Austria

Homelessness is a persistent issue, impacting millions worldwide, and over 770,000 people experienced homelessness in the U.S. in 2024. Social stigmatization is a significant barrier to alleviation, shifting public perception, and influencing policy. Online discourse on platforms such as Reddit shape public opinion. To address this, the project leverages natural language processing and large language models (LLMs) to mitigate bias against people experiencing homelessness (PEH) in online spaces. The goal is to promote awareness, reduce harmful biases, inform policy, and improve the fairness of generative AI. We gather Reddit data for 10 U.S. cities, then perform zero-shot classification, and finally, mitigation using Llama 3.2 Instruct and Qwen 2.5 7B Instruct models. The initial results highlighted the differing classifications between models and indicated that many mitigated outputs remained biased. This suggests the need for potential model refinement for the mitigation of text related to PEH.

ACL 2025

Local LLM Zero-Shot Analysis of Homelessness Discourse on Reddit

people experiencing homelessness

large language models

natural language processing

## Welcome to EMNLP 2024! 
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EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

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### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP), but their success remains largely confined to high-resource, general-purpose domains. In contrast, applying LLMs to low-resource domains poses significant challenges due to limited training data, domain drift, and strict terminology constraints. This survey provides an overview of the current landscape in domain-specific, low-resource QA with LLMs. We begin by analyzing the coverage and representativeness of specialized-domain QA datasets against large-scale reference datasets what we refer to as ParentQA. Building on this analysis, we survey data-centric strategies to enhance input diversity, including data augmentation techniques. We further discuss evaluation metrics for specialized tasks and consider ethical concerns. By mapping current methodologies and outlining open research questions, this survey aims to guide future efforts in adapting LLMs for robust and responsible use in resource-constrained, domain-specific environments.

QA Analysis in Medical and Legal Domains: A Survey of Data Augmentation in Low-Resource Settings

With Large Language Model (LLM)-based applications becoming more common due to strong performance across many tasks, prompt optimization has emerged as a way to extract better solutions from frozen, often commercial LLMs that are not specifically adapted to a task. LLM-assisted prompt optimization methods provide a promising alternative to manual/human prompt engineering, where LLM “reasoning” can be used to make them optimizing agents. However, the cost of using LLMs for prompt optimization via commercial APIs remains high, especially for heuristic methods like evolutionary algorithms (EAs), which need many iterations to converge, and thus, tokens, API calls, and rate-limited network overhead. We propose GenDLN, an efficient genetic algorithm-based prompt pair optimization framework that leverages commercial API free tiers. Our approach allows teams with limited resources (NGOs, non-profits, academics…) to efficiently use commercial LLMs for EA-based prompt optimization. We conduct experiments on CLAUDETTE for legal terms of service classification and MRPC for paraphrase detection, performing in line with selected prompt optimization baselines, at no cost. Our code is available in <omitted>.

GenDLN: Evolutionary Algorithm-Based Stacked LLM Framework for Joint Prompt Optimization

Sign language segmentation focuses on identifying temporal boundaries within sign language videos. As compared to previous segmentation techniques that have depended on frame-level and phrase-level segmentation, our study emphasizes on subtitle-level segmentation, using synchronized subtitle data to facilitate temporal boundary recognition. Based on Beginning-Inside-Outside (BIO) tagging for subtitle unit delineation, we train a sequence-to-sequence (Seq2Seq) model with and without attention for subtitle boundary identification. Training on optical flow data and aligned subtitles from BOBSL and YouTube-ASL, we show that the Seq2Seq model with attention outperforms baseline models, achieving improved percentage of segments, F1 and IoU score. An additional contribution is the development of an method for subtitle temporal resolution, aiming to facilitate manual annotation.

Sign Language Video Segmentation Using Temporal Boundary Identification

Large Language Models (LLMs) can enhance the performance of Named Entity Recognition (NER) tasks by leveraging external knowledge through in-context learning. When it comes to entity-type-related external knowledge, existing methods mainly provide LLMs with semantic information such as the definition and annotation guidelines of an entity type, leaving the effect of orthographic or morphological information on LLM-based NER unexplored. Besides, it is non-trivial to obtain literal patterns written in natural language to serve LLMs. In this work, we propose LiP-NER, an LLM-based NER framework that utilizes \textbf{Li}teral \textbf{P}atterns, the entity-type-related knowledge that directly describes the orthographic and morphological features of entities. We also propose an LLM-based method to automatically acquire literal patterns, which requires only several sample entities rather than any annotation example, thus further reducing human labor. Our extensive experiments suggest that literal patterns can enhance the performance of LLMs in NER tasks. In further analysis, we found that entity types with relatively standardized naming conventions but limited world knowledge in LLMs, as well as entity types with broad and ambiguous names or definitions yet low internal variation among entities, benefit most from our approach. We found that the most effective written literal patterns are (1) detailed in classification, (2) focused on majority cases rather than minorities, and (3) explicit about obvious literal features.

