Austria

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 \textbf{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. 
\textbf{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: \url{https://app.ai4wa.com}. The demonstration video is available: \url{https://app.ai4wa.com/docs/docspiral/demo}.

ACL 2025

DocSpiral: A Platform for Integrated Assistive Document Annotation through Human-in-the-Spiral

comprehensive document annoatation platform

artifical intelligence

document

machine learning

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

demo

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

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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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.

ADEPT-SQL: A High-performance Text-to-SQL Application for Real-World Enterprise-Level Databases

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 $\texttt{oAMF}$ (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 $\texttt{oAMF}$ 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.

The Open Argument Mining Framework

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.

Live Football Commentary System Providing Background Information

This paper introduces and evaluates a novel web-based application designed to identify and repair fallacious arguments in political debates. DISPUTool 3.0 offers a comprehensive tool for argumentation analysis of political debate, integrating state-of-the-art natural language processing techniques to mine and classify argument components and relations. DISPUTool 3.0 builds on the $\textit{ElecDeb60to20}$ dataset, covering US presidential debates from 1960 to 2020. In this paper, we introduce a novel task which is integrated as a new module in DISPUTool, i.e., the automatic detection and classification of fallacious arguments, and the automatic $\textit{repairing}$ of such misleading arguments. The goal is to show to the user a tool which not only identifies fallacies in political debates, but it also shows how the argument looks like once the veil of fallacy falls down. An extensive evaluation of the module is addressed employing both automated metrics and human assessments. With the inclusion of this module, DISPUTool 3.0 advances even more user critical thinking in front of the augmenting spread of such nefarious kind of content in political debates and beyond. The tool is publicly available here: https://3ia-demos.inria.fr/disputool/

DISPUTool 3.0: Fallacy Detection and Repairing in Argumentative Political Debates

The emerging paradigm of enabling Large Language Models (LLMs) to generate citations in Question-Answering (QA) tasks is lacking in a unified framework to standardize and fairly compare different citation generation methods, leading to difficulties in reproduction and innovation. Therefore, we introduce Citeflow, an open-source and modular framework fostering reproduction and the implementation of new designs. Citeflow is highly extensible, allowing users to utilize four main modules and 14 components to construct a pipeline, evaluate an existing method, and understand the attributing LLM-generated contents. The framework is also paired with a visual interface, Citefix, facilitating case study and modification of existing citation generation methods. Users can use this interface to conduct LLM-powered case studies according to different scenarios. Citeflow and Citefix are highly integrated into the toolkit CiteLab, and we use an authentic process of multiple rounds of improvement through the Human-LLM interaction interface to demonstrate the efficiency of our toolkit on implementing and modifying citation generation pipelines. Citelab is released at https://github.com/SjJ1017/Citelab

CiteLab: Developing and Diagnosing LLM Citation Generation Workflows via the Human-LLM Interaction

Retrieval-augmented generation is increasingly effective in answering scientific questions from literature, but many state-of-the-art systems are expensive and closed-source. We introduce Ai2 Scholar QA, a free online scientific question answering application. To facilitate research, we make our entire pipeline public: as a customizable open-source Python package and interactive web app, along with paper indexes accessible through public APIs and downloadable datasets. We describe our system in detail and present experiments analyzing its key design decisions. In an evaluation on a recent scientific QA benchmark, we find that Ai2 Scholar QA outperforms competing systems.

Ai2 Scholar QA: Organized Literature Synthesis with Attribution

We introduce **ComfyUI-Copilot**, a large language model-powered plugin designed to enhance the usability and efficiency of ComfyUI, an open-source platform for AI-driven art creation. Despite its flexibility and user-friendly interface, ComfyUI can present challenges to newcomers, including limited documentation, model misconfigurations, and the complexity of workflow design. ComfyUI-Copilot addresses these challenges by offering intelligent node and model recommendations, along with automated one-click workflow construction. At its core, the system employs a hierarchical multi-agent framework comprising a central assistant agent for task delegation and specialized worker agents for different usages, supported by our curated ComfyUI knowledge bases to streamline debugging and deployment. We validate the effectiveness of ComfyUI-Copilot through both offline quantitative evaluations and online user feedback, showing that it accurately recommends nodes and accelerates workflow development. Additionally, use cases illustrate that ComfyUI-Copilot lowers entry barriers for beginners and enhances workflow efficiency for experienced users. The ComfyUI-Copilot installation package and a demo video are available at https://github.com/AIDC-AI/ComfyUI-Copilot.

