications.

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