Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

NameTag 3: A Tool and a Service for Multilingual/Multitagset NER (2025.acl-demo)

Challenge:	MERaLiON-AudioLLM is the first general-purpose audio-based large language model for multitask learning.
Approach:	They introduce MERaLiON-AudioLLM, a general-purpose audio-based large language model for multitask learning with a focus on Singlish understanding.
Outcome:	The proposed model exhibits strong generalization across a diverse set of tasks . it is a leading solution for region-specific AI applications.

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Jana Straková, Milan Straka

Multi-Programming Language Sandbox for LLMs (2025.acl-demo)

Challenge:	NameTag 3 is an open-source tool and cloud-based web service for named entity recognition.
Approach:	NameTag 3 is an open-source tool and cloud-based web service for named entity recognition.
Outcome:	NameTag 3 achieves state-of-the-art on 21 test datasets in 15 languages . available as command-line tool and as cloud-based service, enabling use without local installation .

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Shihan Dou, Jiazheng Zhang, Jianxiang Zang, Yunbo Tao, Weikang Zhou, Haoxiang Jia, Shichun Liu, Yuming Yang, Shenxi Wu, Zhiheng Xi, Muling Wu, Rui Zheng, Changze Lv, Limao Xiong, Shaoqing Zhang, Lin Zhang, Wenyu Zhan, Rongxiang Weng, Jingang Wang, Xunliang Cai, Yueming Wu, Ming Wen, Yixin Cao, Tao Gui, Xipeng Qiu, Qi Zhang, Xuanjing Huang

FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model Evaluation (2025.acl-demo)

Challenge:	MPLSandbox is an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs).
Approach:	They propose a multi-programming language sandbox that provides unified feedback from compilers and analysis tools for Large Language Models.
Outcome:	The proposed multi-language sandbox can provide comprehensive feedback from compilers and analysis tools for large language models (LLMs).

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Zheqi He, Yesheng Liu, Jing-Shu Zheng, Xuejing Li, Jin-Ge Yao, Bowen Qin, Richeng Xuan, Xi Yang

My Climate CoPilot: A Question Answering System for Climate Adaptation in Agriculture (2025.acl-demo)

Challenge:	FlagEvalMM is an evaluation framework designed to assess multimodal models . it is designed to be used for vision-language understanding and generation tasks .
Approach:	They propose an evaluation framework that decouples model inference from evaluation through an independent evaluation service.
Outcome:	The evaluation framework offers accurate and efficient insights into model strengths and limitations.

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Vincent Nguyen, Willow Hallgren, Ashley Harkin, Mahesh Prakash, Sarvnaz Karimi

SPOT: Bridging Natural Language and Geospatial Search for Investigative Journalism (2025.acl-demo)

Challenge:	Accurately answering climate science questions requires scientific literature and climate data.
Approach:	They propose to provide climate adaptation experts with information on adaptation practices relevant to a specific commodity and location.
Outcome:	My Climate CoPilot is a platform that assists users to mitigate and adapt to projected climate change by providing answers grounded in evidence.

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Lynn Khellaf, Ipek Baris Schlicht, Tilman Mirass, Julia Bayer, Tilman Wagner, Ruben Bouwmeester

Information Retrieval

Challenge:	Existing tools to query OSM data require familiarity with complex query languages, creating barriers for non-technical users.
Approach:	They propose to use a semantic bundling system to make querying OSM more accessible through intuitive scene descriptions.
Outcome:	The proposed system interprets user inputs as structured representations of geospatial object configurations using fine-tuned Large Language Models (LLMs) with results displayed in an interactive map interface.

Textagon: Boosting Language Models with Theory-guided Parallel Representations (2025.acl-demo)

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John P. Lalor, Ruiyang Qin, David Dobolyi, Ahmed Abbasi

GigaChat Family: Efficient Russian Language Modeling Through Mixture of Experts Architecture (2025.acl-demo)

Challenge:	Pretrained language models do not account for the wide variety of available expert-generated language resources and lexicons that explicitly encode linguistic/domain knowledge.
Approach:	They propose a Python package for generating parallel representations for text based on predefined lexicons and selecting representations that provide the most information.
Outcome:	The proposed model can generate parallel representations of text based on predefined lexicons and select representations that provide the most information.

