Morocco

Large language models are extremely sensitive to prompt design, a phenomenon which is amplified in multilingual scenarios especially with low-resource languages due to low coverage in model training data, orthographic variation and tokenization issues.
In this work, we evaluate a reflective prompt evaluation technique (GEPA) as an inference time optimization strategy on multiple multilingual benchmarks spanning various African languages.
Using a more capable model as a reflection model and operating under strict optimization budgets - we show that reflective prompt optimization enables inference time improvements through textual policy evolution resulting in consistent improvements on different tasks without any weight updates. 

Our evaluation mostly focuses on closed-source models and we observe that on some of the benchmarks, prompt-optimized smaller models can outperform better and more recent models highlighting the importance of instruction design for measuring the capabilities of a language model on a given task.
Qualitative review of model outputs before and after optimization also shows that prompt evolution not only reinforces models ability to perform a particular task, it also improves output formatting which is very important for proper model evaluation.
We characterize the resulting prompt-latency tradeoff by quantifying the optimization cost in terms of prompt token growth, and our results show that modest increase in prompt size can result in substantial gains in performance. Finally, we argue that benchmark evaluations should report prompt-optimized results alongside baseline prompts in order to properly reflect model capabilities for low-resource languages.

EACL 2026

What Do Prompts Reveal About Model Capabilities in Low-Resource Languages?

prompt optimizatization

low resource languages

workshop paper

#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)*<br><br>

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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

BambaraMLLM is a unified multilingual multimodal large language model (MMLLM) designed to address the critical lack of digital resources for Bambara, a West African language spoken by over 15 million people. Unlike traditional approaches that rely on task-specific models for different linguistic functions, BambaraMLLM integrates text generation, automatic speech recognition (ASR), machine translation (MT), and text-to-tpeech (TTS) synthesis into a single, transformer-based architecture. This work establishes a scalable, open-source foundation for African language technology, optimizing for both performance and deployment under resource constraints.

BambaraMLLM: A Unified Multilingual Multimodal Large Language Model for Comprehensive Bambara Language Processing

This paper presents a Multilingual Vision Large Language Model, named M-MiniGPT4. Our model exhibits strong vision-language understanding (VLU) capabilities across 40+ languages. We utilize a mixture of native multilingual and translated data to push the multilingual VLU performance of the MiniGPT4 architecture. In addition, we propose a multilingual alignment training stage that uses parallel text corpora to further enhance the multilingual capabilities of our model. M-MiniGPT4 achieves 36\% accuracy on the multilingual MMMU benchmark, which is competitive with the English MMMU scores of state-of-the-art models in the same weight class. We open-source our models, code, and translated datasets to facilitate future research in low-resource and multilingual settings.

M-MiniGPT4: Multilingual VLLM Alignment via Translated Data

This preliminary work addresses the critical gap in multimodal Large Language Models (LLMs) for African languages, which remain underrepresented despite their rich multimodal communication traditions. We propose a framework that leverages simulated multimodal data and cross-lingual transfer learning to bootstrap multimodal capabilities. Our initial experiments with Swahili demonstrate that proxy multimodal embeddings can be effectively generated using pre-trained encoders, achieving an average cosine similarity of 0.72 for culturally relevant concepts. We further show that simple fusion methods can effectively combine these embeddings, and that transfer learning from high-resource languages yields a 28% improvement in multimodal alignment over zero-shot approaches. These results validate the feasibility of our approach and provide a foundation for culturally-aware multimodal LLMs in low-resource African language contexts.

Towards Multimodal Cultural Context Modeling for African Languages in Large Language Models

Low-resource languages serve as invaluable repositories of human history, preserving cultural and intellectual diversity. Despite their significance, they remain largely absent from modern natural language processing systems. While progress has been made for widely spoken African languages like Swahili, Yoruba, and Amharic, smaller indigenous languages such as Efik continue to be underrepresented in machine translation research. This study evaluates the effectiveness of AI-based models in translating between English and Efik, leveraging a small-scale, community-curated parallel corpus. We fine-tuned the mT5 multilingual model on our manually curated $N = 13{,}865$ sentence parallel corpus. The model achieved a BLEU score of $15.61$ and a chrF score of $35.04$, demonstrating reasonable translation performance for a low-resource language. Our work demonstrates the feasibility of developing practical machine translation tools for low-resource languages and highlights the importance of inclusive data practices and culturally grounded evaluation in advancing equitable NLP.

Developing an English–Efik Corpus and Machine Translation System for Digitization Inclusion

Despite serving over 100 million speakers as a vital African lingua franca, Swahili remains critically under-resourced for Natural Language Processing, hindering technological progress across East Africa. We present a scalable solution: a controllable synthetic data generation pipeline that produces culturally grounded Swahili text for sentiment analysis, validated through automated LLM judges. To ensure reliability, we conduct targeted human evaluation with a native Swahili speaker on a stratified sample, achieving 80.95% agreement between generated sentiment labels and human ground truth, with strong agreement on judge quality assessments. This demonstrates that LLM-based generation and quality assessment can transfer effectively to low-resource languages. We release a dataset and provide a reproducible pipeline in tandem providing ample knowledge and working material for NLP researchers in low-resource contexts.

