United States

Self-attention has revolutionized natural language processing by capturing long-range dependencies and improving context understanding. However, it comes with high computational costs and struggles with sequential data&#39;s inherent directionality. This paper investigates and presents a simplified approach called &quot;shared weight self-attention,&quot; where a single weight matrix is used for Keys, Queries, and Values instead of separate matrices for each. This approach cuts training parameters by more than half and significantly reduces training time. Our method not only improves efficiency but also achieves strong performance on tasks from the GLUE benchmark, even outperforming the standard BERT baseline in handling noisy and out-of-domain data. Experimental results show a 66.53% reduction in parameter size within the attention block and competitive accuracy improvements of 3.81% and 1.06% over symmetric and pairwise attention-based BERT models, respectively. The source code will be publicly available.

NAACL 2025

Does Self-Attention Need Separate Weights in Transformers?

shared-self-attention

self-attention

bert

transformer

### 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

Self-attention has revolutionized natural language processing by capturing long-range dependencies and improving context understanding. However, it comes with high computational costs and struggles with sequential data's inherent directionality. This paper investigates and presents a simplified approach called "shared weight self-attention," where a single weight matrix is used for Keys, Queries, and Values instead of separate matrices for each. This approach cuts training parameters by more than half and significantly reduces training time. Our method not only improves efficiency but also achieves strong performance on tasks from the GLUE benchmark, even outperforming the standard BERT baseline in handling noisy and out-of-domain data. Experimental results show a 66.53% reduction in parameter size within the attention block and competitive accuracy improvements of 3.81% and 1.06% over symmetric and pairwise attention-based BERT models, respectively. The source code will be publicly available.

poster

Advances in Natural Language Processing (NLP) have the potential to transform HR processes, from recruitment to employee management. While recent breakthroughs in NLP have generated significant interest in its industrial applications, a comprehensive overview of how NLP can be applied across HR activities is still lacking. This paper discovers opportunities for NLP researchers and HR practitioners to harness NLP’s transformative potential in this domain. We analyze key tasks such as information extraction and text classification, and their roles in enabling core applications such as recommendation and language generation. We also identify gaps in current research and suggest holistic approaches, stressing the importance of realistic datasets to drive progress.

Natural Language Processing for Human Resources: A Survey

Enterprise customers are increasingly adopting language models for critical communication tasks, such as drafting emails, crafting sales pitches, and composing casual messages. Deploying such models across different regions requires them to understand diverse cultural and linguistic contexts and to generate safe and respectful responses. For enterprise applications, it is crucial to mitigate reputational risks, maintain trust, and ensure compliance by effectively identifying and handling unsafe or offensive language. To address this, we develop SweEval, a benchmark simulating real-world scenarios with variations in tone (positive or negative) and context (formal or informal). The prompts explicitly instruct the model to include specific swear words while completing the task. This benchmark evaluates whether LLMs comply with or resist such inappropriate instructions and assesses their alignment with ethical frameworks, cultural nuances, and language comprehension capabilities. In order to advance research in building ethically aligned AI systems for enterprise use and beyond, we will open-source this dataset upon acceptance.

SweEval: Do LLMs Really Swear? A Benchmark for Testing Limits for Enterprise Use

The retrieval-augmented generation (RAG) technique enables generative AI models to extract accurate facts from external unstructured data sources. For structured data, RAG is further augmented by function calls to query databases. This paper presents an industrial case study that implements RAG in a large financial institution's call center. The study showcases experiences and architecture for a scalable RAG deployment. It also introduces enhancements to RAG for retrieving facts from structured data sources using data embeddings, achieving low latency and high reliability. Our optimized production application demonstrates an average response time of only 7.33 seconds. Additionally, the paper compares various open-source and closed-source models for answer generation in an industrial context.

Implementing Retrieval Augmented Generation Technique on Unstructured and Structured Data Sources in a Call Center of a Large Financial Institution

Language localization is the adaptation of written content to different linguistic and cultural contexts. Ability to localize written content is crucial for global businesses to provide consistent and reliable customer experience across diverse markets. Traditional methods have approached localization as an application of machine translation (MT), but localization requires more than linguistic conversion – content needs to align with the target audience's cultural norms, linguistic nuances, and technical requirements. This difference is prominent for long-form text, where multiple facts are present in a creative choice of language. We propose a novel prompt approach for Large Languages Models (LLMs), called Break-Ideate-Generate (BrIdGe), for language localization. BrIdGe 'breaks' the source content into granular facts, 'ideates' an action plan for content creation in the target language by organizing the granular facts, and finally executes the plan to 'generate' localized content. This approach emulates the cognitive processes humans employ in writing that begin with identifying important points, followed by brainstorming on how to structure and organize the output. We evaluated the BrIdGe methodology from multiple perspectives, including impact of BrIdGe prompt on different LLMs and performance comparisons with traditional MT models and direct translation through LLMs on public benchmark and proprietary e-commerce datasets. Through human and LLM-based automated evaluations across content in multiple languages, we demonstrate effectiveness of BrIdGe in generating fluent localized content while preserving factual consistency between source and target languages.