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Valentin Mamedov, Evgenii Kosarev, Gregory Leleytner, Ilya Shchuckin, Valeriy Berezovskiy, Daniil Smirnov, Dmitry Kozlov, Sergei Averkiev, Lukyanenko Ivan, Aleksandr Proshunin, Ainur Israfilova, Ivan Baskov, Artem Chervyakov, Emil Shakirov, Mikhail Kolesov, Daria Khomich, Daria Latortseva, Sergei Porkhun, Yury Fedorov, Oleg Kutuzov, Polina Kudriavtseva, Sofiia Soldatova, Kolodin Egor, Stanislav Pyatkin, Dzmitry Menshykh, Grafov Sergei IUrevich, Eldar Damirov, Vladimir Karlov, Ruslan Gaitukiev, Arkadiy Shatenov, Alena Fenogenova, Nikita Savushkin, Fedor Minkin

Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Research (2025.acl-demo)

Challenge:	generative large language models have become crucial for modern NLP research and applications across multiple languages.
Approach:	They introduce the GigaChat family of Russian LLMs, available in various sizes . they evaluate their performance on Russian and English benchmarks and compare them with multilingual analogs .
Outcome:	The proposed model family is available in various sizes and is tested on Russian and English benchmarks.

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Qianqian Zhang, Jiajia Liao, Heting Ying, Yibo Ma, Haozhan Shen, Jingcheng Li, Peng Liu, Lu Zhang, Chunxin Fang, Kyusong Lee, Ruochen Xu, Tiancheng Zhao

Abacus-SQL: A Text-to-SQL System Empowering Cross-Domain and Open-Domain Database Retrieval (2025.acl-demo)

Challenge:	Language agents powered by large language models (LLMs) have demonstrated remarkable capabilities in understanding, reasoning, and executing complex tasks.
Approach:	They propose a flexible framework that addresses engineering overhead and insufficient evaluation frameworks for fair comparison.
Outcome:	The proposed framework simplifies language agent development and establishes a foundation for reproducible agent research.

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Keyan Xu, Dingzirui Wang, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che

Tulun: Transparent and Adaptable Low-resource Machine Translation (2025.acl-demo)

Challenge:	Existing text-to-SQL systems often lack retrieval capabilities for open-domain databases, requiring users to manually filter relevant databases.
Approach:	They propose to use database retrieval technology to locate the required databases in an open-domain database environment and enhance system cross-domain transferability through data augmentation methods.
Outcome:	The proposed system performs excellently in multi-turn text-to-SQL tasks, validating the proposed approach’s effectiveness.

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Raphael Merx, Hanna Suominen, Lois Yinghui Hong, Nick Thieberger, Trevor Cohn, Ekaterina Vylomova

Text Generation

Challenge:	a low-resource language that is the lingua franca in Timor-Leste lacks available corpora in the health domain.
Approach:	They propose a solution that combines neural MT with large language model-based post-editing guided by existing glossaries and translation memories.
Outcome:	The proposed system outperforms both standalone MT and LLM approaches across six low-resource languages on the FLORES dataset.

ReasonGraph: Visualization of Reasoning Methods and Extended Inference Paths (2025.acl-demo)

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Zongqian Li, Ehsan Shareghi, Nigel Collier

Dia-Lingle: A Gamified Interface for Dialectal Data Collection (2025.acl-demo)

Challenge:	Large Language Models (LLMs) reasoning processes are complex and lack of organized visualization tools creates barriers to understanding, evaluation, and improvement.
Approach:	They propose a web-based platform for visualizing and analyzing LLM reasoning processes.
Outcome:	The proposed platform shows high parsing reliability, efficient processing, and excellent usability across various downstream applications.

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Jiugeng Sun, Rita Sevastjanova, Sina Ahmadi, Rico Sennrich, Mennatallah El-Assady

Challenge:	Dialects suffer from the scarcity of textual resources and are largely spoken rather than written.
Approach:	They propose a gamified interface that combines active learning with gamification to enhance the dialect corpus.
Outcome:	The proposed interface demonstrates high levels of user satisfaction while requiring minimal effort.

