ymoslem/acl-abstracts huggingface dataset:
A Learning-based Multi-Frame Visual Feature Framework for Real-Time Driver Fatigue Detection (2025)

Driver fatigue is a significant factor contributing to road accidents, highlighting the need for reliable and accurate detection methods. In this study, we introduce a novel learning-based multi-frame visual feature framework (LMVFF) designed for precise fatigue detection. Our methodology comprises several clear and interpretable steps. Initially, facial landmarks are detected, enabling the calculation of distances between eyes, lips, and the assessment of head rotation angles based on 68 identified landmarks. Subsequently, visual features from the eye region are extracted, and an effective visual model is developed to accurately classify eye openness. Additionally, features characterizing lip movements are analyzed to detect yawning, thereby enriching fatigue detection through continuous monitoring of eye blink frequency, yawning occurrences, and head movements. Compared to conventional single-feature detection approaches, LMVFF significantly reduces instances of fatigue misidentification. Moreover, we employ various quantization and compression techniques for multiple computation stages, substantially reducing the latency of our system and achieving a real-time frame rate of 25-30 FPS for practical applications.
TRUSTEVAL: A Dynamic Evaluation Toolkit on Trustworthiness of Generative Foundation Models (2025)

Ensuring the trustworthiness of Generative Foundation Models (GenFMs) is a pressing challenge as they gain widespread use. Existing evaluation toolkits are often limited in scope, dynamism, and flexibility. This paper introduces TRUSTEVAL, a dynamic and comprehensive toolkit designed for evaluating GenFMs across various dimensions. TRUSTEVAL supports both dynamic dataset generation and evaluation, offering advanced features including comprehensiveness, usability, and flexibility. TRUSTEVAL integrates diverse generative models, datasets, evaluation methods, metrics, inference efficiency enhancement, and evaluation report generation. Through case studies, we demonstrate TRUSTEVAL`s potential to advance the trustworthiness evaluation of GenFMs.
AutoClean: LLMs Can Prepare Their Training Corpus (2025)

Recent studies highlight the reliance of Large Language Models (LLMs) on high-quality, diverse data for optimal performance. The data sourced from the Internet often aggregated into datasets like the Common Crawl corpus, presents significant quality variability and necessitates extensive cleaning. Moreover, specific domain knowledge is usually presented in HTML, but there is a lack of effective methods to clean them into the training corpus automatically. Traditional cleaning methods involve either labor-intensive human teams that lack scalability or static heuristics that lead to suboptimal outcomes and are unable to be applied to specific target domains. In this paper, inspired by the recent progress in employing LLMs as versatile agents for diverse tasks, we take the initiative to explore the potential of these agents in automating data-cleaning methodologies. By configuring LLMs as an agent team that imitates the human data-cleaning team, we can automatically generate cleaning rules that traditionally require the involvement of data-cleaning experts. These rules are developed using a limited number of data samples and can then be applied broadly to substantial portions of raw data from the same domain. We demonstrate the efficiency and effectiveness of on both pre-train scale corpora such as Common Crawl and specific target websites. Both automatic and human evaluations of the quality of the cleaned content highlight the feasibility of using LLMs to prepare their training corpus.
SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages (2025)

Large Language Models (LLMs) have shown remarkable abilities across various tasks, yet their development has predominantly centered on high-resource languages like English and Chinese, leaving low-resource languages underserved. To address this disparity, we present SeaLLMs 3, the latest iteration of the SeaLLMs model family, tailored for Southeast Asian languages. This region, characterized by its rich linguistic diversity, has lacked adequate language technology support. SeaLLMs 3 aims to bridge this gap by covering a comprehensive range of languages spoken in this region, including English, Chinese, Indonesian, Vietnamese, Thai, Tagalog, Malay, Burmese, Khmer, Lao, Tamil, and Javanese. Leveraging efficient language enhancement techniques and a specially constructed instruction tuning dataset, SeaLLMs 3 significantly reduces training costs while maintaining high performance and versatility. Our model excels in tasks such as world knowledge, mathematical reasoning, translation, and instruction following, achieving state-of-the-art performance among similarly sized models. Additionally, we prioritized safety and reliability by addressing both general and culture-specific considerations and incorporated mechanisms to reduce hallucinations. This work underscores the importance of inclusive AI, showing that advanced LLM capabilities can benefit underserved linguistic and cultural communities.
Prompto: An open source library for asynchronous querying of LLM endpoints (2025)

