Morocco

Data augmentation (DA) using large language models (LLMs) is a cost-effective method for generating synthetic data, particularly for tasks with scarce datasets. However, its potential remains largely underexplored, both in terms of augmentation configuration and evaluation of synthetic data. This paper investigates LLM-based synthetic data generation for irony and sarcasm, two subjective and context-dependent forms of figurative language. We propose a multi-aspect evaluation framework assessing synthetic data&#39;s utility-plausibility and extrinsic-intrinsic dimensions through four aspects: predictive performance, sample diversity, linguistic properties, and human judgment. Our findings indicate that other aspects of evaluation, like diversity and linguistic features, do not necessarily correlate with an increase in predictive performance, underscoring the importance of multi-faceted evaluation. This work highlights the potential of LLM-based DA for irony and sarcasm detection, offering insights into the linguistic competence of LLMs. As synthetic data becomes increasingly prevalent, our framework offers a broadly applicable and crucial evaluation method, particularly for linguistically complex tasks.

EACL 2026

A Multi-Aspect Evaluation Framework for Synthetic Data: Case Study on Irony and Sarcasm

synthetic data

data augmentation

evaluation

**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.

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Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

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EMNLP 2023

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

Data augmentation (DA) using large language models (LLMs) is a cost-effective method for generating synthetic data, particularly for tasks with scarce datasets. However, its potential remains largely underexplored, both in terms of augmentation configuration and evaluation of synthetic data. This paper investigates LLM-based synthetic data generation for irony and sarcasm, two subjective and context-dependent forms of figurative language. We propose a multi-aspect evaluation framework assessing synthetic data's utility-plausibility and extrinsic-intrinsic dimensions through four aspects: predictive performance, sample diversity, linguistic properties, and human judgment. Our findings indicate that other aspects of evaluation, like diversity and linguistic features, do not necessarily correlate with an increase in predictive performance, underscoring the importance of multi-faceted evaluation. This work highlights the potential of LLM-based DA for irony and sarcasm detection, offering insights into the linguistic competence of LLMs. As synthetic data becomes increasingly prevalent, our framework offers a broadly applicable and crucial evaluation method, particularly for linguistically complex tasks.

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)* 

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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. 

Large Language Models (LLMs) are rapidly being adopted by users across the globe, who interact with them in a diverse range of languages. At the same time, there are well-documented imbalances in the training data and optimisation objectives of this technology, raising doubts as to whether LLMs can accurately represent the cultural diversity of their broad user base. In this study, we look at LLMs and cultural values in particular, and examine how prompt language and cultural framing influence model responses and their alignment with human values in different countries.We do so by probing 10 LLMs with 63 items from the Hofstede Values Survey Module and World Values Survey, translated into 11 languages, and formulated as prompts with and without different explicit cultural perspectives.; Our study confirms that both prompt language and cultural perspective produce variation in LLM outputs, but with an important caveat: While targeted prompting can, to a certain extent, steer LLM responses in the direction of the predominant values of the corresponding countries, it does not overcome the modelsâ systematic bias toward the values associated with a restricted set of countries in our dataset: the Netherlands, Germany, the United States, and Japan. All tested models, regardless of their origin, exhibit remarkably similar patterns: They produce fairly neutral responses on most topics, with selective progressive stances on issues such as social tolerance. Alignment with cultural values of human respondents is improved more with an explicit cultural perspective than with a targeted prompt language. Unexpectedly, combining both approaches is no more effective than cultural framing with an English prompt. These findings reveal that LLMs occupy an uncomfortable middle ground: They are responsive enough to changes in prompts to produce variation, but they are also too firmly anchored to specific cultural defaults to adequately represent cultural diversity.

LLMs and Cultural Values: The Impact of Prompt Language and Explicit Cultural Framing

In this paper, we present PropGenie, a novel multi-agent framework based on large language models (LLMs) to deliver comprehensive real estate assistance in real-world scenarios. PropGenie coordinates eight specialized sub-agents, each tailored for distinct tasks, including search and recommendation, question answering, financial calculations, and task execution. To enhance response accuracy and reliability, the system integrates diverse knowledge sources and advanced computational tools, leveraging structured, unstructured, and multimodal retrieval-augmented generation techniques. Experiments on real user queries show that PropGenie outperforms both a general-purpose LLM (OpenAI’s o3-mini-high) and a domain-specific chatbot (Realty AI's Madison) in real estate scenarios. We hope that PropGenie serves as a valuable reference for future research in broader AI-driven applications.