Token Level Routing Inference System for Edge Devices (2025.acl-demo)

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Jianshu She, Wenhao Zheng, Zhengzhong Liu, Hongyi Wang, Eric P. Xing, Huaxiu Yao, Qirong Ho

The ShareLM Collection and Plugin: Contributing Human-Model Chats for the Benefit of the Community (2025.acl-demo)

Challenge:	Large language models (LLMs) have been gaining in performance but deployment in edge devices faces significant hurdles due to their high computational complexity.
Approach:	They propose a collaborative decoding system that allows small models to perform on-device inference while selectively consulting a cloud-based large model for critical token generation.
Outcome:	The proposed system achieves 60% performance gain on CommonsenseQA using a 0.5B model on an M1 MacBook, with under 7% of tokens generation uploaded to the large model in the cloud.

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Shachar Don-Yehiya, Leshem Choshen, Omri Abend

OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens (2025.acl-demo)

Challenge:	Using human-model conversations is a valuable resource for model development and research, but the open source and research community lags behind.
Approach:	They propose a unified set of human conversations with large language models and a plugin for voluntarily contributing user-model conversations.
Outcome:	The ShareLM collection and its plugin allow users to share conversations from most platforms.

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Jiacheng Liu, Taylor Blanton, Yanai Elazar, Sewon Min, Yen-Sung Chen, Arnavi Chheda-Kothary, Huy Tran, Byron Bischoff, Eric Marsh, Michael Schmitz, Cassidy Trier, Aaron Sarnat, Jenna James, Jon Borchardt, Bailey Kuehl, Evie Yu-Yen Cheng, Karen Farley, Taira Anderson, David Albright, Carissa Schoenick, Luca Soldaini, Dirk Groeneveld, Rock Yuren Pang, Pang Wei Koh, Noah A. Smith, Sophie Lebrecht, Yejin Choi, Hannaneh Hajishirzi, Ali Farhadi, Jesse Dodge

AutoAlign: Get Your LLM Aligned with Minimal Annotations (2025.acl-demo)

Challenge:	tracing language models' outputs back to training data is a problem because they are trained on text corpora with trillions of tokens . existing methods for tracers have not been scaled to work within this multi-trillion-token setting .
Approach:	They propose a system that traces language models' outputs verbatim back to training data . OLMOTRACE retrieves documents from the model's training data that contain exact matches .
Outcome:	The proposed system can find verbatim matches between LM output and training data . it can be used to explore fact checking, hallucination, and creativity of language models .

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Xinyu Lu, Dong Xu, Chunkang Zhang, Xinyan Guan, Junxiang Wang, Qingyu Zhang, Pengbo Wang, Yingzhi Mao, Hao Xiang, Xueru Wen, Zichao Li, Yaojie Lu, Hongyu Lin, Le Sun, Xianpei Han

Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models (2025.acl-demo)

Challenge:	Automated Alignment (ALM) is a set of algorithms designed to align Large Language Models (LLMs) with human intentions and values while minimizing manual intervention.
Approach:	They propose an open-source toolkit that integrates mainstream automated algorithms through a consistent interface and an accessible workflow supporting one-click execution for prompt synthesis and automatic alignment signal construction.
Outcome:	The proposed framework enables easy reproduction of existing results through extensive benchmarks and facilitates the development of novel approaches via modular components.

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Jiaxiang Liu, Boxuan Xing, Chenhao Yuan, ChenxiangZhang ChenxiangZhang, Di Wu, Xiusheng Huang, Haida Yu, Chuhan Lang, Pengfei Cao, Jun Zhao, Kang Liu

Explainability & Interpretability in NLP

Challenge:	Existing interpretation methods only support tasks with specific inputs, limiting their practical applications.
Approach:	They propose an extensible module that matches different input data with interpretation methods and consolidates the interpreting outputs.
Outcome:	The proposed module can match different input data with interpretation methods and consolidate the interpreting outputs.

AI4Reading: Chinese Audiobook Interpretation System Based on Multi-Agent Collaboration (2025.acl-demo)

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Minjiang Huang, Jipeng Qiang, Yi Zhu, Chaowei Zhang, Xiangyu Zhao, Kui Yu

DEEP: an automatic bidirectional translator leveraging an ASR for translation of Italian sign language (2025.acl-demo)

Challenge:	Interpretative audiobooks are becoming more popular, but their manual creation process remains time-consuming and resource-intensive.
Approach:	They propose a multi-agent collaboration system that leverages large language models and speech synthesis technology to generate podcast-like audiobook interpretations.
Outcome:	The proposed system is open source and open to the public.