LIP-NER: Literal Patterns Benefit LLM-Based NER

It is widely assumed that Large Language Models (LLMs) are shaping language, with multiple studies noting the growing presence of LLM-generated content and suggesting homogenizing effects. However, it remains unclear if these effects are already evident in recent writing. This study addresses that gap by comparing two datasets of English online news articles -- one from 2018, prior to LLM popularization, and one from 2024, after widespread LLM adoption. We define lexical homogenization as a decrease in lexical diversity, measured by the MATTR, Maas, and MTLD metrics, and introduce the LLM-Style-Word Ratio (SWR) to measure LLM influence. We found higher MTLD and SWR scores, yet negligible changes in Maas and MATTR scores in 2024 corpus. We conclude that while there is an apparent influence of LLMs on written online English, homogenization effects do not show in the measurements. We therefore propose to apply different metrics to measure lexical homogenization in future studies on the influence of LLM usage on language change.

Testing English News Articles for Lexical Homogenization Due to Widespread Use of Large Language Models

Producing trustworthy and reliable Large Language Models (LLMs) has become increasingly important as their usage becomes more widespread. Calibration seeks to achieve this by improving the alignment between the model's confidence and the actual likelihood of its responses being correct or desirable. However, it has been observed that the internal confidence of a model, derived from token probabilities, is not well aligned with its verbalized confidence, leading to misleading results with different calibration methods. In this paper, we propose Direct Confidence Alignment (DCA), a method using Direct Preference Optimization to align an LLM’s verbalized confidence with its internal confidence rather than ground-truth accuracy, enhancing model transparency and reliability by ensuring closer alignment between the two confidence measures. We evaluate DCA across multiple open-weight LLMs on a wide range of datasets. To further assess this alignment, we also introduce three new calibration error-based metrics. Our results show that DCA improves alignment metrics on certain model architectures, reducing inconsistencies in a model's confidence expression. However, we also show that it can be ineffective on others, highlighting the need for more model-aware approaches in the pursuit of more interpretable and trustworthy LLMs.

Direct Confidence Alignment: Aligning Verbalized Confidence with Internal Confidence In Large Language Models

Static and outdated datasets hinder the accuracy of Financial Sentiment Analysis (FSA) in capturing rapidly evolving market sentiment. We tackle this by proposing a novel data augmentation technique using Retrieval Augmented Generation (RAG). Our method leverages a generative LLM to infuse established benchmarks with up-to-date contextual information from contemporary financial news. While this RAG-based augmentation significantly modernizes the data's alignment with current financial language, we employ a robust BERT-BiGRU judge model to ensure the sentiment of the original annotations is faithfully preserved. Crucially, FSA models trained on this enriched data exhibit enhanced performance on unseen test sets, demonstrating the practical value of our approach for developing more reliable and current sentiment classifiers.

Bridging the Data Gap in Financial Sentiment: LLM-Driven Augmentation

Voice conversion has emerged as a pivotal technology in numerous applications ranging from assistive communication to entertainment. In this paper, we present RT-VC, a zero-shot real-time voice conversion system that delivers ultra-low latency and high-quality performance. Our approach leverages an articulatory feature space to naturally disentangle content and speaker characteristics, facilitating more robust and interpretable voice transformations. Additionally, the integration of differentiable digital signal processing (DDSP) enables efficient vocoding directly from articulatory features, significantly reducing conversion latency. Experimental evaluations demonstrate that, while maintaining synthesis quality comparable to the current state-of-the-art (SOTA) method, RT-VC achieves a CPU latency of 61.4 ms, representing a 13.3% reduction in latency. Project page: https://berkeley-speech-group.github.io/RT-VC/

RT-VC: Real-Time Zero-Shot Voice Conversion with Speech Articulatory Coding

Machine translation (MT) systems that support low-resource languages often struggle on specialized domains. While researchers have proposed various techniques for domain adaptation, these approaches typically require model fine-tuning, making them impractical for non-technical users and small organizations. To address this gap, we propose Tulun, a versatile solution for terminology-aware translation, combining neural MT with large language model (LLM)-based post-editing guided by existing glossaries and translation memories.
Our open-source web-based platform enables users to easily create, edit, and leverage terminology resources, fostering a collaborative human-machine translation process that respects and incorporates domain expertise while increasing MT accuracy.
Evaluations show effectiveness in both real-world and benchmark scenarios: on medical and disaster relief translation tasks for Tetun and Bislama, our system achieves improvements of 16.90-22.41 ChrF++ points over baseline MT systems. Across six low-resource languages on the FLORES dataset, Tulun outperforms both standalone MT and LLM approaches, achieving an average improvement of 2.8 ChrF points over NLLB-54B. Tulun is publicly accessible at https://bislama-trans.rapha.dev.

Tulun: Transparent and Adaptable Low-resource Machine Translation

DEEP is a bidirectional translation system for the Italian Sign Language, tailored to two specific, common use cases: pharmacy and the registry office of the municipality, for which a custom corpus has been collected. Two independent pipelines permit to create a chat-like interaction style, where the deaf subject just signs in front of a camera, and see a virtual LIS interpreter, while the hearing subject read and write messages into a chat UI. The LIS-to-Italian pipeline leverages, in a novel way, a customized Whisper model (a well-known speech recognition system), by means of "pseudo-spectrograms''. The Italian-to-LIS pipeline leverages a virtual avatar created with Viggle.ai. DEEP has been evaluated with LIS signers, obtaining very promising results.

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QA Analysis in Medical and Legal Domains: A Survey of Data Augmentation in Low-Resource Settings

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