PropGenie: A Multi-Agent Conversational Framework for Real Estate Assistance (presented virtually)

The globalization of education and rapid growth of online learning have made localizing educational content a critical challenge. Lecture materials are inherently multimodal, combining spoken audio with visual slides, which requires systems capable of processing multiple input modalities. To provide an accessible and complete learning experience, translations must preserve all modalities: text for reading, slides for visual understanding, and speech for auditory learning. We present \textbf{BOOM}, a multimodal multilingual lecture companion that jointly translates lecture audio and slides to produce synchronized outputs across three modalities: translated text, localized slides with preserved visual elements, and synthesized speech. This end-to-end approach enables students to access lectures in their native language while aiming to preserve the original content in its entirety. Our experiments demonstrate that slide-aware transcripts also yield cascading benefits for downstream tasks such as summarization and question answering. We release our Slide Translation code at \url{https://github.com/saikoneru/image-translator} and integrate it in Lecture Translator at \url{https://gitlab.kit.edu/kit/isl-ai4lt/lt-middleware/ltpipeline}\footnote{All released code and models are licensed under the MIT License}.

BOOM: : Beyond Only One Modality KIT's Multimodal Multilingual Lecture Companion

High-quality datasets are crucial for training effective state of the art machine translation systems. However, due to the data-intensive nature of these systems, they have to be trained on large amounts of text that can easily go beyond the scope of full human inspection. This makes the presence of noisy data that can degrade overall system performance a frequent and significant issue. While various approaches have been developed to identify and select only the highest-quality training examples, this is undesirable in scenarios where resources are limited. For this reason, we introduce AlignFix, an open-source tool for augmenting data, identifying and correcting errors in parallel corpora. Leveraging word alignments, AlignFix extracts consistent phrase pairs, enabling targeted replacements that can improve the dataset quality. Besides targeted replacements, the tool enables contextual augmentation by duplicating sentences and allowing users to substitute words with alternatives of their choice. The tool maintains and updates the underlying word alignments, thereby avoiding the costly recomputation. AlignFix runs locally in the browser, requires no installation, and ensures that all data remains entirely on the client side. It is released under Apache 2.0 license, encouraging broad adoption, reuse, and further development. A live demo is available at https://ifi-alignfix.uibk.ac.at.

AlignFix: A Tool for Parallel Corpora Augmentation and Refinement

As large language models (LLMs) are deployed widely, detecting and understanding bias in their outputs is critical. We present LLM BiasScope, a web application for side-by-side comparison of LLM outputs with real-time bias analysis. The system supports multiple providers (Google Gemini, DeepSeek, MiniMax, Mistral, Meituan, Meta Llama) and enables researchers and practitioners to compare models on the same prompts while analyzing bias patterns.
LLM BiasScope uses a two-stage bias detection pipeline: sentence-level bias detection followed by bias type classification for biased sentences. The analysis runs automatically on both user prompts and model responses, providing statistics, visualizations, and detailed breakdowns of bias types. The interface displays two models side-by-side with synchronized streaming responses, per-model bias summaries, and a comparison view highlighting differences in bias distributions.
The system is built on Next.js with React, integrates Hugging Face inference endpoints for bias detection, and uses the Vercel AI SDK for multi-provider LLM access. Features include real-time streaming, export to JSON/PDF, and interactive visualizations (bar charts, radar charts) for bias analysis. LLM BiasScope is available as an open-source web application, providing a practical tool for bias evaluation and comparative analysis of LLM behaviour.

LLM BiasScope: A Real-Time Bias Analysis Platform for Comparative LLM Evaluation

Large-scale scientific research on historical documents --- particularly medieval Arabic manuscripts --- remains challenging due to the need for advanced paleographic and linguistic training, the large volume of hand-written materials, and the absence of assisting software. In this paper, we propose InkSight, the first end-to-end Arabic manuscript analysis tool for manuscript-based analytics and research hypothesis testing. InkSight integrates three key components: (i) an Optical Character Recognition (OCR) module utilizing a Large Visual Language Model (LVLM); (ii) a lightweight document indexing and information retrieval module that enables query-based evidence retrieval from book-length manuscripts; and (iii) a flexible Large Language Model (LLM) prompting interface factually grounded to the given manuscript via Retrieval-Augmented Generation (RAG). Empirical evaluation on the existing KITAB OCR benchmark and our in-house dataset of ancient Arabic manuscripts has revealed that historical research can be effectively supported using smaller fine-tuned LVLMs without relying on larger proprietary models. The live demo for InkSight is available freely at: https://inksight.ru and the demo video is
at https://youtu.be/wPtxviLQH0A.