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Nicolas Tagliabue, Elisa Colletti, Francesco Roberto Dani, Roberto Tedesco, Sonia Cenceschi, Alessandro Trivilini

Text Generation

Challenge:	DEEP is a bidirectional translation system for the Italian Sign Language . it is designed for pharmacies and the registry office of the municipality .
Approach:	They propose a bidirectional translation system for the Italian Sign Language, DEEP . the system allows deaf people to interact with a virtual LIS interpreter and a chat UI .
Outcome:	The proposed system can be used in pharmacies and the registry office of the municipality.

VenusFactory: An Integrated System for Protein Engineering with Data Retrieval and Language Model Fine-Tuning (2025.acl-demo)

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Yang Tan, Chen Liu, Jingyuan Gao, Wu Banghao, Mingchen Li, Ruilin Wang, Lingrong Zhang, Huiqun Yu, Guisheng Fan, Liang Hong, Bingxin Zhou

GenGO Ultra: an LLM-powered ACL Paper Explorer (2025.acl-demo)

Challenge:	Pre-trained protein language models have been used in protein engineering, but their adoption is limited due to data collection, task benchmarking, and application challenges.
Approach:	They propose a versatile engine that integrates biological data retrieval, standardized task benchmarking, and modular fine-tuning of PLMs.
Outcome:	The proposed engine integrates biological data retrieval, task benchmarking, and modular fine-tuning of PLMs.

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Sotaro Takeshita, Tornike Tsereteli, Simone Paolo Ponzetto

Challenge:	The main repository of natural language processing (NLP) has grown its number of stored papers by 70% from 2019 to 2023.
Approach:	They propose an extension to GenGO Ultra which exploits large language models to dynamically generate responses grounded by published papers.
Outcome:	The proposed system exploits large language models to generate responses grounded by published papers and performs multi-granularity experiments.

SpatialWebAgent: Leveraging Large Language Models for Automated Spatial Information Extraction and Map Grounding (2025.acl-demo)

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Shunfeng Zheng, Meng Fang, Ling Chen

DocSpiral: A Platform for Integrated Assistive Document Annotation through Human-in-the-Spiral (2025.acl-demo)

Challenge:	Understanding and extracting spatial information from text is vital for a wide range of applications, says nielsen . inherent complexity of geographic expressions in natural language presents significant hurdles for traditional extraction methods.
Approach:	They propose a system that leverages large language models to extract spatial information from natural language.
Outcome:	SpatialWebAgent is designed to extract, standardize, and ground spatial information from natural language text directly onto maps.

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Qiang Sun, Sirui Li, Tingting Bi, Du Q. Huynh, Mark Reynolds, Yuanyi Luo, Wei Liu

ADEPT-SQL: A High-performance Text-to-SQL Application for Real-World Enterprise-Level Databases (2025.acl-demo)

Challenge:	Documents that are image-based are difficult to extract because of document variability.
Approach:	They propose a human-in-the-spiral assistive document annotation platform to extract structured data from document collections.
Outcome:	The proposed framework reduces annotation time by at least 41% while showing consistent performance gains over three iterations.

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Yongnan Chen, Zhuo Chang, Shijia Gu, Yuanhang Zong, Zhang Mei, Shiyu Wang, Hezixiang Hezixiang, Hongzhi Chen, Jin Wei, Bin Cui

LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review (2025.acl-demo)

Challenge:	et al., 2017) address domain-specific knowledge barriers, schemas complexity, and computational costs of large LLMs.
Approach:	They propose a domain-adapted Text2SQL system that addresses critical deployment challenges in professional fields.
Outcome:	The proposed system achieves 97% execution accuracy on real-world databases . it is faster than existing systems and has a higher performance than existing ones.

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Cheng Yuan, Xinkai Rui, Yongqi Fan, Yawei Fan, Boyang Zhong, Jiacheng Wang, Weiyan Zhang, Tong Ruan

Challenge:	Large language models (LLMs) are capable of generating inaccurate discharge summary content or fabricating information without valid sources.
Approach:	They propose a tool for empowering LLMs with Logic-Controlled Discharge Summary generation.
Outcome:	The proposed tool identifies the writing logic of discharge summaries and integrates it with EMRs to generate silver discharge summararies.

