GenAI4ELab/papercli-papers huggingface dataset:
DocSpiral: A Platform for Integrated Assistive Document Annotation through Human-in-the-Spiral
Authors: Qiang Sun; Sirui Li; Tingting Bi; Du Q. Huynh; Mark Reynolds; Yuanyi Luo; Wei Liu
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Acquiring structured data from domain-specific, image-based documents—such as scanned reports—is crucial for many downstream tasks but remains challenging due to document variability. Many of these documents exist as images rather than as machine-readable text, which requires human annotation to train automated extraction systems.We present DocSpiral, the first Human-in-the-Spiral assistive document annotation platform, designed to address the challenge of extracting structured information from domain-specific, image-based document collections.Our spiral design establishes an iterative cycle in which human annotations train models that progressively require less manual intervention. DocSpiral integrates document format normalization, comprehensive annotation interfaces, evaluation metrics dashboard, and API endpoints for the development of AI / ML models into a unified workflow.Experiments demonstrate that our framework reduces annotation time by at least 41% while showing consistent performance gains across three iterations during model training.By making this annotation platform freely accessible, we aim to lower barriers to AI/ML models development in document processing, facilitating the adoption of large language models in image-based, document-intensive fields such as geoscience and healthcare. The system is freely available at: https://app.ai4wa.com. The demonstration video is available: https://app.ai4wa.com/docs/docspiral/demo.
ADEPT-SQL: A High-performance Text-to-SQL Application for Real-World Enterprise-Level Databases
Authors: Yongnan Chen; Zhuo Chang; Shijia Gu; Yuanhang Zong; Mei Zhang; Shiyu Wang; Zixiang He; HongZhi Chen; Wei Jin; Bin Cui
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: This paper presents Adept-SQL, a domain-adapted Text2SQL system that addresses critical deployment challenges in professional fields. While modern LLM-based solutions excel on academic benchmarks, we identify three persistent limitations in industrial application: domain-specific knowledge barriers, the schemas complexity in the real world, and the prohibitive computational costs of large LLMs. Our framework introduces two key innovations: a three-stage grounding mechanism combining dynamic terminology expansion, focused schema alignment, and historical query retrieval; coupled with a hybrid prompting architecture that decomposes SQL generation into schema-aware hinting, term disambiguation, and few-shot example incorporation phases. This approach enables efficient execution using smaller open-source LLMs while maintaining semantic precision. Deployed in petroleum engineering domains, our system achieves 97% execution accuracy on real-world databases, demonstrating 49% absolute improvement over SOTA baselines. We release implementation code to advance research in professional Text2SQL systems.
LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review
Authors: Cheng Yuan; Xinkai Rui; Yongqi Fan; Yawei Fan; Boyang Zhong; Jiacheng Wang; Weiyan Zhang; Tong Ruan
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Despite the remarkable performance of Large Language Models (LLMs) in automated discharge summary generation, they still suffer from generating inaccurate content or fabricating information without valid sources. To address these issues, we propose LCDS, a tool for empowering LLMs with Logic-Controlled Discharge Summary generation. LCDS constructs a source mapping table by calculating the textual similarity between electronic medical records (EMRs) and discharge summaries, providing a structured reference for generation. Based on a comprehensive set of logical rules, LCDS identifies the structured writing logic of discharge summaries and integrates it with EMRs to generate silver discharge summaries. Furthermore, LCDS traces the provenance of generated content, allowing experts to review, provide feedback, and rectify errors to produce golden discharge summaries, which are subsequently recorded for the incremental fine-tuning of LLMs.Our project and demo video are in the GitHub repository https://github.com/ycycyc02/LCDS.
Revealing the Unwritten: Visual Investigation of Beam Search Trees to Address Language Model Prompting Challenges
Authors: Thilo Spinner; Rita Sevastjanova; Rebecca Kehlbeck; Tobias Stähle; Daniel A. Keim; Oliver Deussen; Andreas Spitz; Mennatallah El-Assady
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: The present popularity of generative language models has amplified interest in interactive methods to guide model outputs. Prompt refinement is considered one of the most effective means to influence output among these methods. We identify several challenges associated with prompting large language models, categorized into data- and model-specific, linguistic, and socio-linguistic challenges. A comprehensive examination of model outputs, including runner-up candidates and their corresponding probabilities, is needed to address these issues. The beam search tree, the prevalent algorithm to sample model outputs, can inherently supply this information. Consequently, we leverage an interactive visual method for investigating the beam search tree, facilitating analysis of the decisions made by the model during generation. Our explorative approach validates existing results and offers additional insights.