InkSight: Towards AI-Aided Historical Manuscript Analysis

We introduce JEEM, a benchmark designed to evaluate Vision-Language Models (VLMs) on visual understanding across four Arabic-speaking countries: Jordan, The Emirates, Egypt, and Morocco. JEEM includes the tasks of image captioning and visual question answering, and features culturally rich and regionally diverse content. This dataset aims to assess the ability of VLMs to generalize across dialects and accurately interpret cultural elements in visual contexts. In an evaluation of five prominent open-source Arabic VLMs and GPT-4o, we find that the Arabic VLMs consistently underperform, struggling with both visual understanding and dialect-specific generation. While GPT-4o ranks best in this comparison, the model's linguistic competence varies across dialects, and its visual understanding capabilities lag behind. This underscores the need for more inclusive models and the value of culturally-diverse evaluation paradigms.

JEEM: Vision-Language Understanding in Four Arabic Dialects

Classifying subtypes of primary progressive aphasia (PPA) from connected speech presents significant diagnostic challenges due to overlapping linguistic markers. This study benchmarks the performance of traditional machine learning models with various feature extraction techniques, transformer-based models, and large language models (LLMs) for PPA classification. Our results indicate that while transformer-based models and LLMs exceed chance-level performance in terms of balanced accuracy, traditional classifiers combined with contextual embeddings remain highly competitive. Notably, MLP using MentalBert's embeddings achieves the highest accuracy. These findings underscore the potential of machine learning for enhancing the automatic classification of PPA subtypes.

Detecting Primary Progressive Aphasia (PPA) from Text: A Benchmarking Study

Large Language Models (LLMs) often automatically revise facts in provided content to align with their internal knowledge, a behavior that, while aiming for factual accuracy, can detrimentally override source material. This paper systematically investigates and formally defines this critical issue as Harmful Factuality Hallucination, where LLMs unexpectedly correct perceived inaccuracies in the input, prioritizing global factual correctness over essential source fidelity. Moving beyond anecdotal evidence, we introduce a robust framework to induce and quantify Harmful Factuality by applying controlled soft (Gaussian Embedding Perturbation) and hard (LLM-Instructed Entity Replacement) entity perturbations. We evaluate a diverse set of open-source (e.g., Llama series) and commercial (e.g., GPT-4o) LLMs of varying scales across abstractive summarization, rephrasing, and context-grounded question-answering tasks. Our experiments reveal that Harmful Factuality is prevalent, with its incidence significantly influenced by model scale (larger models often exhibit higher rates), perturbation type, entity position within the source, and task characteristics. Furthermore, through analysis of Dual Presence outputs, we identify and categorize three core behavioral mechanisms that underlie this phenomenon. Importantly, we also demonstrate that a simple instructional defense prompt can substantially mitigate Harmful Factuality, reducing it by approximately 50% in several leading models. This research provides a foundational methodology and crucial insights for evaluating and alleviating source-conflicting behaviors, thereby supporting the development of more reliable and source-faithful LLM systems.

Harmful Factuality: LLMs Correcting What They Shouldn't

Over the past four decades, work at the intersection of Arabic and technology has evolved alongside major technical breakthroughs and shifting regional and global political dynamics. In this talk, I revisit the development of Arabic NLP and AI, from early foundational efforts to today’s large-scale generative systems, and highlight how each phase has engaged with distinct aspects of Arabic’s linguistic complexity. I reflect on the language’s ambiguous orthography, rich morphology, diglossic landscape, and wide geographic and historical reach, and on the growing research community committed to ensuring that AI technologies meaningfully support Arabic and its diverse cultural expressions. I conclude with a forward-looking vision for building a cohesive and sustainable ecosystem that advances Arabic in AI through strengthened training, deeper collaboration, and sustained innovation for the next generation of researchers and practitioners.

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