GenAI4ELab/papercli-papers huggingface dataset:
GigaChat Family: Efficient Russian Language Modeling Through Mixture of Experts Architecture
Authors: 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
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Generative large language models (LLMs) have become crucial for modern NLP research and applications across various languages. However, the development of foundational models specifically tailored to the Russian language has been limited, primarily due to the significant computational resources required. This paper introduces the GigaChat family of Russian LLMs, available in various sizes, including base models and instruction-tuned versions. We provide a detailed report on the model architecture, pre-training process, and experiments to guide design choices. In addition, we evaluate their performance on Russian and English benchmarks and compare GigaChat with multilingual analogs. The paper presents a system demonstration of the top-performing models accessible via an API, a Telegram bot, and a Web interface. Furthermore, we have released three open GigaChat models in open-source, aiming to expand NLP research opportunities and support the development of industrial solutions for the Russian language.
Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Research
Authors: Qianqian Zhang; Jiajia Liao; Heting Ying; Yibo Ma; Haozhan Shen; Jingcheng Li; Peng Liu; Lu Zhang; Chunxin Fang; Kyusong Lee; Ruochen Xu; Tiancheng Zhao
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Language agents powered by large language models (LLMs) have demonstrated remarkable capabilities in understanding, reasoning, and executing complex tasks. However, developing robust agents presents significant challenges: substantial engineering overhead, lack of standardized components, and insufficient evaluation frameworks for fair comparison. We introduce Agent Graph-based Orchestration for Reasoning and Assessment (AGORA), a flexible and extensible framework that addresses these challenges through three key contributions: (1) a modular architecture with a graph-based workflow engine, efficient memory management, and clean component abstraction; (2) a comprehensive suite of reusable agent algorithms implementing state-of-the-art reasoning approaches; and (3) a rigorous evaluation framework enabling systematic comparison across multiple dimensions. Through extensive experiments on mathematical reasoning and multimodal tasks, we evaluate various agent algorithms across different LLMs, revealing important insights about their relative strengths and applicability. Our results demonstrate that while sophisticated reasoning approaches can enhance agent capabilities, simpler methods like Chain-of-Thought often exhibit robust performance with significantly lower computational overhead. AGORA not only simplifies language agent development but also establishes a foundation for reproducible agent research through standardized evaluation protocols.We made a demo video at: https://www.youtube.com/watch?v=WRH-F1zegKI. The comparison agent of algorithms is also available at https://huggingface.co/spaces/omlab/open-agent-leaderboard. Source code of AGORA can be found at https://github.com/om-ai-lab/OmAgent.
Abacus-SQL: A Text-to-SQL System Empowering Cross-Domain and Open-Domain Database Retrieval
Authors: Keyan Xu; Dingzirui Wang; Xuanliang Zhang; Qingfu Zhu; Wanxiang Che
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: The existing text-to-SQL systems have made significant progress in SQL query generation, but they still face numerous challenges. Existing systems often lack retrieval capabilities for open-domain databases, requiring users to manually filter relevant databases. Additionally, their cross-domain transferability is limited, making it challenging to accommodate diverse query requirements. To address these issues, we propose Abacus-SQL. Abacus-SQL utilizes database retrieval technology to accurately locate the required databases in an open-domain database environment. It also enhances the system cross-domain transfer ability through data augmentation methods. Moreover, Abacus-SQL employs Pre-SQL and Self-debug methods, thereby enhancing the accuracy of SQL queries. Experimental results demonstrate that Abacus-SQL performs excellently in multi-turn text-to-SQL tasks, effectively validating the approach’s effectiveness.Abacus-SQL is publicly accessible at https://huozi.8wss.com/abacus-sql/.
Tulun: Transparent and Adaptable Low-resource Machine Translation
Authors: Raphael Merx; Hanna Suominen; Lois Yinghui Hong; Nick Thieberger; Trevor Cohn; Ekaterina Vylomova
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: 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.