Scholar Inbox: Personalized Paper Recommendations for Scientists
Authors: Markus Flicke; Glenn Angrabeit; Madhav Iyengar; Vitalii Protsenko; Illia Shakun; Jovan Cicvaric; Bora Kargi; Haoyu He; Lukas Schuler; Lewin Scholz; Kavyanjali Agnihotri; Yong Cao; Andreas Geiger
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Scholar Inbox is a new open-access platform designed to address the challenges researchers face in staying current with the rapidly expanding volume of scientific literature. We provide personalized recommendations, continuous updates from open-access archives (arXiv, bioRxiv, etc.), visual paper summaries, semantic search, and a range of tools to streamline research workflows and promote open research access. The platform’s personalized recommendation system is trained on user ratings, ensuring that recommendations are tailored to individual researchers’ interests. To further enhance the user experience, Scholar Inbox also offers a map of science that provides an overview of research across domains, enabling users to easily explore specific topics. We use this map to address the cold start problem common in recommender systems, as well as an active learning strategy that iteratively prompts users to rate a selection of papers, allowing the system to learn user preferences quickly. We evaluate the quality of our recommendation system on a novel dataset of 800k user ratings, which we make publicly available, as well as via an extensive user study.
The Open Argument Mining Framework
Authors: Debela Gemechu; Ramon Ruiz-Dolz; Kamila Górska; Somaye Moslemnejad; Eimear Maguire; Dimitra Zografistou; Yohan Jo; John Lawrence; Chris Reed
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Despite extensive research in Argument Mining (AM), the field faces significant challenges in limited reproducibility, difficulty in comparing systems due to varying task combinations, and a lack of interoperability caused by the heterogeneous nature of argumentation theory. These challenges are further exacerbated by the absence of dedicated tools, with most advancements remaining isolated research outputs rather than reusable systems. The oAMF (Open Argument Mining Framework) addresses these issues by providing an open-source, modular, and scalable platform that unifies diverse AM methods. Initially released with seventeen integrated modules, the oAMF serves as a starting point for researchers and developers to build, experiment with, and deploy AM pipelines while ensuring interoperability and allowing multiple theories of argumentation to co-exist within the same framework. Its flexible design supports integration via Python APIs, drag-and-drop tools, and web interfaces, streamlining AM development for research and industry setup, facilitating method comparison, and reproducibility.
Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines
Authors: Yunsu Kim; Ahmedelmogtaba Abdelaziz; Thiago Castro Ferreira; Mohamed Al-Badrashiny; Hassan Sawaf
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: As the demand for artificial intelligence (AI) grows to address complex real-world tasks, single models are often insufficient, requiring the integration of multiple models into pipelines. This paper introduces Bel Esprit, a conversational agent designed to construct AI model pipelines based on user requirements. Bel Esprit uses a multi-agent framework where subagents collaborate to clarify requirements, build, validate, and populate pipelines with appropriate models. We demonstrate its effectiveness in generating pipelines from ambiguous user queries, using both human-curated and synthetic data. A detailed error analysis highlights ongoing challenges in pipeline building.Bel Esprit is available for a free trial at https://belesprit.aixplain.com.
ZeroSumEval: An Extensible Framework For Scaling LLM Evaluation with Inter-Model Competition
Authors: Hisham Abdullah Alyahya; Haidar Khan; Yazeed Alnumay; M Saiful Bari; Bulent Yener
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: We introduce ZeroSumEval, a dynamic, competition-based, and evolving evaluation framework for Large Language Models (LLMs) that leverages competitive games. ZeroSumEval encompasses a diverse suite of games, including security challenges (Capture the Flag), classic board games (chess), and knowledge tests (MathQuiz). These games are designed to evaluate a range of capabilities such as strategic reasoning, planning, knowledge application, safety, and adaptability. Building upon recent studies that highlight the effectiveness of game-based evaluations for LLMs, ZeroSumEval enhances these approaches by providing a standardized and extensible framework for easily implementing games and leverages DSPy to provide a better abstraction for LLM player strategies.