Break-Ideate-Generate (BrIdGe): Moving beyond Translations for Localization using LLMs

Hand-crafting high quality prompts to optimize the performance of language models is a complicated and labor-intensive process. Furthermore, when migrating to newer, smaller, or weaker models (possibly due to latency or cost gains), prompts need to be updated to re-optimize the task performance. We propose Concept Distillation (CD), an automatic prompt optimization technique for enhancing weaker models on complex tasks. CD involves: (1) collecting mistakes made by weak models with a base prompt (initialization), (2) using a strong model to generate reasons for these mistakes and create rules/concepts for weak models (induction), and (3) filtering these rules based on validation set performance and integrating them into the base prompt (deduction/verification). We evaluated CD on NL2Code and mathematical reasoning tasks, observing significant performance boosts for small and weaker language models. Notably, Mistral-7B's accuracy on Multi-Arith increased by 20%, and Phi-3-mini-3.8B's accuracy on HumanEval rose by 34%. Compared to other automated methods, CD offers an effective, cost-efficient strategy for improving weak models' performance on complex tasks and enables seamless workload migration across different language models without compromising performance.

Concept Distillation from Strong to Weak Models via Hypotheses-to-Theories Prompting

Large Language Models (LLMs) offer the potential to automate hiring by matching job descriptions with candidate resumes, streamlining recruitment processes, and reducing operational costs. However, biases inherent in these models may lead to unfair hiring practices, reinforcing societal prejudices and undermining workplace diversity. This study examines the performance and fairness of LLMs in job-resume matching tasks within the English language and U.S. context. It evaluates how factors such as gender, race, and educational background influence model decisions, providing critical insights into the fairness and reliability of LLMs in HR applications.
Our findings indicate that while recent models have reduced biases related to explicit attributes like gender and race, implicit biases concerning educational background remain significant. These results highlight the need for ongoing evaluation and the development of advanced bias mitigation strategies to ensure equitable hiring practices when using LLMs in industry settings.

Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Education

General vision-language models (VLMs) trained on web data struggle to understand and converse about real-world e-commerce product images. We propose a cost-efficient approach for collecting training data to train a generative VLM for e-commerce product images. The key idea is to leverage large-scale, loosely-coupled image-text pairs from e-commerce stores, use a pretrained LLM to generate multimodal instruction-following data, and fine-tune a general vision-language model using LoRA. Our instruction-finetuned model, VIT-Pro, can understand and respond to queries about product images, covering diverse concepts and tasks. VIT-Pro outperforms several general-purpose VLMs on multiple vision tasks in the e-commerce domain.

VIT-Pro: Visual Instruction Tuning for Product Images

Spoken Named Entity Recognition (NER) aims
to extracting named entities from speech and
categorizing them into types like person, location, organization, etc. In this work, we present
VietMed-NER - the first spoken NER dataset
in the medical domain. To our best knowledge, our real-world dataset is the largest spoken NER dataset in the world in terms of the
number of entity types, featuring 18 distinct
types. Furthermore, we present baseline results
using various state-of-the-art pre-trained models: encoder-only and sequence-to-sequence;
and conduct quantitative and qualitative error
analysis. We found that pre-trained multilingual models generally outperform monolingual
models on reference text and ASR output, encoders outperform sequence-to-sequence models in NER tasks, and pre-trained audio data
affects ASR output but not NER performance
in the cascaded setting. By simply translating,
the dataset is applicable not just to Vietnamese
but to other languages as well. All code, data
and models are made publicly available here.

Medical Spoken Named Entity Recognition

This paper analyses the safety of Large Language Models (LLMs) in interactions with children below age of 18 years. Despite the transformative applications of LLMs in various aspects of children’s lives, such as education and therapy, there remains a significant gap in understanding and mitigating potential content harms specific to this demographic. The study acknowledges the diverse nature of children, often overlooked by standard safety evaluations, and proposes a comprehensive approach to evaluating LLM safety specifically for children. We introduce a novel Harm Taxonomy to classify potential risks that children may encounter when using LLM-powered applications. Additionally, we develop Child User Models that reflect the varied personalities and interests of children, informed by literature in childcare and psychology. These user models aim to bridge the existing gap in child safety literature across various fields. We utilize Child User Models to evaluate the safety of six state-of-the-art LLMs. Our observations reveal significant safety gaps in LLMs, particularly in categories harmful to children but not adults.

LLM Safety for Children

In real-world applications with Large Language Models (LLMs), external retrieval mechanisms—such as Search-Augmented Generation (SAG), tool utilization, and Retrieval-Augmented Generation (RAG)—are often employed to enhance the quality of augmented generations in dialogues. These approaches often come with multi-turn dialogue, where each interaction is enriched by relevant information retrieved from external sources. Existing benchmarks either assess LLMs' chat abilities in multi-turn dialogues or their use of retrieval for augmented responses in limited tasks such as knowledge QA or numeric reasoning. To address this gap, we introduce **RAD**-Bench (**R**etrieval **A**ugmented **D**ialogue), a comprehensive benchmark designed to evaluate LLMs' capabilities in multi-turn dialogues following retrievals. RAD-Bench evaluates two key abilities of LLMs: Retrieval Synthesis and Retrieval Reasoning over 6 representative scenarios, concluded from analysis of real-world tasks. By employing discriminative questions, retrieved contexts, and reference answers, our evaluation of prevalent LLMs reveals performance degradation as additional layers of conditions or constraints are applied across conversation turns, even when accurate retrieved contexts are provided.

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Natural Language Processing for Human Resources: A Survey

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