ReasonGraph: Visualization of Reasoning Methods and Extended Inference Paths
Authors: Zongqian Li; Ehsan Shareghi; Nigel Collier
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Large Language Models (LLMs) reasoning processes are challenging to analyze due to their complexity and the lack of organized visualization tools. We present ReasonGraph, a web-based platform for visualizing and analyzing LLM reasoning processes. It supports both sequential and tree-based reasoning methods and extended inference outputs while integrating with major LLM providers and over fifty state-of-the-art models. ReasonGraph incorporates an intuitive UI with meta reasoning method selection, configurable visualization parameters, and a modular framework that facilitates efficient extension. Our evaluation shows high parsing reliability, efficient processing, and excellent usability across various downstream applications. By providing a unified visualization framework, ReasonGraph reduces cognitive load in analyzing complex reasoning paths, improves error identification in logical processes, and enables more effective development of LLM-based applications. The platform is open-source, facilitating accessibility and reproducibility in LLM reasoning analysis.
Dia-Lingle: A Gamified Interface for Dialectal Data Collection
Authors: Jiugeng Sun; Rita Sevastjanova; Sina Ahmadi; Rico Sennrich; Mennatallah El-Assady
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Dialects suffer from the scarcity of computational textual resources as they exist predominantly in spoken rather than written form and exhibit remarkable geographical diversity. Collecting dialect data and subsequently integrating it into current language technologies present significant obstacles. Gamification has been proven to facilitate remote data collection processes with great ease and on a substantially wider scale. This paper introduces Dia-Lingle, a gamified interface aimed to improve and facilitate dialectal data collection tasks such as corpus expansion and dialect labelling. The platform features two key components: the first challenges users to rewrite sentences in their dialects, identifies them through a classifier and solicits feedback, and the other one asks users to match sentences to their geographical locations. Dia-Lingle combines active learning with gamified difficulty levels, strategically encouraging prolonged user engagement while efficiently enriching the dialect corpus. Usability evaluation shows that our interface demonstrates high levels of user satisfaction. We provide the link to Dia-Lingle: https://dia-lingle.ivia.ch/, and demo video: https://youtu.be/0QyJsB8ym64.
Token Level Routing Inference System for Edge Devices
Authors: Jianshu She; Wenhao Zheng; Zhengzhong Liu; Hongyi Wang; Eric P. Xing; Huaxiu Yao; Qirong Ho
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: The computational complexity of large language model (LLM) inference significantly constrains their deployment efficiency on edge devices. In contrast, small language models offer faster decoding and lower resource consumption but often suffer from degraded response quality and heightened susceptibility to hallucinations. To address this trade-off, collaborative decoding, in which a large model assists in generating critical tokens, has emerged as a promising solution. This paradigm leverages the strengths of both model types by enabling high-quality inference through selective intervention of the large model, while maintaining the speed and efficiency of the smaller model. In this work, we present a novel collaborative decoding inference system that allows small models to perform on-device inference while selectively consulting a cloud-based large model for critical token generation. Remarkably, the system achieves a 60% performance gain on CommonsenseQA using only a 0.5B model on an M1 MacBook, with under 7% of tokens generation uploaded to the large model in the cloud.
The ShareLM Collection and Plugin: Contributing Human-Model Chats for the Benefit of the Community
Authors: Shachar Don-Yehiya; Leshem Choshen; Omri Abend
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Human-model conversations provide a window into users’ real-world scenarios, behavior, and needs, and thus are a valuable resource for model development and research. While for-profit companies collect user data through the APIs of their models, using it internally to improve their own models, the open source and research community lags behind. We introduce the ShareLM collection, a unified set of human conversations with large language models, and its accompanying plugin, a Web extension for voluntarily contributing user-model conversations. Where few platforms share their chats, the ShareLM plugin adds this functionality, thus, allowing users to share conversations from most platforms. The plugin allows the user to rate their conversations, both at the conversation and the response levels, and delete conversations they prefer to keep private before they ever leave the user’s local storage.
OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens
Authors: 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
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: We present OLMoTrace, the first system that traces the outputs of language models back to their full, multi-trillion-token training data in real time. OLMoTrace finds and shows verbatim matches between segments of language model output and documents in the training text corpora. Powered by an extended version of infini-gram (Liu et al., 2024), our system returns tracing results within a few seconds. OLMoTrace can help users understand the behavior of language models through the lens of their training data. We showcase how it can be used to explore fact checking, hallucination, and the creativity of language models. OLMoTrace is publicly available and fully open-source.
AutoAlign: Get Your LLM Aligned with Minimal Annotations
Authors: 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
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Automated Alignment refers to a set of algorithms designed to align Large Language Models (LLMs) with human intentions and values while minimizing manual intervention. However, it faces challenges such as algorithmic diversity and excessively convoluted workflows. We present AutoAlign, an open-source toolkit that offers:(1) a unified framework integrating mainstream automated algorithms through a consistent interface, and(2) an accessible workflow supporting one-click execution for prompt synthesis, automatic alignment signal construction, and iterative model training. Our toolkit enables easy reproduction of existing results through extensive benchmarks and facilitates the development of novel approaches via modular components. It includes implementations for both highly efficient inference and training, as well as low-resource training. By standardizing automated alignment methodologies and providing accessible implementations, AutoAlign lowers the barriers to building customized aligned models and supports academic research.
Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models
Authors: Jiaxiang Liu; Boxuan Xing; Chenhao Yuan; Chenxiang Zhang; Di Wu; Xiusheng Huang; Haida Yu; Chuhan Lang; Pengfei Cao; Jun Zhao; Kang Liu
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: As large language models (LLMs) continue to advance, there is a growing urgency to enhance the interpretability of their internal knowledge mechanisms. Consequently, many interpretation methods have emerged, aiming to unravel the knowledge mechanisms of LLMs from various perspectives. However, current interpretation methods differ in input data formats and interpreting outputs. The tools integrating these methods are only capable of supporting tasks with specific inputs, significantly constraining their practical applications. To address these challenges, we present an open-source **Know**ledge **M**echanisms **R**evealer&**I**nterpreter (**Know-MRI**) designed to analyze the knowledge mechanisms within LLMs systematically. Specifically, we have developed an extensible core module that can automatically match different input data with interpretation methods and consolidate the interpreting outputs. It enables users to freely choose appropriate interpretation methods based on the inputs, making it easier to comprehensively diagnose the model’s internal knowledge mechanisms from multiple perspectives. Our code is available at https://github.com/nlpkeg/Know-MRI. We also provide a demonstration video on https://youtu.be/NVWZABJ43Bs.
AI4Reading: Chinese Audiobook Interpretation System Based on Multi-Agent Collaboration
Authors: Minjiang Huang; Jipeng Qiang; Yi Zhu; Chaowei Zhang; Xiangyu Zhao; Kui Yu
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Audiobook interpretations are attracting increasing attention, as they provide accessible and in-depth analyses of books that offer readers practical insights and intellectual inspiration. However, their manual creation process remains time-consuming and resource-intensive. To address this challenge, we propose AI4Reading, a multi-agent collaboration system leveraging large language models (LLMs) and speech synthesis technology to generate podcast-like audiobook interpretations. The system is designed to meet three key objectives: accurate content preservation, enhanced comprehensibility, and a logical narrative structure. To achieve these goals, We develop a framework composed of 11 specialized agents—including topic analysts, case analysts, editors, a narrator, and proofreaders—that work in concert to explore themes, extract real-world cases, refine content organization, and synthesize natural spoken language. By comparing expert interpretations with our system’s output, the results show that although AI4Reading still has a gap in speech generation quality, the generated interpretative scripts are simpler and more accurate. The code of AI4Reading is publicly accessible , with a demonstration video available .