DECAF: A Dynamically Extensible Corpus Analysis Framework
Authors: Max Müller-Eberstein; Rob Van Der Goot; Anna Rogers
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: The study of generalization in Language Models (LMs) requires controlled experiments that can precisely measure complex linguistic variations between training and testing datasets. We introduce DECAF, a framework that enables the analysis and filtering of linguistically-annotated datasets down to the character level. Rather than creating new resources for each experiment, DECAF starts from datasets with existing linguistic annotations, and leverages them to analyze, filter, and generate highly controlled and reproducible experimental settings targeting specific research questions. We demonstrate DECAF’s functionality by adding 28 morphosyntactic annotation layers to the 115M-word BabyLM corpus and indexing the resulting 1.1B annotations to analyze its internal domain variance, and to create a controlled training data curriculum for a small-scale gender bias study. We release DECAF as an open-source Python library, along with the parsed and indexed version of BabyLM, as resources for future generalization research.
Dialz: A Python Toolkit for Steering Vectors
Authors: Zara Siddique; Liam Turner; Luis Espinosa-Anke
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: We introduce *Dialz*, a Python library for advancing research on steering vectors for open-source LMs. Steering vectors allow users to modify activations at inference time to amplify or weaken a ‘concept’, e.g. honesty or positivity, providing a more powerful alternative to prompting or fine-tuning. Dialz supports a diverse set of tasks, including creating contrastive pair datasets, computing and applying steering vectors, and visualizations. Unlike existing libraries, Dialz emphasizes modularity and usability, enabling both rapid prototyping and in-depth analysis. We demonstrate how Dialz can be used to reduce harmful outputs such as stereotypes, while also providing insights into model behaviour across different layers. We release Dialz with full documentation, tutorials, and support for popular open-source models to encourage further research in safe and controllable language generation. Dialz enables faster research cycles and facilitates insights into model interpretability, paving the way for safer, more transparent, and more reliable AI systems.
FORG3D: Flexible Object Rendering for Generating Vision-Language Spatial Reasoning Data from 3D Scenes
Authors: Oscar Pang; Freda Shi
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: We introduce FORG3D, a 3D rendering toolkit developed with Blender and Python, which synthesizes vision-language data for two primary purposes: (1) supporting human cognitive experiments that require fine-grained control over material and (2) analyzing and improving the visual reasoning capabilities of large vision-language models. The toolkit provides flexible and precise control over object placement, orientation, inter-object distances, and camera configurations while automatically generating detailed spatial metadata. Additionally, it includes a built-in feature for integrating AI-generated backgrounds, enhancing the realism of synthetic scenes. FORG3D is publicly available at https://github.com/compling-wat/FORG3D, and a video demonstration is available at https://www.youtube.com/watch?v=QvIqib_PU8A.
RT-VC: Real-Time Zero-Shot Voice Conversion with Speech Articulatory Coding
Authors: Yisi Liu; Chenyang Wang; Hanjo Kim; Raniya Khan; Gopala Anumanchipalli
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: 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.
Live Football Commentary System Providing Background Information
Authors: Yuichiro Mori; Chikara Tanaka; Aru Maekawa; Satoshi Kosugi; Tatsuya Ishigaki; Kotaro Funakoshi; Hiroya Takamura; Manabu Okumura
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Previous research on sports commentary generation has primarily focused on describing major events in the match.However, real-world commentary often includes comments beyond what is visible in the video content, e.g., “Florentina has acquired him for 7 million euros.”For enhancing the viewing experience with such background information,we developed an audio commentary system for football matches that generates utterances with background information, as well as play-by-play commentary.Our system first extracts visual information, and determines whether it is an appropriate timing to produce an utterance.Then it decides which type of utterance to generate: play-by-play or background information. In the latter case, the system leverages external knowledge through retrieval-augmented generation.