Synthetic Data Generation Pipeline for Low-Resource Swahili Sentiment Analysis: Multi-LLM Judging with Human Validation

Lemmatization, which reduces words to their
root forms, plays a key role in tasks such as information retrieval, text indexing, and machine
learning-based language models. However, a
key research challenge for low-resourced languages such as the Somali is the lack of humanannotated lemmatization datasets and reliable
ground truth to underpin accurate morphological analysis and training relevant NLP models. To address this problem, we developed the
first large-scale, purpose-built Somali lemmatization lexicon, coupled with a crowdsourcing
platform for ongoing expansion. The system
leverages Somali’s agglutinative and derivational morphology, encompassing over 5,584
root words and 78,629 derivative forms, each
annotated with part-of-speech tags. For data
validation purpose, we have devised a pilot
lexicon based lemmatizer integrated with rulebased logic to handle out-of-vocabulary terms.
Evaluation on a 294-document corpus spanning news articles, social media posts, and
short messages shows lemmatization accuracies of 51.27% for full articles, 44.14% for
excerpts, and 59.51% for short texts such as
tweets. These results demonstrate that combining lexical resources, POS tagging, and rulebased strategies provides a robust and scalable
framework for addressing morphological complexity in Somali and other low-resource languages

Morphologically-informed Somali Lemmatization Corpus built with a Web-based Crowdsourcing Platform

Text-to-Speech (TTS) technology offers potential to improve exam accessibility for visually impaired learners, but existing systems often underperform in underrepresented languages like Yoruba. This study evaluates current Yoruba TTS models in delivering standardized exam content to five visually impaired students through a web-based interface. Before testing, four Yoruba TTS systems were compared; only Facebook’s mms-tts-yor and YarnGPT produced intelligible Yoruba speech. Students experienced exam questions delivered by human voice, Braille, and TTS. All preferred Braille for clarity and independence, some valued human narration, while TTS was least favored due to robotic and unclear output. These results reveal a significant gap between TTS capabilities and the needs of users in low-resource languages. The paper highlights the urgency of developing tone-aware, user-centered TTS solutions to ensure equitable access to digital education for visually impaired speakers of underrepresented languages

Can Text-to-Speech Systems enable Inclusive Computer-Based Testing? An Evaluation of Yoruba TTS for Visually Impaired Learners

Climate change refers to variations in temperature and weather conditions due to various climate-related factors on earth.These factors vary across regions, and people's perceptions of climate change. Analyzing public opinion on climate change at a regional level is crucial for developing targeted solutions. However, manually analyzing large volumes of data is challenging for informed dissension. Applying emerging pre-trained Large Language Models offers a promising solution for efficiently analyzing large datasets and understanding public perspectives on climate change. Amharic is one of the widely spoken African languages. Many speakers of the language are actively discussing and expressing their opinions on various topics, including climate change, on social media. Given the increasing discussions about climate change, this study focuses on the sentiment analysis of Amharic climate texts. We collected 6013 sentences from social media and news sources. The data is annotated manually by native speakers to its target polarity. We conducted experiments using the LLM that supports African languages during pre-training. In this study, MultilingualBert and AfriBERTa models were employed with hyperparameter tuning to perform sentiment polarity analysis on Amharic climate text. The experimental results shows that MultilingualBert outperforms AfriBERTa, achieving an accuracy of 69%. This performance is attributed to MultilingualBert’s enhanced capability to capture token-level semantics by giving a variety of attention across tokens, thereby improving its contextual understanding in downstream sentiment classification tasks

Analyzing Sentiment Polarity in Amharic Climate Change Discussions Using Large Language Models

Given the advancement of various Artificial Intelligence (AI) technologies in the 21st century, Automatic Speech Recognition (ASR) plays a vital role in human and machine interaction and serves as an interface for a wide range of applications. The development of these high-performing, robust and useful technologies continue to gain more attention on high-resource languages due to high availability of language data, market profitability dominance and access to funding and research initiatives compared to the marginalised low-resource languages. Despite efforts to develop ASR systems for African languages, there are still numerous challenges due to limited speech datasets, tonal complexity and dialectal variation. In this study, we curated a domain-specific speech dataset for one of the oral Yoruba literatures, proverbs, which are highly culturally inclined. We used the Yoruba recording app that was developed for Iroyin-speech project to record 6 hours of Yoruba proverb sentences. The NCAIR1/Yoruba-ASR model which was finetuned on Open AI Whisper Small and Massively Multilingual Speech, a multilingual speech model featuring low-resource languages including Yoruba language was evaluated with the recorded Yoruba proverbs. Evaluation was conducted based on Word Error Rate (WER) and Tone Error Rate (TER). Our result shows that current ASR systems that support Yoruba does not capture cultural nuances. These findings highlight an urgent need to curate more robust speech datasets that are culturally embedded for low resource languages and in this case particularly, Yoruba language in order to build technological tools that preserve African culture, language and identity.

ÒWE-Voice: An Evaluation of Monolingual and Multilingual ASR Model Using Yoruba Proverb Speech Dataset

Travel agencies in many African countries face increasing pressure to handle large volumes of customer inquiries with limited staff or, either non-existent or outdated rule-based chat-bots. To address this challenge, we develop a conversational virtual assistant powered by a Large Language Model (LLM) and enhanced with a Retrieval-Augmented Generation (RAG) pipeline. The system combines LLM reasoning, company-specific knowledge retrieval, and real-time API (Application Programming Interface) integration to deliver accurate, context-aware responses through WhatsApp, the region’s most widely used communication platform. A dedicated web interface enables staff to upload and update internal documents, ensuring that the assistant remains aligned with changing service information. Demonstrations show that the proposed solution improves response speed, enhances user experience, and reduces operational burden.

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