DEEP: an automatic bidirectional translator leveraging an ASR for translation of Italian sign language
Authors: Nicolas Tagliabue; Elisa Colletti; Francesco Roberto Dani; Roberto Tedesco; Sonia Cenceschi; Alessandro Trivilini
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: DEEP is a bidirectional translation system for the Italian Sign Language, tailored to two specific, common use cases: pharmacies and the registry office of the municipality, for which a custom corpus has been collected. Two independent pipelines permit to create a chatlike interaction style, where the deaf subject just signs in front of a camera, and sees a virtual LIS interpreter, while the hearing subject reads and writes messages into a chat UI. The LIS-to-Italian pipeline leverages, in a novel way, a customized Whisper model (a wellknown 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.
VenusFactory: An Integrated System for Protein Engineering with Data Retrieval and Language Model Fine-Tuning
Authors: Yang Tan; Chen Liu; Jingyuan Gao; Banghao Wu; Mingchen Li; Ruilin Wang; Lingrong Zhang; Huiqun Yu; Guisheng Fan; Liang Hong; Bingxin Zhou
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Natural language processing (NLP) has significantly influenced scientific domains beyond human language, including protein engineering, where pre-trained protein language models (PLMs) have demonstrated remarkable success. However, interdisciplinary adoption remains limited due to challenges in data collection, task benchmarking, and application. This work presents VenusFactory, a versatile engine that integrates biological data retrieval, standardized task benchmarking, and modular fine-tuning of PLMs. VenusFactory supports both computer science and biology communities with choices of both a command-line execution and a Gradio-based no-code interface, integrating 40+ protein-related datasets and 40+ popular PLMs. All implementations are open-sourced on https://github.com/ai4protein/VenusFactory. A video introduction is available at https://www.youtube.com/watch?v=MT6lPH5kgCc.
GenGO Ultra: an LLM-powered ACL Paper Explorer
Authors: Sotaro Takeshita; Tornike Tsereteli; Simone Paolo Ponzetto
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: The ever-growing number of papers in natural language processing (NLP) poses the challenge of finding relevant papers. In our previous paper, we introduced GenGO, which complements NLP papers with various information, such as aspect-based summaries, to enable efficient paper exploration. While it delivers a better literature search experience, it lacks an interactive interface that dynamically produces information tailored to the user’s needs. To this end, we present an extension to our previous system, dubbed GenGO Ultra, which exploits large language models (LLMs) to dynamically generate responses grounded by published papers. We also conduct multi-granularity experiments to evaluate six text encoders and five LLMs. Our system is designed for transparency – based only on open-weight models, visible system prompts, and an open-source code base – to foster further development and research on top of our system: https://gengo-ultra.sotaro.io/
SpatialWebAgent: Leveraging Large Language Models for Automated Spatial Information Extraction and Map Grounding
Authors: Shunfeng Zheng; Meng Fang; Ling Chen
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations))

Abstract: Understanding and extracting spatial information from text is vital for a wide range of applications, including geographic information systems (GIS), smart cities, disaster prevention, and logistics planning. This capability empowers decision-makers to gain crucial insights into geographic distributions and trends.However, the inherent complexity of geographic expressions in natural language presents significant hurdles for traditional extraction methods. These challenges stem from variations in place names, vague directional cues, and implicit spatial relationships.To address these challenges, we introduce SpatialWebAgent, an automated agent system that leverages large language models (LLMs). SpatialWebAgent is designed to extract, standardize, and ground spatial information from natural language text directly onto maps. Our system excels at handling the diverse and often ambiguous nature of geographic expressions—from varying place names and vague directions to implicit spatial relationships that demand flexible combinations of localization functions—by tapping into the powerful geospatial reasoning capabilities of LLMs. SpatialWebAgent employs a series of specialized tools to convert this extracted information into precise coordinates, which are then visualized on interactive maps.A demonstration of SpatialWebAgent is available at https://sites.google.com/view/SpatialWebAgent.