Austria

Literary translation remains one of the most challenging frontiers in machine translation due to the complexity of capturing figurative language, cultural nuances, and unique stylistic elements. In this work, we introduce TransAgents, a novel multi-agent framework that simulates the roles and collaborative practices of a human translation company, including a CEO, Senior Editor, Junior Editor, Translator, Localization Specialist, and Proofreader. The translation process is divided into two stages: a preparation stage where the team is assembled and comprehensive translation guidelines are drafted, and an execution stage that involves sequential translation, localization, proofreading, and a final quality check. Furthermore, we propose two innovative evaluation strategies: Monolingual Human Preference (MHP), which evaluates translations based solely on target language quality and cultural appropriateness, and Bilingual LLM Preference (BLP), which leverages large language models like gpt-4 for direct text comparison. Although TransAgents achieves lower d-BLEU scores, due to the limited diversity of references, its translations are significantly better than those of other baselines and are preferred by both human evaluators and LLMs over traditional human references and gpt-4 translations. Our findings highlight the potential of multi-agent collaboration in enhancing translation quality, particularly for longer texts.

ACL 2025

(Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts

multi-agent systems

large language models

machine translation

## Welcome to EMNLP 2024! 
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EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

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

Recent decoder-based pre-trained language models (PLMs) generally use subword tokenizers. However, adding character-level perturbations drastically changes the delimitation of texts by the tokenizers, leading to the vulnerability of PLMs. This study proposes a method of continual pre-training to convert decoder-based PLMs with subword tokenizers into perturbation-robust few-shot in-context learners. Our method continually trains decoder-based PLMs to predict the next tokens conditioning on artificially created character-level noisy texts. Since decoder-based language models are auto-regressive, we skip noised words from the target optimization. In addition, to maintain the same word prediction performance under noisy text as clean text, our method employs word distribution matching between the original PLMs and training models. We conducted experiments on various subword-based PLMs, including GPT2, Pythia, Mistral, Gemma2, and Llama3, ranging from 1B to 8B parameters. The results show that our method consistently improves downstream task performance for texts containing artificial noise and actual typos or spelling errors with few-shot in-context learning settings.

Continual Pre-training on Character-Level Noisy Texts Makes Decoder-based Language Models Robust Few-shot Learners

Vision Language Models (VLMs) extend remarkable capabilities of text-only large language models and vision-only models, being able to learn from and process multi-modal vision-text input. While modern VLMs perform well on a number of standard image classification and image-text matching tasks, they still struggle with a number of crucial vision-language (VL) reasoning abilities such as counting and spatial reasoning. Moreover, while they might be very brittle to small variations in instructions and/or evaluation protocols, existing benchmarks fail to evaluate their robustness (or rather the lack of it). In order to couple challenging VL scenarios with comprehensive robustness evaluation, we introduce DARE, Diverse Visual Question Answering with Robustness Evaluation, a carefully created and curated multiple-choice VQA benchmark. DARE evaluates VLM performance on five diverse categories and includes four robustness-oriented evaluations based on the variations of: prompts, the subsets of answer options, the output format and the number of correct answers. Among a spectrum of other findings, we report that state-of-the-art VLMs still struggle with questions in most categories and are unable to consistently deliver their peak performance across the tested robustness evaluations. Consequently, our work calls for the systematic addition of robustness evaluations in future VLM research.

DARE: Diverse Visual Question Answering with Robustness Evaluation

Lay summaries for scientific documents typically include explanations to help readers grasp sophisticated concepts or arguments. However, current automatic summarization methods do not explicitly model explanations, which makes it difficult to align the proportion of explanatory content with human-written summaries. In this paper, we present a plan-based approach that leverages discourse frameworks to organize summary generation and guide explanatory sentences by prompting responses to the plan. Specifically, we propose two discourse-driven planning strategies, where the plan is conditioned as part of the input or part of the output prefix, respectively. Empirical experiments on three lay summarization datasets show that our approach outperforms existing state-of-the-art methods in terms of summary quality, and it enhances model robustness, controllability and mitigates hallucination.

Explanatory Summarization with Discourse-Driven Planning

We introduce MPLSandbox, 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). It can automatically identify the programming language of the code, compiling and executing it within an isolated sub-sandbox to ensure safety and stability. In addition, MPLSandbox integrates both traditional and LLM-based code analysis tools, providing a comprehensive analysis of generated code. It also can be effortlessly integrated into the training and deployment of LLMs to improve the quality and correctness of generated code. It also helps researchers streamline their workflows for various LLM-based code-related tasks, reducing the development cost. To validate the effectiveness of MPLSandbox, we conduct extensive experiments by integrating it into several training and deployment scenarios, and employing it to optimize workflows for a wide range of downstream code tasks. Our goal is to enhance researcher productivity on LLM-based code tasks by simplifying and automating workflows through delegation to MPLSandbox.

Multi-Programming Language Sandbox for LLMs

Accurately answering climate science questions requires scientific literature and climate data. Interpreting climate literature and data, however, presents inherent challenges, such as interpreting uncertainties, determining relevant climate factors and drivers, and dealing with the sheer volume of data. My Climate CoPilot is a platform that assists climate experts, such as farmer advisors, mitigate and adapt to projected climate impacts by answering questions grounded in evidence. It emphasises transparency, user privacy, and low-resource use, and provides automatic evaluation. Fifty domain experts have carefully evaluated every aspect of My Climate CoPilot and based on their interactions and feedback, the system continues to evolve. To date, we have received 2180 question-answer pairs with an 82% positive rating from the experts.

My Climate CoPilot: A Question Answering System for Climate Adaptation in Agriculture

OpenStreetMap (OSM) is a vital resource for investigative journalists using geolocation verification. However, existing tools such as Overpass Turbo require familiarity with complex query languages, creating barriers for non-technical users. We present SPOT, an open source natural language interface that makes OSM's rich, tag-based geographic data more accessible through intuitive scene descriptions. SPOT interprets user inputs as structured representations of geographic object configurations using fine-tuned Large Language Models (LLMs), with results being displayed in an interactive map interface. While more general geospatial search tasks are conceivable, SPOT is specifically designed for use in investigative journalism, addressing real-world challenges such as hallucinations in model output, inconsistencies in OSM tagging, and the noisy nature of user input. It combines a novel synthetic data pipeline with a semantic bundling system to enable robust, accurate query generation. To our knowledge, SPOT is the first system to achieve reliable natural language access to OSM data at this level of accuracy. By lowering the technical barrier to spatial verification, SPOT contributes a practical tool to the broader efforts to support fact-checking and combat disinformation.

SPOT: Bridging Natural Language and Geospatial Search for Investigative Journalism

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.

VenusFactory: An Integrated System for Protein Engineering with Data Retrieval and Language Model Fine-Tuning

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}.

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

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.

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Continual Pre-training on Character-Level Noisy Texts Makes Decoder-based Language Models Robust Few-shot Learners

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