Recent surge in Large Language Model (LLM) availability has opened exciting avenues for research. However, efficiently interacting with these models presents a significant hurdle since LLMs often reside on proprietary or self-hosted API endpoints, each requiring custom code for interaction. Conducting comparative studies between different models can therefore be time-consuming and necessitate significant engineering effort, hindering research efficiency and reproducibility. To address these challenges, we present prompto, an open source Python library which facilitates asynchronous querying of LLM endpoints enabling researchers to interact with multiple LLMs concurrently, while maximising efficiency and utilising individual rate limits. Our library empowers researchers and developers to interact with LLMs more effectively and allowing faster experimentation, data generation and evaluation. prompto is released with an introductory video (https://youtu.be/lWN9hXBOLyQ) under MIT License and is available via GitHub (https://github.com/alan-turing-institute/prompto).
ESPnet-SpeechLM: An Open Speech Language Model Toolkit (2025)

We present ESPnet-SpeechLM, an open toolkit designed to democratize the development of speech language models (SpeechLMs) and voice-driven agentic applications. The toolkit standardizes speech processing tasks by framing them as universal sequential modeling problems, encompassing a cohesive workflow of data preprocessing, pre-training, inference, and task evaluation. With ESPnet-SpeechLM, users can easily define task templates and configure key settings, enabling seamless and streamlined SpeechLM development. The toolkit ensures flexibility, efficiency, and scalability by offering highly configurable modules for every stage of the workflow. To illustrate its capabilities, we provide multiple use cases demonstrating how competitive SpeechLMs can be constructed with ESPnet-SpeechLM, including a 1.7B-parameter model pre-trained on both text and speech tasks, across diverse benchmarks. The toolkit and its recipes are fully transparent and reproducible at: https://github.com/espnet/espnet/tree/speechlm.
InspectorRAGet: An Introspection Platform for RAG Evaluation (2025)

Large Language Models (LLM) have become a popular approach for implementing Retrieval Augmented Generation (RAG) systems, and a significant amount of effort has been spent on building good models and metrics. In spite of increased recognition of the need for rigorous evaluation of RAG systems, few tools exist that go beyond the creation of model output and automatic calculation. We present InspectorRAGet, an introspection platform for performing a comprehensive analysis of the quality of RAG system output. InspectorRAGet allows the user to analyze aggregate and instance-level performance of RAG systems, using both human and algorithmicmetrics as well as annotator quality. InspectorRAGet is suitable for multiple use cases and is available publicly to the community.A live instance of the platform is available at https://ibm.biz/InspectorRAGet
Cerebrum (AIOS SDK): A Platform for Agent Development, Deployment, Distribution, and Discovery (2025)

Autonomous LLM-based agents have emerged as a powerful paradigm for complex task execution, yet the field lacks standardized tools for development, deployment, and distribution. We present Cerebrum, an open-source platform that addresses this gap through three key components: (1) a comprehensive SDK featuring a modular four-layer architecture for agent development, encompassing LLM, memory, storage, and tool management; (2) a community-driven Agent Hub for sharing and discovering agents, complete with version control and dependency management; and (3) an interactive web interface for testing and evaluating agents. The platform`s effectiveness is demonstrated through implementations of various agent architectures, including Chain of Thought (CoT), ReAct, and tool-augmented agents. Cerebrum advances the field by providing a unified framework that standardizes agent development while maintaining flexibility for researchers and developers to innovate and distribute their work. Live url for demo can be found at https://app.aios.foundation. Code can be found at https://github.com/agiresearch/Cerebrum. Video demo can be found at https://app.aios.foundation/video-demo.
GenSim: A General Social Simulation Platform with Large Language Model based Agents (2025)

With the rapid advancement of large language models (LLMs), recent years have witnessed many promising studies on leveraging LLM-based agents to simulate human social behavior. While prior work has demonstrated significant potential across various domains, much of it has focused on specific scenarios involving a limited number of agents and has lacked the ability to adapt when errors occur during simulation. To overcome these limitations, we propose a novel LLM-agent-based simulation platform called GenSim, which: (1) Abstracts a set of general functions to simplify the simulation of customized social scenarios; (2) Supports one hundred thousand agents to better simulate large-scale populations in real-world contexts; (3) Incorporates error-correction mechanisms to ensure more reliable and long-term simulations. To evaluate our platform, we assess both the efficiency of large-scale agent simulations and the effectiveness of the error-correction mechanisms. To our knowledge, GenSim represents an initial step toward a general, large-scale, and correctable social simulation platform based on LLM agents, promising to further advance the field of social science.
Semi-automatic Sequential Sentence Classification in the Discourse Analysis Tool Suite (2025)

