Austria

The rapid advancement of large language models (LLMs) has significantly enhanced the capabilities of agents across various tasks. However, existing agentic systems, whether based on fixed pipeline algorithms or pre-defined meta-learning frameworks, cannot search the whole agent design space due to the restriction of human-designed components, and thus might miss the more optimal agent design. In this paper, we introduce Gödel Agent, a self-evolving framework inspired by the \godel machine, enabling agents to recursively improve themselves without relying on predefined routines or fixed optimization algorithms. Gödel Agent leverages LLMs to dynamically modify its own logic and behavior, guided solely by high-level objectives through prompting. Experimental results on multiple domains demonstrate that implementation of Gödel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.

ACL 2025

Gödel Agent: A Self-Referential Agent Framework for Recursively Self-Improvement

### 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)

workshop paper

Machine translation (MT) has advanced significantly for high-resource languages, yet specialized domain translation remains a challenge for low-resource languages. This study evaluates the abilityof state-of-the-art multilingual models to translate animal health reports from English to Yorùbá, a crucial task for veterinary communication in underserved regions. We curated a dataset of 1,468 parallel sentences and compared multiple MT models in zero-shot and fine-tuned settings. Our findings indicate substantial limitations in their ability to generalize to domain-specific translation, with common errors arising from vocabulary mismatch, training data scarcity, and morphological complexity. Fine-tuning improves performance, particularly for the NLLB 3.3B model, but challenges remain in preserving technical accuracy. These results underscore the need for more targeted approaches to multilingual and culturally aware LLMs for African languages.

Beyond Generalization :Evaluating Multilingual LLMs for Yorùbá Animal Health Translation

The deployment of Large Language Models (LLMs) in real-world applications presents both opportunities and challenges, particularly in multilingual and code-mixed communication settings. This research evaluates the performance of seven leading LLMs in sentiment analysis on a dataset derived from multilingual and code-mixed WhatsApp chats, including Swahili, English and Sheng. Our evaluation includes both quantitative analysis using metrics like F1 score and qualitative assessment of LLMs' explanations for their predictions. We find that, while Mistral-7b and Mixtral-8x7b achieved high F1 scores, they and other LLMs such as GPT-3.5-Turbo, Llama-2-70b, and Gemma-7b struggled with understanding linguistic and contextual nuances, as well as lack of transparency in their decision-making process as observed from their explanations. In contrast, GPT-4 and GPT-4-Turbo excelled in grasping diverse linguistic inputs and managing various contextual information, demonstrating high consistency with human alignment and transparency in their decision-making process. The LLMs however, encountered difficulties in incorporating cultural nuance especially in non-English settings with GPT-4s doing so inconsistently. The findings emphasize the necessity of continuous improvement of LLMs to effectively tackle the challenges of culturally nuanced, low-resource real-world settings and the need for developing evaluation benchmarks for capturing these issues.

Beyond Metrics: Evaluating LLMs’ Effectiveness in Culturally Nuanced, Low-Resource Real-World Scenarios

In this paper, we introduce the first publicly available English-Kpelle dataset for machine translation, comprising over 2,000 sentence pairs drawn from everyday communication, religious texts, and educational materials. By fine-tuning Meta’s No Language Left Behind (NLLB) model on two versions of the dataset, we achieved BLEU scores of up to 30 in the Kpelle-to-English direction, demonstrating the benefits of data augmentation. Our findings align with NLLB-200 benchmarks on other African languages, underscoring Kpelle’s potential for competitive performance despite its low-resource status. Beyond machine translation, this dataset enables broader NLP tasks, including speech recognition and language modeling. We conclude with a roadmap for future dataset expansion, emphasizing orthographic consistency, community-driven validation, and interdisciplinary collaboration to advance inclusive language technology development for Kpelle and other low-resourced Mande languages

Building a Functional Machine Translation Corpus for Kpelle

Low-resource languages face significant challenges in natural language processing due to the scarcity of annotated data, linguistic resources, and the lack of language standardization, which leads to variations in grammar, vocabulary, and writing systems. This issue is particularly observed in many African languages, which significantly reduces their usability. To bridge this barrier, this paper investigates the challenges and limitations of collecting datasets for the Medumba language, a Grassfields Bantu language spoken in Cameroon, in the context of extremely low-resource natural language processing. We mainly focus on the specificity of this language, including its grammatical and lexical structure. Our findings highlight key barriers, including (1) the challenges in typing and encoding Latin scripts, (2) the absence of standardized translations for technical and scientific terms, and (3) the challenge of limited digital resources and financial constraints, highlighting the need to improve data strategies and collaboration to advance computational research on African languages. We hope that our study informs the development of better tools and policies to make knowledge platforms more accessible to extremely low-resource language speakers. We further discuss the representation of the language, data collection, parallel corpus development.

