GenAI4ELab/papercli-papers huggingface dataset:
Efficient Continual Pre-training of LLMs for Low-resource Languages
Authors: Arijit Nag; Soumen Chakrabarti; Animesh Mukherjee; Niloy Ganguly
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track))

Abstract: Open-source large language models (Os-LLMs) propel the democratization of natural language research by giving the flexibility to augment or update model parameters for performance improvement. Nevertheless, like proprietary LLMs, Os-LLMs offer poorer performance on low-resource languages (LRLs) than high-resource languages (HRLs), owing to smaller amounts of training data and underrepresented vocabulary. On the other hand, continual pre-training (CPT) with large amounts of language-specific data is a costly proposition in terms of data acquisition and computational resources. Our goal is to drastically reduce CPT cost.To that end, we first develop a new algorithm to select a subset of texts from a larger corpus. We show the effectiveness of our technique using very little CPT data. In search of further improvement, we design a new algorithm to select tokens to include in the LLM vocabulary.We experiment with the recent Llama-3 model and nine Indian languages with diverse scripts and extent of resource availability.For evaluation, we use IndicGenBench, a generation task benchmark dataset for Indic languages. We experiment with various CPT corpora and augmented vocabulary size and offer insights across language families.
DSRAG: A Double-Stream Retrieval-Augmented Generation Framework for Countless Intent Detection
Authors: Pei Guo; Enjie Liu; Ruichao Zhong; Mochi Gao; Yunzhi Tan; Bo Hu; Zang Li
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track))

Abstract: Current intent detection work experiments with minor intent categories. However, in real-world scenarios of data analysis dialogue systems, intents are composed of combinations of numerous metrics and dimensions, resulting in countless intents and posing challenges for the language model. The retrieval-augmented generation (RAG) method efficiently retrieves key intents. However, the single retrieval route sometimes fails to recall target intents and causes incorrect results. To alleviate the above challenges, we introduce the DSRAG framework combining query-to-query (Q2Q) and query-to-metadata (Q2M) double-stream RAG approaches. Specifically, we build a repository of query statements for Q2Q using the query templates with the key intents. When a user’s query comes, it rapidly matches repository statements. Once the relevant query is retrieved, the results can be quickly returned. In contrast, Q2M retrieves the relevant intents from the metadata and utilizes large language models to choose the answer. Experimental results show that DSRAG achieves significant improvements compared with merely using prompt engineering and a single retrieval route.
Octopus: On-device language model for function calling of software APIs
Authors: Wei Chen; Zhiyuan Li; Mingyuan Ma
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track))

Abstract: Large Language Models (LLMs) are pivotal for advanced text processing and generation. This study presents a framework to train a series of on-device LLMs optimized for invoking software APIs. Using a curated dataset of 30,000 API function calls from software documentation, we fine-tune LLMs with 2B, 3B, and 7B parameters to enhance their proficiency in API interactions. Our approach improves the understanding of API structures and syntax, leading to significantly better accuracy in API function calls. We also propose a conditional masking technique to enforce correct output formats, reducing errors while maintaining inference speed, specifically tailored for API tasks. The fine-tuned model, Octopus, outperforms GPT-4 in API calling tasks, showcasing advancements in automated software development and API integration. The model checkpoints are publicly available.
MoFE: Mixture of Frozen Experts Architecture
Authors: Jean Seo; Jaeyoon Kim; Hyopil Shin
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track))

Abstract: We propose the Mixture of Frozen Experts (MoFE) architecture, which integrates Parameter-efficient Fine-tuning (PEFT) and the Mixture of Experts (MoE) architecture to enhance both training efficiency and model scalability. By freezing the Feed Forward Network (FFN) layers within the MoE framework, MoFE significantly reduces the number of trainable parameters, improving training efficiency while still allowing for effective knowledge transfer from the expert models. This facilitates the creation of models proficient in multiple domains. We conduct experiments to evaluate the trade-offs between performance and efficiency, compare MoFE with other PEFT methodologies, assess the impact of domain expertise in the constituent models, and determine the optimal training strategy. The results show that, although there may be some trade-offs in performance, the efficiency gains are substantial, making MoFE a reasonable solution for real-world, resource-constrained environments.
FinLLM-B: When Large Language Models Meet Financial Breakout Trading
Authors: Kang Zhang; Osamu Yoshie; Lichao Sun; Weiran Huang
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track))