This paper explores an AI-assisted approach to sequential sentence annotation designed to enhance qualitative data analysis (QDA) workflows within the open-source Discourse Analysis Tool Suite (DATS) developed at our university.We introduce a three-phase Annotation Assistant that leverages the capabilities of large language models (LLMs) to assist researchers during annotation.Based on the number of annotations, the assistant employs zero-shot prompting, few-shot prompting, or fine-tuned models to provide the best suggestions.To evaluate this approach, we construct a benchmark with five diverse datasets.We assess the performance of three prominent open-source LLMs {---} Llama 3.1, Gemma 2, and Mistral NeMo {---} and a sequence tagging model based on SentenceTransformers.Our findings demonstrate the effectiveness of our approach, with performance improving as the number of annotated examples increases. Consequently, we implemented the Annotation Assistant within DATS and report the implementation details.With this, we hope to contribute to a novel AI-assisted workflow and further democratize access to AI for qualitative data analysis.
CowPilot: A Framework for Autonomous and Human-Agent Collaborative Web Navigation (2025)

While much work on web agents emphasizes the promise of autonomously performing tasks on behalf of users, in reality, agents often fallshort on complex tasks in real-world contexts and modeling user preference. This presents an opportunity for humans to collaborate with the agent and leverage the agent`s capabilities effectively. We propose CowPilot, a frame- work supporting autonomous as well as human-agent co llaborative w eb navigation, and evaluation across task success and task efficiency. CowPilot reduces the number of steps humans need to perform by allowing agents to propose next steps, while users are able to pause, reject, or take alternative actions. During execution, users can interleave their actions with the agent`s by overriding suggestions or resuming agent control when needed. We conducted case studies on five common websites and found that the human-agent collaborative mode achieves the highest success rate of 95{\%} while requiring humans to perform only 15.2{\%} of the total steps. Even with human interventions during task execution, the agent successfully drives up to half of task success on its own. CowPilot can serve as a useful tool for data collection and agent evaluation across websites, which we believe will enable research in how users and agents can work together. Video demonstrations are available at https://oaishi.github.io/cowpilot.html
eRevise+RF: A Writing Evaluation System for Assessing Student Essay Revisions and Providing Formative Feedback (2025)

The ability to revise essays in response to feedback is important for students' writing success. An automated writing evaluation (AWE) system that supports students in revising their essays is thus essential. We present eRevise+RF, an enhanced AWE system for assessing student essay revisions (e.g., changes made to an essay to improve its quality in response to essay feedback) and providing revision feedback. We deployed the system with 6 teachers and 406 students across 3 schools in Pennsylvania and Louisiana. The results confirmed its effectiveness in (1) assessing student essays in terms of evidence usage, (2) extracting evidence and reasoning revisions across essays, and (3) determining revision success in responding to feedback. The evaluation also suggested eRevise+RF is a helpful system for young students to improve their argumentative writing skills through revision and formative feedback.
VERSA: A Versatile Evaluation Toolkit for Speech, Audio, and Music (2025)

In this work, we introduce VERSA, a unified and standardized evaluation toolkit designed for various speech, audio, and music signals. The toolkit features a Pythonic interface with flexible configuration and dependency control, making it user-friendly and efficient. With full installation, VERSA offers 65 metrics with 729 metric variations based on different configurations. These metrics encompass evaluations utilizing diverse external resources, including matching and non-matching reference audio, text transcriptions, and text captions. As a lightweight yet comprehensive toolkit, VERSA is versatile to support the evaluation of a wide range of downstream scenarios. To demonstrate its capabilities, this work highlights example use cases for VERSA, including audio coding, speech synthesis, speech enhancement, singing synthesis, and music generation. The toolkit is available at https://github.com/shinjiwlab/versa.
Persona-SQ: A Personalized Suggested Question Generation Framework For Real-world Documents (2025)

Suggested questions (SQs) provide an effective initial interface for users to engage with their documents in AI-powered reading applications. In practical reading sessions, users have diverse backgrounds and reading goals, yet current SQ features typically ignore such user information, resulting in homogeneous or ineffective questions. We introduce a pipeline that generates personalized SQs by incorporating reader profiles (professions and reading goals) and demonstrate its utility in two ways: 1) as an improved SQ generation pipeline that produces higher quality and more diverse questions compared to current baselines, and 2) as a data generator to fine-tune extremely small models that perform competitively with much larger models on SQ generation. Our approach can not only serve as a drop-in replacement in current SQ systems to immediately improve their performance but also help develop on-device SQ models that can run locally to deliver fast and private SQ experience.
ESPnet-SDS: Unified Toolkit and Demo for Spoken Dialogue Systems (2025)

Advancements in audio foundation models (FMs) have fueled interest in end-to-end (E2E) spoken dialogue systems, but different web interfaces for each system makes it challenging to compare and contrast them effectively. Motivated by this, we introduce an open-source, user-friendly toolkit designed to build unified web interfaces for various cascaded and E2E spoken dialogue systems. Our demo further provides users with the option to get on-the-fly automated evaluation metrics such as (1) latency, (2) ability to understand user input, (3) coherence, diversity, and relevance of system response, and (4) intelligibility and audio quality of system output. Using the evaluation metrics, we compare various cascaded and E2E spoken dialogue systems with a human-human conversation dataset as a proxy. Our analysis demonstrates that the toolkit allows researchers to effortlessly compare and contrast different technologies, providing valuable insights such as current E2E systems having poorer audio quality and less diverse responses. An example demo produced using our toolkit is publicly available here: https://huggingface.co/spaces/Siddhant/Voice{\_}Assistant{\_}Demo.
SURF: A System to Unveil Explainable Risk Relations between Firms (2025)