Revealing the Unwritten: Visual Investigation of Beam Search Trees to Address Language Model Prompting Challenges (2025.acl-demo)

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Thilo Spinner, Rita Sevastjanova, Rebecca Kehlbeck, Tobias Stähle, Daniel A. Keim, Oliver Deussen, Andreas Spitz, Mennatallah El-Assady

Scholar Inbox: Personalized Paper Recommendations for Scientists (2025.acl-demo)

Challenge:	generative language models have become popular, but comprehending their outputs remains challenging for NLP practitioners and linguistic experts.
Approach:	They propose to use a beam search tree to examine model outputs to provide information on runner-up candidates and their corresponding probabilities to address these challenges.
Outcome:	The proposed method validates existing results and offers additional insights.

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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

The Open Argument Mining Framework (2025.acl-demo)

Challenge:	Scholar Inbox is an open-access platform designed to address the challenges researchers face in staying current with the rapidly expanding volume of scientific publications.
Approach:	They propose to provide personalized recommendations, continuous updates from open-access archives, visual paper summaries, semantic search, and a range of tools to streamline research workflows and promote open access publications.
Outcome:	The proposed platform is based on a dataset of 800k user ratings and an extensive user study.

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Debela Gemechu, Ramon Ruiz-Dolz, Kamila Górska, Somaye Moslemnejad, Eimear Maguire, Dimitra Zografistou, Yohan Jo, John Lawrence, Chris Reed

Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines (2025.acl-demo)

Challenge:	Argument Mining (AM) has been a key area of research for many years, but it is still a challenging field.
Approach:	the oAMF provides an open-source, modular platform that unifies diverse AM methods.
Outcome:	the oAMF is an open-source, modular, and scalable platform that unifies diverse AM methods.

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Yunsu Kim, Ahmedelmogtaba Abdelaziz, Thiago Castro Ferreira, Mohamed Al-Badrashiny, Hassan Sawaf

Dialogue Systems & Conversational Agents

Challenge:	a single AI model is often insufficient for complex tasks, requiring integration of multiple models into pipelines . a conversational agent can build pipelines composed of diverse AI models based on user requirements .
Approach:	They propose a conversational agent that constructs AI model pipelines based on user requirements.
Outcome:	The proposed agent can build AI model pipelines from human-curated and synthetic data.

ZeroSumEval: An Extensible Framework For Scaling LLM Evaluation with Inter-Model Competition (2025.acl-demo)

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Hisham Abdullah Alyahya, Haidar Khan, Yazeed Alnumay, M Saiful Bari, Bulent Yener

Challenge:	Recent studies highlight the effectiveness of game-based evaluations for Large Language Models.
Approach:	They propose a dynamic, competition-based evaluation framework for Large Language Models that leverages competitive games.
Outcome:	The framework leverages competitive games to evaluate models in large language models.

DECAF: A Dynamically Extensible Corpus Analysis Framework (2025.acl-demo)

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Max Müller-Eberstein, Rob Van Der Goot, Anna Rogers

Challenge:	DeCAF is an open-source Python library that enables the analysis and filtering of linguistically-annotated datasets down to the character level.
Approach:	They propose a framework that enables the analysis and filtering of linguistically-annotated datasets down to the character level.
Outcome:	The proposed framework analyzes a parsed version of the 115M-word BabyLM corpus and generates highly controlled and reproducible experimental settings targeting specific research questions.

Dialz: A Python Toolkit for Steering Vectors (2025.acl-demo)

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Zara Siddique, Liam Turner, Luis Espinosa-Anke

Challenge:	Dialz is a Python library for advancing research on steering vectors for open-source LMs.
Approach:	They propose a Python library for advancing research on steering vectors for open-source LMs.
Outcome:	The proposed method reduces harmful outputs and provides insights into model behaviour across different layers.

FORG3D: Flexible Object Rendering for Generating Vision-Language Spatial Reasoning Data from 3D Scenes (2025.acl-demo)

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Oscar Pang, Freda Shi

RT-VC: Real-Time Zero-Shot Voice Conversion with Speech Articulatory Coding (2025.acl-demo)

Challenge:	FORG3D synthesizes vision-language data for use in cognitive experiments . it provides precise control over object placement, orientation, and camera configurations .
Approach:	They propose a 3D rendering toolkit that synthesizes vision-language data with Blender and Python.
Outcome:	The toolkit synthesizes vision-language data for two primary purposes: supporting cognitive experiments and improving visual reasoning capabilities of large vision-linguistic models.