Abstract: Trading range breakout is a key method in the technical analysis of financial trading, widely employed by traders in financial markets such as stocks, futures, and foreign exchange. However, distinguishing between true and false breakout and providing the correct rationale cause significant challenges to investors. Traditional quantitative methods require large amounts of data and cannot directly present the reasoning process to users, making them less than perfect in this field. Recently, large language models have achieved success in various downstream applications, but their effectiveness in the domain of financial breakout detection has been subpar. The reason is that the unique data and specific knowledge are required in breakout detection. To address these issues, we created the first financial breakout dataset and introduce FinLLM-B, the premier large language model for financial breakout detection, which enhances the effectiveness of breakout trading strategies. Furthermore, we have developed a novel framework for large language models, namely multi-stage structure, effectively reducing mistakes in downstream applications. Experimental results indicate that compared to GPT-3.5, FinanceGPT-B improves the average accuracy of answers and rational by 49.97%, with the multi-stage structure contributing 9.72% to the improvement. Additionally, it outperforms ChatGPT-4 by 42.38%.
QueryShield: A Platform to Mitigate Enterprise Data Leakage in Queries to External LLMs
Authors: Nitin Ramrakhiyani; Delton Myalil; Sachin Pawar; Manoj Apte; Rajan M A; Divyesh Saglani; Imtiyazuddin Shaik
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track))

Abstract: Unrestricted access to external Large Language Models (LLM) based services like ChatGPT and Gemini can lead to potential data leakages, especially for large enterprises providing products and services to clients that require legal confidentiality guarantees. However, a blanket restriction on such services is not ideal as these LLMs boost employee productivity. Our goal is to build a solution that enables enterprise employees to query such external LLMs, without leaking confidential internal and client information. In this paper, we propose QueryShield - a platform that enterprises can use to interact with external LLMs without leaking data through queries. It detects if a query leaks data, and rephrases it to minimize data leakage while limiting the impact to its semantics. We construct a dataset of 1500 queries and manually annotate them for their sensitivity labels and their low sensitivity rephrased versions. We fine-tune a set of lightweight model candidates using this dataset and evaluate them using multiple metrics including one we propose specific to this problem.
SwissADT: An Audio Description Translation System for Swiss Languages
Authors: Lukas Fischer; Yingqiang Gao; Alexa Lintner; Annette Rios; Sarah Ebling
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track))

Abstract: Audio description (AD) is a crucial accessibility service provided to blind persons and persons with visual impairment, designed to convey visual information in acoustic form. Despite recent advancements in multilingual machine translation research, the lack of well-crafted and time-synchronized AD data impedes the development of audio description translation (ADT) systems that address the needs of multilingual countries such as Switzerland. Furthermore, most ADT systems rely on text alone, and it is unclear whether incorporating visual information from video clips improves the quality of ADT outputs.In this work, we introduce SwissADT, an **emerging** ADT system for three main Swiss languages and English, designed for future use by our industry partners. By collecting well-crafted AD data augmented with video clips in German, French, Italian, and English, and leveraging the power of Large Language Models (LLMs), we aim to enhance information accessibility for diverse language populations in Switzerland by automatically translating AD scripts to the desired Swiss language. Our extensive experimental ADT results, composed of both automatic and human evaluations of ADT quality, demonstrate the promising capability of SwissADT for the ADT task. We believe that combining human expertise with the generation power of LLMs can further enhance the performance of ADT systems, ultimately benefiting a larger multilingual target population.
Chinese Morph Resolution in E-commerce Live Streaming Scenarios
Authors: Jiahao Zhu; Jipeng Qiang; Ran Bai; Chenyu Liu; Xiaoye Ouyang
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track))

Abstract: E-commerce live streaming in China, particularly on platforms like Douyin, has become a major sales channel, but hosts often use morphs to evade scrutiny and engage in false advertising. This study introduces the Live Auditory Morph Resolution (LiveAMR) task to detect such violations. Unlike previous morph research focused on text-based evasion in social media and underground industries, LiveAMR targets pronunciation-based evasion in health and medical live streams. We constructed the first LiveAMR dataset with 86,790 samples and developed a method to transform the task into a text-to-text generation problem. By leveraging large language models (LLMs) to generate additional training data, we improved performance and demonstrated that morph resolution significantly enhances live streaming regulation.
MonoTODia: Translating Monologue Requests to Task-Oriented Dialogues
Authors: Sebastian Steindl; Ulrich Schäfer; Bernd Ludwig
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track))