Firm risk relations are crucial in financial applications, including hedging and portfolio construction. However, the complexity of extracting relevant information from financial reports poses significant challenges in quantifying these relations. To this end, we introduce SURF, a System to Unveil Explainable Risk Relations between Firms. SURF employs a domain-specific encoder and an innovative scoring mechanism to uncover latent risk connections from financial reports. It constructs a network graph to visualize these firm-level risk interactions and incorporates a rationale explainer to elucidate the underlying links. Our evaluation using stock data shows that SURF outperforms baseline methods in effectively capturing firm risk relations. The demo video of the system is publicly available.
Libra-Leaderboard: Towards Responsible AI through a Balanced Leaderboard of Safety and Capability (2025)

As large language models (LLMs) continue to evolve, leaderboards play a significant role in steering their development. Existing leaderboards often prioritize model capabilities while overlooking safety concerns, leaving a significant gap in responsible AI development. To address this gap, we introduce Libra-Leaderboard, a comprehensive framework designed to rank LLMs through a balanced evaluation of performance and safety. Combining a dynamic leaderboard with an interactive LLM arena, Libra-Leaderboard encourages the joint optimization of capability and safety. Unlike traditional approaches that average performance and safety metrics, Libra-Leaderboard uses a distance-to-optimal-score method to calculate the overall rankings. This approach incentivizes models to achieve a balance rather than excelling in one dimension at the expense of some other ones. In the first release, Libra-Leaderboard evaluates 26 mainstream LLMs from 14 leading organizations, identifying critical safety challenges even in state-of-the-art models.
Unlocking Korean Verbs: A User-Friendly Exploration into the Verb Lexicon (2025)

The Sejong dictionary dataset offers a valuable resource, providing extensive coverage of morphology, syntax, and semantic representation. This dataset can be utilized to explore linguistic information in greater depth.The labeled linguistic structures within this dataset form the basis for uncovering relationships between words and phrases and their associations with target verbs. This paper introduces a user-friendly web interface designed for the collection and consolidation of verb-related information, with a particular focus on subcategorization frames. Additionally, it outlines our efforts in mapping this information by aligning subcategorization frames with corresponding illustrative sentence examples.Furthermore, we provide a Python library that would simplify syntactic parsing and semantic role labeling. These tools are intended to assist individuals interested in harnessing the Sejong dictionary dataset to develop applications for Korean language processing.
TransformerRanker: A Tool for Efficiently Finding the Best-Suited Language Models for Downstream Classification Tasks (2025)

Classification tasks in NLP are typically addressed by selecting a pre-trained language model (PLM) from a model hub, and fine-tuning it for the task at hand. However, given the very large number of PLMs that are currently available, a practical challenge is to determine which of them will perform best for a specific downstream task. With this paper, we introduce TransformerRanker, a lightweight library that efficiently ranks PLMs for classification tasks without the need for computationally costly fine-tuning. Our library implements current approaches for transferability estimation (LogME, H-Score, kNN), in combination with layer aggregation options, which we empirically showed to yield state-of-the-art rankings of PLMs (Garbas et al., 2024). We designed the interface to be lightweight and easy to use, allowing users to directly connect to the HuggingFace Transformers and Dataset libraries. Users need only select a downstream classification task and a list of PLMs to create a ranking of likely best-suited PLMs for their task. We make TransformerRanker available as a pip-installable open-source library.
Learning Low-Resource Languages Through NLP-Driven Flashcards: A Case Study of Hokkien in Language Learning Applications (2025)

LangLearn is an open-source framework designed to facilitate autonomous learning of low-resource languages (LRL). By combining a language-agnostic approach with AI-enhanced flashcards, LangLearn empowers users to generate custom flashcards for their vocabulary, while offering structured learning through both pre-curated and self-curated decks. The framework integrates six key components: the word definition, corresponding Hanji characters, romanization with numeric tones, audio pronunciation, a sample sentence, as well as a contextual AI-generated image. LangLearn currently supports English and Taiwanese Hokkien (a variety of Southern Min), with plans to extend support for other dialects. Our preliminary study demonstrates that LangLearn positively empowers users to engage with LRLs using their vocabulary preferences, with a comprehensive user study currently underway. LangLearn`s modular structure enables future expansion, including ASR-based pronunciation practice. The code is available at https://github.com/HokkienTranslation/HokkienTranslation.