ComfyUI-Copilot: An Intelligent Assistant for Automated Workflow Development

LLM Unlearning plays a crucial role in removing sensitive information from language models to mitigate potential misuse. However, previous approaches often treat nonsensical responses or template-based refusals (e.g., ``Sorry, I cannot answer.'') as the unlearning target, which can give the impression of deliberate information suppression, making the process even more vulnerable to attacks and jailbreaks. Moreover, most methods rely on auxiliary models or retaining datasets, which adds complexity to the unlearning process. To address these challenges, we propose MEOW, a streamlined and stealthy unlearning method that eliminates the need for auxiliary models or retaining data while avoiding leakage through its innovative use of inverted facts. These inverted facts are generated by an offline LLM and serve as fine-tuning labels. Meanwhile, we introduce MEMO, a novel metric that measures the model’s memorization, to select optimal fine-tuning targets. The use of inverted facts not only maintains the covert nature of the model but also ensures that sensitive information is effectively forgotten without revealing the target data. Evaluated on the ToFU Knowledge Unlearning dataset using Llama2-7B-Chat and Phi-1.5, MEOW outperforms baselines in forgetting quality while preserving model utility. MEOW also maintains strong performance across NLU and NLG tasks and demonstrates superior resilience to attacks, validated via the Min-K% membership inference method.

From Evasion to Concealment: Stealthy Knowledge Unlearning for LLMs

Although Large Language Models (LLMs) have demonstrated strong language understanding and generation abilities across various languages, their cultural knowledge is often limited to English-speaking communities, which can marginalize the cultures of non-English communities. To address the problem, the evaluation of the cultural awareness of the LLMs and the methods to develop culturally aware LLMs have been investigated. In this study, we focus on evaluating knowledge of folktales, a key medium for conveying and circulating culture. In particular, we focus on Japanese folktales, specifically on knowledge of Yokai. Yokai are supernatural creatures originating from Japanese folktales that continue to be popular motifs in art and entertainment today. Yokai have long served as a medium for cultural expression, making them an ideal subject for assessing the cultural awareness of LLMs. We introduce YokaiEval, a benchmark dataset consisting of 809 multiple-choice questions (each with four options) designed to probe knowledge about yokai. We evaluate the performance of 31 Japanese and multilingual LLMs on this dataset. The results show that models trained with Japanese language resources achieve higher accuracy than English-centric models, with those that underwent continued pretraining in Japanese, particularly those based on Llama-3, performing especially well.

Do Large Language Models Know Folktales? A Case Study of Yokai in Japanese Folktales

Authorship Attribution (AA) seeks to identify the author of a given text, yet existing methods often struggle with trustworthiness and interpretability, particularly across different domains, languages, and stylistic variations. These challenges arise from the absence of uncertainty quantification and the inability of current models to adapt to diverse authorship tasks. To address these limitations, we introduce BEDAA, a Bayesian-Enhanced DeBERTa framework that integrates Bayesian reasoning with transformer-based language models to enable uncertainty-aware and interpretable authorship attribution. BEDAA achieves up to 19.69% improvement in F1-score across multiple authorship attribution tasks, including binary, multiclass, and dynamic authorship detection. By incorporating confidence ranking, uncertainty decomposition, and probabilistic reasoning, BEDAA improves robustness while offering transparent decision-making processes. Furthermore, BEDAA extends beyond traditional AA by demonstrating its effectiveness in human vs. machine-generated text classification, code authorship detection, and cross-lingual attribution. These advances establish BEDAA as a generalised, interpretable, and adaptable framework for modern authorship attribution challenges.

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ADEPT-SQL: A High-performance Text-to-SQL Application for Real-World Enterprise-Level Databases

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