GreaterPrompt: A Unified, Customizable, and High-Performing Open-Source Toolkit for Prompt Optimization
Authors: Wenliang Zheng; Sarkar Snigdha Sarathi Das; Yusen Zhang; Rui Zhang
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: LLMs have gained immense popularity among researchers and the general public for its impressive capabilities on a variety of tasks. Notably, the efficacy of LLMs remains significantly dependent on the quality and structure of the input prompts, making prompt design a critical factor for their performance. Recent advancements in automated prompt optimization have introduced diverse techniques that automatically enhance prompts to better align model outputs with user expectations. However, these methods often suffer from the lack of standardization and compatibility across different techniques, limited flexibility in customization, inconsistent performance across model scales, and they often exclusively rely on expensive proprietary LLM APIs. To fill in this gap, we introduce GreaterPrompt, a novel framework that democratizes prompt optimization by unifying diverse methods under a unified, customizable API while delivering highly effective prompts for different tasks. Our framework flexibly accommodates various model scales by leveraging both text feedback-based optimization for larger LLMs and internal gradient-based optimization for smaller models to achieve powerful and precise prompt improvements. Moreover, we provide a user-friendly Web UI that ensures accessibility for non-expert users, enabling broader adoption and enhanced performance across various user groups and application scenarios. GreaterPrompt is available at https://github.com/psunlpgroup/GreaterPrompt via GitHub, PyPI, and web user interfaces.
iPET: An Interactive Emotional Companion Dialogue System with LLM-Powered Virtual Pet World Simulation
Authors: Zheyong Xie; Shaosheng Cao; Zuozhu Liu; Zheyu Ye; Zihan Niu; Chonggang Lu; Tong Xu; Enhong Chen; Zhe Xu; Yao Hu; Wei Lu
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: The rapid advancement of large language models (LLMs) has unlocked transformative potential for role-playing emotional companion products, enabling systems that support emotional well-being, educational development, and therapeutic applications. However, existing approaches often lack sustained personalization and contextual adaptability, limiting their effectiveness in real-world settings. In this paper, we introduce iPET, an LLM-powered virtual pet agent designed to enhance user engagement through rich, dynamic pet behaviors and interactions tailored to individual preferences. iPET comprises three core components: a dialogue module that instantiates virtual pet agents for emotionally interactive conversations; a memory module that stores and synthesizes records of both agent and user experiences; and a world simulation module that generates diverse, preference-driven pet behaviors guided by high-level reflections. Deployed for over 200 days in a real-world, non-commercial product, iPET has served millions of users – providing emotional support to psychologically distressed individuals and demonstrating its effectiveness in practical applications.
LiDARR: Linking Document AMRs with Referents Resolvers
Authors: Jon Z. Cai; Kristin Wright-Bettner; Zekun Zhao; Shafiuddin Rehan Ahmed; Abijith Trichur Ramachandran; Jeffrey Flanigan; Martha Palmer; James Martin
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: In this paper, we present LiDARR (**Li**nking **D**ocument **A**MRs with **R**eferents **R**esolvers), a web tool for semantic annotation at the document level using the formalism of Abstract Meaning Representation (AMR). LiDARR streamlines the creation of comprehensive knowledge graphs from natural language documents through semantic annotation. The tool features a visualization and interactive user interface, transforming document-level AMR annotation into an models-facilitated verification process. This is achieved through the integration of an AMR-to-surface alignment model and a coreference resolution model. Additionally, we incorporate PropBank rolesets into LiDARR to extend implicit roles in annotated AMR, allowing implicit roles to be linked through the coreference chains via AMRs.
SlimLM: An Efficient Small Language Model for On-Device Document Assistance
Authors: Thang M. Pham; Phat T. Nguyen; Seunghyun Yoon; Viet Dac Lai; Franck Dernoncourt; Trung Bui
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: While small language models (SLMs) show promises for mobile deployment, their real world performance and applications on smartphones remain underexplored. We present SlimLM, a series of SLMs optimized for document assistance tasks on mobile devices. Through extensive experiments on a Samsung Galaxy S24, we identify the sweet spot between model size (ranging from 125M to 8B parameters), context length, and inference time for efficient on-device processing. SlimLM is pretrained on SlimPajama-627B and fine-tuned on DocAssist, our constructed dataset for summarization, question answering, and suggestion tasks. Our smallest model demonstrates efficient performance on S24, while larger variants offer enhanced capabilities within mobile constraints. We evaluate SlimLM against existing SLMs, showing comparable or superior performance and offering a benchmark for future research in on-device language models. We provide an Android application allowing users to experience SlimLM’s document assistance capabilities, offering valuable insights for mobile developers, researchers, and companies seeking privacy-preserving on-device alternatives to server-based language models.