Challenges and Limitations in Gathering Resources for Low-Resource Languages: The Case of Medumba

Despite rapid advancements in multimodal large language models (MLLMs), their ability to process low-resource African languages in document-based visual question answering (VQA) tasks remains limited. This paper evaluates three state-of-the-art MLLMs—GPT-4o, Claude-3.5 Haiku, and Gemini-1.5 Pro—on WAEC/NECO standardized exam questions in Yoruba, Igbo, and Hausa. We curate a dataset of multiple-choice questions from exam images and compare model accuracies across two prompting strategies: (1) using English prompts for African language questions, and (2) using native-language prompts. While GPT-4o achieves over 90% accuracy for English, performance drops below 40% for African languages, highlighting severe data imbalance in model training. Notably, native-language prompting improves accuracy for most models, yet no system approaches human-level performance, which reaches over 50% in Yoruba, Igbo, and Hausa. These findings emphasize the need for diverse training data, fine-tuning, and dedicated benchmarks that address the linguistic intricacies of African languages in multimodal tasks, paving the way for more equitable and effective AI systems in education.

Challenging Multimodal LLM with African Standardized Exams: A Document VQA Evaluations

Building high-quality large language models (LLMs) for enterprise Arabic applications remains challenging due to the limited availability of digitized Arabic data. In this work, we present a data synthesis and refinement strategy to help address this problem, namely, by leveraging synthetic data generation and human-in-the-loop annotation to expand our Arabic training corpus. We further present our iterative post training recipe that is essential to achieving state-of-the-art performance in aligning the model with human preferences, a critical aspect to enterprise use cases. The culmination of this effort is the release of a small, 7B, open-weight model that outperforms similarly sized peers in head-to-head comparisons and on Arabic-focused benchmarks covering cultural knowledge, instruction following, RAG, and contextual faithfulness.

Command R7B Arabic: a small, enterprise-focused, multilingual, and culturally aware Arabic LLM

Pretrained language models (PLMs) for African languages are continually improving, but the reasons behind these advances remain unclear. This paper presents the first systematic investigation into how knowledge about African languages is encoded in PLMs. We train layer-wise probes for six typologically diverse African languages to ana\-lyse how linguistic features are distributed. We also design control tasks, a way to interpret probe performance, for the MasakhaPOS dataset. We find PLMs adapted for African languages to encode more linguistic information about target languages than massively multilingual PLMs. Our results reaffirm previous findings that token-level syntactic information concentrates in middle-to-last layers, while sentence-level semantic information is distributed across all layers. Through control tasks and probing baselines, we confirm that performance reflects the internal know\-ledge of PLMs rather than probe memorisation. Our study applies established interpretability techniques to African-language PLMs. In doing so, we highlight the internal mechanisms underlying the success of strategies like active learning and multilingual adaptation.

Designing and Contextualising Probes for African Languages


We present a scalable pipeline for automatically generating high-quality training data for web agents. In particular, a major challenge in identifying high-quality training instances is trajectory evaluation - quantifying how much progress was made towards task completion. We introduce a novel constraint-based evaluation framework that provides fine-grained assessment of progress towards task completion. This enables us to leverage partially successful trajectories, which significantly expands the amount of usable training data. We evaluate our method on a new benchmark we propose called BookingArena, which consists of complex booking tasks across 20 popular websites, and demonstrate that our distilled student model outperforms open-source approaches and matches or exceeds commercial systems, while being a significantly smaller model. Our work addresses the challenge of efficiently creating diverse, realistic web interaction datasets and provides a systematic evaluation methodology for complex structured web tasks.

Scaling Web Agent Training through Automatic Data Generation and Fine-grained Evaluation

This study proposes VisTRA (Visual Tool-use Reasoning Analyzer), a framework for analyzing how Visual Language Models (VLMs) utilize tools in VQA tasks involving small objects in high-resolution images. While tools like object detection and zoom functionality are essential for small object VQA, their potential errors necessitate careful verification of outputs. Our framework provides systematic evaluation of VLMs' tool-use capabilities through analysis of verification patterns. Using the V* bench dataset, we find that direct acceptance of tool outputs correlates with decreased VQA accuracy, while lower-performing models exhibit higher frequencies of cyclic verification loops. These findings offer insights for improving tool verification mechanisms in VLM architectures focused on small object detection tasks.

VisTRA: Visual Tool-use Reasoning Analyzer for Small Object Visual Question Answering

Differential Diagnosis (DDx) is a fundamental yet complex aspect of clinical decision-making, in which physicians iteratively refine a ranked list of possible diseases based on symptoms, antecedents, and medical knowledge. While recent advances in large language models (LLMs) have shown promise in supporting DDx, existing approaches face key limitations, including single-dataset evaluations, isolated optimization of components, unrealistic assumptions about complete patient profiles, and single-attempt diagnosis. We introduce a Modular Explainable DDx Agent (MEDDxAgent) framework designed for interactive DDx, where diagnostic reasoning evolves through iterative learning, rather than assuming a complete patient profile is accessible. MEDDxAgent integrates three modular components: (1) an orchestrator (DDxDriver), (2) a history taking simulator, and (3) two specialized agents for knowledge retrieval and diagnosis strategy. To ensure robust evaluation, we introduce a comprehensive DDx benchmark covering respiratory, skin, and rare diseases. We analyze single-turn diagnostic approaches and demonstrate the importance of iterative refinement when patient profiles are not available at the outset. Our broad evaluation demonstrates that MEDDxAgent achieves over 10% accuracy improvements in interactive DDx across both large and small LLMs, while offering critical explainability into its diagnostic reasoning process.

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