Abstract: Data scarcity is one of the main problems when it comes to real-world applications of transformer-based models.This is especially evident for task-oriented dialogue (TOD) systems, which require specialized datasets, that are usually not readily available. This can hinder companies from adding TOD systems to their services.This study therefore investigates a novel approach to sourcing annotated dialogues from existing German monologue material.Focusing on a real-world example, we investigate whether these monologues can be transformed into dialogue formats suitable for training TOD systems.We show the approach with the concrete example of a company specializing in travel bookings via e-mail. We fine-tune state-of-the-art Large Language Models for the task of rewriting e-mails as dialogues and annotating them.To ensure the quality and validity of the generated data, we employ crowd workers to evaluate the dialogues across multiple criteria and to provide gold-standard annotations for the test dataset.We further evaluate the usefulness of the dialogues for training TOD systems.Our evaluation shows that the dialogues and annotations are of high quality and can serve as a valuable starting point for training TOD systems.Finally, we make the annotated dataset publicly available to foster future research.
MedEthicEval: Evaluating Large Language Models Based on Chinese Medical Ethics
Authors: Haoan Jin; Jiacheng Shi; Hanhui Xu; Kenny Q. Zhu; Mengyue Wu
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track))

Abstract: Large language models (LLMs) demonstrate significant potential in advancing medical applications, yet their capabilities in addressing medical ethics challenges remain underexplored. This paper introduces MedEthicEval, a novel benchmark designed to systematically evaluate LLMs in the domain of medical ethics. Our framework encompasses two key components: knowledge, assessing the models’ grasp of medical ethics principles, and application, focusing on their ability to apply these principles across diverse scenarios. To support this benchmark, we consulted with medical ethics researchers and developed three datasets addressing distinct ethical challenges: blatant violations of medical ethics, priority dilemmas with clear inclinations, and equilibrium dilemmas without obvious resolutions. MedEthicEval serves as a critical tool for understanding LLMs’ ethical reasoning in healthcare, paving the way for their responsible and effective use in medical contexts.
Predicting ICU Length of Stay for Patients using Latent Categorization of Health Conditions
Authors: Tirthankar Dasgupta; Manjira Sinha; Sudeshna Jana
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track))

Abstract: Predicting the duration of a patient’s stay in an Intensive Care Unit (ICU) is a critical challenge for healthcare administrators, as it impacts resource allocation, staffing, and patient care strategies. Traditional approaches often rely on structured clinical data, but recent developments in language models offer significant potential to utilize unstructured text data such as nursing notes, discharge summaries, and clinical reports for ICU length-of-stay (LoS) predictions. In this study, we introduce a method for analyzing nursing notes to predict the remaining ICU stay duration of patients. Our approach leverages a joint model of latent note categorization, which identifies key health-related patterns and disease severity factors from unstructured text data. This latent categorization enables the model to derive high-level insights that influence patient care planning. We evaluate our model on the widely used MIMIC-III dataset, and our preliminary findings show that it significantly outperforms existing baselines, suggesting promising industrial applications for resource optimization and operational efficiency in healthcare settings.
RevieWeaver: Weaving Together Review Insights by Leveraging LLMs and Semantic Similarity
Authors: Jiban Adhikary; Mohammad Alqudah; Arun Palghat Udayashankar
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track))

Abstract: With the rise of online retail, customer reviews have become a critical factor in shaping purchasing decisions. The sheer volume of customer reviews being generated continuously presents a challenge for consumers who must sift through an overwhelming amount of feedback. To address this issue, we introduce RevieWeaver, a novel framework that extracts key product features and provides concise review summaries. Our innovative approach not only scales efficiently to 30 million reviews but also ensures reproducibility and controllability. Moreover, it delivers unbiased and reliable assessments of products that accurately reflect the input reviews.
MedCodER: A Generative AI Assistant for Medical Coding
Authors: Krishanu Das Baksi; Elijah Soba; John J Higgins; Ravi Saini; Jaden Wood; Jane Cook; Jack I Scott; Nirmala Pudota; Tim Weninger; Edward Bowen; Sanmitra Bhattacharya
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track))