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Yisi Liu, Chenyang Wang, Hanjo Kim, Raniya Khan, Gopala Anumanchipalli

Live Football Commentary System Providing Background Information (2025.acl-demo)

Challenge:	Experimental evaluations show RT-VC delivers a 13.3% reduction in latency . voice conversion modifies speech to match the timbre of a target speaker while preserving content information.
Approach:	They propose a zero-shot real-time voice conversion system that leverages an articulatory feature space to naturally disentangle content and speaker characteristics.
Outcome:	The proposed system achieves a CPU latency of 61.4 ms, representing a 13.3% reduction in latency.

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Yuichiro Mori, Chikara Tanaka, Aru Maekawa, Satoshi Kosugi, Tatsuya Ishigaki, Kotaro Funakoshi, Hiroya Takamura, Manabu Okumura

Challenge:	Existing studies on sports commentary generation focus on describing major events in the video, but real-world commentary often includes background information.
Approach:	They developed an audio commentary system that generates utterances with background information and play-by-play commentary for football matches.
Outcome:	The proposed system generates utterances with background information and play-by-play commentary for football matches.

GreaterPrompt: A Unified, Customizable, and High-Performing Open-Source Toolkit for Prompt Optimization (2025.acl-demo)

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Wenliang Zheng, Sarkar Snigdha Sarathi Das, Yusen Zhang, Rui Zhang

iPET: An Interactive Emotional Companion Dialogue System with LLM-Powered Virtual Pet World Simulation (2025.acl-demo)

Challenge:	Recent advances in prompt optimization have introduced diverse techniques that automatically enhance prompts to better align model outputs with user expectations.
Approach:	They propose a framework that unifies different methods under a unified, customizable API while delivering highly effective prompts for different tasks.
Outcome:	The proposed framework unifies multiple methods under a unified, customizable API while delivering highly effective prompts for different tasks.

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Zheyong Xie, Shaosheng Cao, Zuozhu Liu, Zheyu Ye, Zihan Niu, Chonggang Lu, Tong Xu, Enhong Chen, Zhe Xu, Yao Hu, Wei Lu

Dialogue Systems & Conversational Agents

Challenge:	Existing approaches to role-playing emotional companion products lack sustained personalization and contextual adaptability, limiting their effectiveness in real-world settings.
Approach:	They propose a virtual pet agent that can enhance user engagement through rich, dynamic pet behaviors and interactions tailored to individual preferences.
Outcome:	The proposed system has been deployed in a real-world, non-commercial product for 200 days and has demonstrated its effectiveness in practical applications.

LiDARR: Linking Document AMRs with Referents Resolvers (2025.acl-demo)

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Jon Cai, Kristin Wright-Bettner, Zekun Zhao, Shafiuddin Rehan Ahmed, Abijith Trichur Ramachandran, Jeffrey Flanigan, Martha Palmer, James Martin

Information Extraction & Text Mining

Challenge:	Abstract Meaning Representation (AMR) is a formalism for semantic representation of natural language text.
Approach:	They propose a web tool for semantic annotation at the document level using Abstract Meaning Representation (AMR) it integrates an AMR-to-surface alignment model and a coreference resolution model into the tool .
Outcome:	The proposed tool simplifies the creation of knowledge graphs from natural language documents . it integrates an AMR-to-surface alignment model and coreference resolution model .

SlimLM: An Efficient Small Language Model for On-Device Document Assistance (2025.acl-demo)

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Thang M. Pham, Phat T. Nguyen, Seunghyun Yoon, Viet Dac Lai, Franck Dernoncourt, Trung Bui

VeriMinder: Mitigating Analytical Vulnerabilities in NL2SQL (2025.acl-demo)

Challenge:	Small language models (SLMs) show promise for mobile deployment, but their real world performance and applications on smartphones remain understudied.
Approach:	They propose a slim language model with a model size of 125M to 8B and a context length of 8B for efficient on-device processing.
Outcome:	The proposed model is based on a Samsung Galaxy S24 and shows comparable or superior performance.

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Shubham Mohole, Sainyam Galhotra

DocAgent: A Multi-Agent System for Automated Code Documentation Generation (2025.acl-demo)

Challenge:	Application systems using natural language interfaces to databases (NLIDBs) have democratized data analysis, but they are not without significant risks.
Approach:	They propose an interactive system that detects and mitigates cognitive biases in analytical questions by using contextual semantic mapping frameworks.
Outcome:	The proposed system detects and mitigates cognitive biases in analytical questions and generates high-quality, task-specific prompts.