Abstract: Medical coding standardizes clinical data but is both time-consuming and error-prone. Traditional Natural Language Processing (NLP) methods struggle with automating coding due to the large label space, lengthy text inputs, and the absence of supporting evidence annotations that justify code selection. Recent advancements in Generative Artificial Intelligence (AI) offer promising solutions to these challenges. In this work, we introduce MedCodER, an emerging Generative AI framework for automatic medical coding that leverages extraction, retrieval, and re-ranking techniques as core components. MedCodER achieves a micro-F1 score of 0.62 on International Classification of Diseases (ICD) code prediction, significantly outperforming state-of-the-art methods. Additionally, we present a new dataset containing medical records annotated with disease diagnoses, ICD codes, and supporting evidence texts (https://doi.org/10.5281/zenodo.13308316). Ablation tests confirm that MedCodER’s performance depends on the integration of each of its aforementioned components, as performance declines when these components are evaluated in isolation.
Visual Zero-Shot E-Commerce Product Attribute Value Extraction
Authors: Jiaying Gong; Ming Cheng; Hongda Shen; Pierre-Yves Vandenbussche; Janet Jenq; Hoda Eldardiry
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track))

Abstract: Existing zero-shot product attribute value (aspect) extraction approaches in e-Commerce industry rely on uni-modal or multi-modal models, where the sellers are asked to provide detailed textual inputs (product descriptions) for the products. However, manually providing (typing) the product descriptions is time-consuming and frustrating for the sellers. Thus, we propose a cross-modal zero-shot attribute value generation framework (ViOC-AG) based on CLIP, which only requires product images as the inputs. ViOC-AG follows a text-only training process, where a task-customized text decoder is trained with the frozen CLIP text encoder to alleviate the modality gap and task disconnection. During the zero-shot inference, product aspects are generated by the frozen CLIP image encoder connected with the trained task-customized text decoder. OCR tokens and outputs from a frozen prompt-based LLM correct the decoded outputs for out-of-domain attribute values. Experiments show that ViOC-AG significantly outperforms other fine-tuned vision-language models for zero-shot attribute value extraction.
SCORE: Systematic COnsistency and Robustness Evaluation for Large Language Models
Authors: Grigor Nalbandyan; Rima Shahbazyan; Evelina Bakhturina
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track))

Abstract: Typical evaluations of Large Language Models (LLMs) report a single metric per dataset, often representing the model’s best-case performance under carefully selected settings. Unfortunately, this approach overlooks model robustness and reliability in real-world applications. For instance, simple paraphrasing of prompts on the MMLU-Pro dataset causes accuracy fluctuations of up to 10%, while reordering answer choices in the AGIEval dataset results in accuracy differences of up to 6.1%. While some studies discuss issues with LLM robustness, there is no unified or centralized framework for evaluating the robustness of language models. To address this gap and consolidate existing research on model robustness, we present SCORE (Systematic COnsistency and Robustness Evaluation), a comprehensive framework for non-adversarial evaluation of LLMs. The SCORE framework evaluates models by repeatedly testing them on the same benchmarks in various setups to give a realistic estimate of their accuracy and consistency. We will make the code publicly available to facilitate further development and research.
Evaluating Large Language Models with Enterprise Benchmarks
Authors: Bing Zhang; Mikio Takeuchi; Ryo Kawahara; Shubhi Asthana; Md. Maruf Hossain; Guang-Jie Ren; Kate Soule; Yifan Mai; Yada Zhu
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track))

Abstract: The advancement of large language models (LLMs) has led to a greater challenge of having a rigorous and systematic evaluation of complex tasks performed, especially in enterprise applications. Therefore, LLMs need to be benchmarked with enterprise datasets for a variety of NLP tasks. This work explores benchmarking strategies focused on LLM evaluation, with a specific emphasis on both English and Japanese. The proposed evaluation framework encompasses 25 publicly available domain-specific English benchmarks from diverse enterprise domains like financial services, legal, climate, cyber security, and 2 public Japanese finance benchmarks. The diverse performance of 8 models across different enterprise tasks highlights the importance of selecting the right model based on the specific requirements of each task. Code and prompts are available on GitHub.
Can Post-Training Quantization Benefit from an Additional QLoRA Integration?
Authors: Xiliang Zhu; Elena Khasanova; Cheng Chen
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track))

Abstract: Large language models (LLMs) have transformed natural language processing but pose significant challenges for real-world deployment. These models necessitate considerable computing resources, which can be costly and frequently unavailable. Model compression techniques such as quantization are often leveraged to alleviate resource demand, but they may have a negative impact on the generation quality. In this study, we explore the integration of 4-bit Post-training Quantization (PTQ) with QLoRA to address these issues. We demonstrate through extensive experiments that this integration outperforms standard PTQ, and in some cases even 16-bit full-parameter fine-tuning on LLMs, validated across proprietary and public datasets with different quantization algorithms. The results demonstrate the efficacy of PTQ-QLoRA integration, offering a viable solution for deploying powerful LLMs in resource-constrained environments without compromising on performance.