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Dayu Yang, Antoine Simoulin, Xin Qian, Xiaoyi Liu, Yuwei Cao, Zhaopu Teng, Grey Yang

DISPUTool 3.0: Fallacy Detection and Repairing in Argumentative Political Debates (2025.acl-demo)

Challenge:	Existing methods for generating documentation using Large Language Models (LLMs) produce incomplete, unhelpful, or factually incorrect outputs.
Approach:	They propose a novel collaborative system that uses topological code processing for incremental context building to generate documentation by agents.
Outcome:	The proposed system outperforms baselines in completeness, helpfulness, and truthfulness evaluations.

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Pierpaolo Goffredo, Deborah Dore, Elena Cabrio, Serena Villata

Reasoning

Challenge:	DISPUTool 3.0 is a web-based application for identifying and fixing fallacious arguments in political debates.
Approach:	They propose a web-based application designed to identify and repair fallacious arguments in political debates.
Outcome:	The proposed tool is based on the ElecDeb60to20 dataset covering US presidential debates from 1960 to 2020.

MedDecXtract: A Clinician-Support System for Extracting, Visualizing, and Annotating Medical Decisions in Clinical Narratives (2025.acl-demo)

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Mohamed Elgaar, Hadi Amiri, Mitra Mohtarami, Leo Anthony Celi

Challenge:	Clinical notes contain important information about medical decisions embedded within unstructured text.
Approach:	They propose an open-source interactive system that automatically extracts medical decisions from clinical text.
Outcome:	The open-source system extracts and visualizes medical decisions from clinical text.

CiteLab: Developing and Diagnosing LLM Citation Generation Workflows via the Human-LLM Interaction (2025.acl-demo)

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Jiajun Shen, Tong Zhou, Yubo Chen, Kang Liu, Jun Zhao

Challenge:	Existing frameworks for enabling Large Language Models to generate citations are lacking . however, they can still produce hallucinated responses that are non-factual or irrelevant to the input.
Approach:	They propose an open-source and modular framework for enabling LLMs to generate citations in Question-Answering tasks.
Outcome:	The proposed framework is extensible and paired with a visual interface, Citefix, facilitating case study and modification of existing citation generation methods.

CodeArena: A Collective Evaluation Platform for LLM Code Generation (2025.acl-demo)

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Mingzhe Du, Anh Tuan Luu, Bin Ji, Xiaobao Wu, Yuhao Qing, Dong Huang, Terry Yue Zhuo, Qian Liu, See-Kiong Ng

Ai2 Scholar QA: Organized Literature Synthesis with Attribution (2025.acl-demo)

Challenge:	Large Language Models (LLMs) have reshaped code generation, but persistent challenges impede accurate assessment.
Approach:	They propose an online evaluation framework tailored for large language models to assess their coding capabilities.
Outcome:	a new evaluation framework for large language models (LLMs) provides unbiased, unbiased evaluations and open access to solutions and test cases.

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Amanpreet Singh, Joseph Chee Chang, Dany Haddad, Aakanksha Naik, Jena D. Hwang, Rodney Kinney, Daniel S Weld, Doug Downey, Sergey Feldman

Natural Language Interfaces

Challenge:	Ai2 Scholar QA is a free online scientific question answering application . it uses retrieval-augmented generation to answer complex scientific questions . many of these systems are expensive to use and closed-source .
Approach:	They propose a retrieval-augmented generation-based scientific question answering application . it uses a Python package and an interactive web app to make the entire pipeline public . they compare it with other similar question-answering applications .
Outcome:	The proposed system outperforms other systems on a recent scientific QA benchmark.

gec-metrics: A Unified Library for Grammatical Error Correction Evaluation (2025.acl-demo)

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Takumi Goto, Yusuke Sakai, Taro Watanabe

Syntactic Text Processing

Challenge:	a library for using and developing grammatical error correction (GEC) evaluation metrics is released under the MIT license .
Approach:	They propose a library for using and developing grammatical error correction (GEC) evaluation metrics through a unified interface.
Outcome:	The proposed method is based on a unified evaluation framework with a strong focus on API usage and extensible.

AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents (2025.acl-demo)

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Jiaxiang Chen, Jingwei Shi, Lei Gan, Jiale Zhang, Qingyu Zhang, Dongqian Zhang, Pang Xin, Zhucong Li, Xu Yinghui

Efficient Annotator Reliability Assessment with EffiARA (2025.acl-demo)

Challenge:	a new framework automates deployment and debugging of AI projects . complexity of environment configurations, dependency conflicts, and debuggering issues hinder scalability and adoption.
Approach:	They propose an end-to-end framework that automates AI project deployment . they conducted experiments on 30 AI deployment cases to evaluate its effectiveness .
Outcome:	The proposed framework reduces deployment time and improves success rates by reducing human intervention.

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Owen Cook, Jake A Vasilakes, Ian Roberts, Xingyi Song

TRANSLATIONCORRECT: A Unified Framework for Machine Translation Post-Editing with Predictive Error Assistance (2025.acl-demo)

Challenge:	Obtaining annotations from experts is ideal, but this expertise is logistically and financially costly.
Approach:	They propose an annotation framework that supports the whole annotation pipeline from understanding the resources required for an annotation task to compiling the annotated dataset.
Outcome:	The proposed framework improves classification performance through annotator-reliability-based soft-label aggregation and sample weighting, and increases agreement among annotators through removal of identifying and replacing an unreliable annotation.

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Syed Mekael Wasti, Shou-Yi Hung, Christopher Collins, En-Shiun Annie Lee

Machine Translation

Challenge:	Current workflows for machine translation (MT) post-editing and research data collection are inefficient and time-consuming.
Approach:	They propose a framework that combines MT and error prediction within a single environment.
Outcome:	TranslationCorrect exports high-quality span-based annotations in the Error Span Annotation format, using an error taxonomy inspired by Multidimensional Quality Metrics (MQM).

First-AID: the first Annotation Interface for grounded Dialogues (2025.acl-demo)

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Stefano Menini, Daniel Russo, Alessio Palmero Aprosio, Marco Guerini

Dialogue Systems & Conversational Agents

Challenge:	Existing tools to fine-tune Large Language Models for specific tasks are limited due to financial constraints and limited availability of human experts.
Approach:	They propose a human-in-the-loop framework for the knowledge-driven generation of synthetic dialogues using LLM prompting that implements different strategies of data collection that require different user intervention during dialogue generation.
Outcome:	The proposed framework reduces post-editing efforts and improves quality of generated dialogues.

Mergenetic: a Simple Evolutionary Model Merging Library (2025.acl-demo)

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Adrian Robert Minut, Tommaso Mencattini, Andrea Santilli, Donato Crisostomi, Emanuele Rodolà

FlagEval-Arena: A Side-by-Side Comparative Evaluation Platform for Large Language Models and Text-Driven AIGC (2025.acl-demo)

Challenge:	Recent work shows that combining model merging with evolutionary algorithms can boost performance, but there is currently no library for experimenting with different evolutionary algorithms and merging methods.
Approach:	They propose an open-source library for evolutionary model merging that enables easy composition of merging methods and evolutionary algorithms while incorporating lightweight fitness estimators to reduce evaluation costs.
Outcome:	The proposed library produces competitive results across languages and tasks using modest hardware.

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Jing-Shu Zheng, Richeng Xuan, Bowen Qin, Zheqi He, Tongshuai.ren Tongshuai.ren, Xuejing Li, Jin-Ge Yao, Xi Yang

IRIS: Interactive Research Ideation System for Accelerating Scientific Discovery (2025.acl-demo)

Challenge:	a new evaluation platform for large language models and text-driven AIGCs is available for free.
Approach:	They propose an evaluation platform for side-by-side comparisons of large language models and text-driven AIGC systems.
Outcome:	a new evaluation platform for large language models and text-driven AIGC systems is available for free . the platform is more focused on the Chinese language and more models developed by Chinese institutes .

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Aniketh Garikaparthi, Manasi Patwardhan, Lovekesh Vig, Arman Cohan

Challenge:	Recent work on automated hypothesis generation focuses on multi-agent frameworks and extending test-time compute, but none incorporates human-in-the-loop (HITL) integration.
Approach:	They propose an open-source platform to enable researchers to leverage LLM-assisted scientific ideation.
Outcome:	The proposed system empowers researchers with greater control throughout ideation process.

ROGRAG: A Robustly Optimized GraphRAG Framework (2025.acl-demo)

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Zhefan Wang, Huanjun Kong, Jie Ying, Wanli Ouyang, Nanqing Dong