Morocco

Most vision-language models (VLMs) are trained on English-centric data, limiting their performance in other languages and cultural contexts. This restricts their usability for non-English-speaking users and hinders the development of multimodal systems that reflect diverse linguistic and cultural realities. In this work, we reproduce and adapt the LLaVA-Next methodology to create a set of Polish VLMs. We rely on a fully automated pipeline for translating and filtering existing multimodal datasets, and complement this with synthetic Polish data for OCR and culturally specific tasks. Despite relying almost entirely on automatic translation and minimal manual intervention, our approach yields strong results: we observe a +9.5 pp improvement over LLaVA‑1.6‑Vicuna‑13B on a Polish-adapted MMBench, along with higher-quality captions in generative evaluations, as measured by human annotators in terms of linguistic correctness. These findings highlight that large-scale automated translation, combined with lightweight filtering, can effectively bootstrap high-quality multimodal models for low-resource languages. Some challenges remain, particularly in cultural coverage and evaluation. To facilitate further research, we release our models, code, and datasets.

EACL 2026

Annotation-Efficient Vision-Language Model Adaptation to the Polish Language Using the LLaVA Framework

language-adaptation

polish-language llm

llava

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community."<br>

*Message from the General Chair, Dirk Hovy*





<br>

EMNLP 2025

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

poster

#### *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. 

E-commerce product understanding demands by nature, strong multimodal comprehension from text, images, and structured attributes. General-purpose Vision–Language Models (VLMs) enable generalizable multimodal latent modelling, yet there is no documented, well-known strategy for adapting them to the attribute-centric, multi-image, and noisy nature of e-commerce data, without sacrificing general performance. In this work, we show through a large-scale experimental study, how targeted adaptation of general VLMs can substantially improve e-commerce performance while preserving broad multimodal capabilities. Furthermore, we propose a novel extensive evaluation suite covering deep product understanding, strict instruction following, and dynamic attribute extraction.

Adapting Vision-Language Models for E-commerce Understanding at Scale

Tasks such as solving arithmetic equations, evaluating truth tables, and completing syllogisms are handled well by large language models (LLMs) in their standard form, but they often fail when the same problems are posed in logically equivalent yet obfuscated formats. To study this vulnerability, we introduce Logifus, a structure-preserving logical obfuscation framework, and, utilizing this, we present LogiQAte, a first-of-its-kind diagnostic benchmark with 1,108 questions across four reasoning tasks: (i) Obfus FOL (first-order logic entailment under equivalence-preserving rewrites), (ii) Obfus Blood Relation (family-graph entailment under indirect relational chains), (iii) Obfus Number Series (pattern induction under symbolic substitutions), and (iv) Obfus Direction Sense (navigation reasoning under altered directions and reference frames). Across all the tasks, evaluating six state-of-the-art models, we find that obfuscation severely degrades zero-shot performance, with performance dropping on average by 47% for GPT-4o, 27% for GPT-5, and 22% for reasoning model, o4-mini. Our findings reveal that current LLMs parse questions without deep understanding, highlighting the urgency of building models that genuinely comprehend and preserve meaning beyond surface form.

Don’t Judge a Book by its Cover: Testing LLMs’ Robustness Under Logical Obfuscation

This thesis investigates how polyvocal ontologies and Large Language Model (LLM) based Multi-Agent Systems (MAS) can operationalize perspective-aware knowledge extraction, preserving conflicting stakeholder interpretations as epistemically separable, queryable Knowledge Graphs (KGs). Current AI systems consolidate multiple perspectives into singular, decontextualized schemas, introducing representational bias and information loss. We propose a systematic framework addressing three interconnected research questions: (1) how to generate polyvocal ontology design patterns for high-stakes domains; (2) how to architect LLM-based MAS that extract perspective-conditioned facts while maintaining schema coherence and provenance traceability; and (3) whether such extractions achieve semantic diversity without sacrificing KG integrity. Evaluation is proposed on medical datasets, conducted with domain experts, to demonstrate the feasibility of perspective-aware extraction as a principled alternative to consensus-oriented KGs. Expected contributions include polyvocal ontology patterns, an ontology-orchestrated MAS extraction framework with auditable provenance, and empirical validation.

Thesis Proposal: A Multi-Agent System for Ontology-Based Perspective-Aware Knowledge Extraction

This paper introduces \textbf{DRAGOn}, method to design a RAG benchmark on a regularly updated corpus. It features recent reference datasets, a question generation framework, an automatic evaluation pipeline, and a public leaderboard.
Specified reference datasets allow for uniform comparison of RAG systems, while newly generated dataset versions mitigate data leakage and ensure that all models are evaluated on unseen, comparable data.
The pipeline for automatic question generation extracts the Knowledge Graph from the text corpus and produces multiple question-answer pairs utilizing modern LLM capabilities.
A set of diverse LLM-as-Judge metrics is provided for a comprehensive model evaluation.
We used Russian news outlets to form the datasets and demonstrate our methodology. We launch a public leaderboard to track the development of RAG systems and encourage community participation.

DRAGOn: Designing RAG On Periodically Updated Corpus

Writing style functions both as a vehicle of expression and as a marker of authorial identity. Stylometric methods enable automatic recognition of authors based on linguistic regularities, while recent advances in adversarial learning—demonstrate how data can be intentionally modified to prevent models from learning usable representations. Yet it remains unclear whether such perturbations, designed to disrupt machine learning processes, also influence human perception of style.

This thesis investigates how humans and models perceive writing style under controlled perturbations and whether manipulations that reduce algorithmic recognition likewise obscure stylistic identity for human readers. The study combines computational and behavioral approaches: constructing semantically controlled yet stylistically diverse text datasets, and conducting human evaluation experiments to compare recognition accuracy between models and readers.

The results are expected to clarify how linguistic cues contribute differently to human and algorithmic perception of style and to inform broader applications in authorship analysis, privacy-preserving text transformation, and creative expression. By situating writing style as a dimension of information quality, the research contributes to understanding how authenticity, anonymity, and expressivity interact in digital communication.

Thesis Proposal: Comparing Human and Model Perception of Writing Style under Controlled Perturbations

The use of large language models (LLMs) for generating responses to multiple-choice style questionnaires that were originally intended to be answered by humans is often a helpful or even necessary task, for example in persona simulation or during LLM alignment. Although the input and output versatility of generative LLMs is beneficial when adapting such questionnaires to machine use, it can be detrimental when mapping the generated text back to a closed set of possible answer options for evaluation or scoring. In this paper, we investigate the performance of smaller models for the classification of LLM outputs into the available answer options of multiple-choice questionnaires. We consider fine-tuned encoder-transformers as well as a rule-based approach on three datasets with differing answer option complexity. Surprisingly, we find that the best-performing neural approach still underperforms in comparison to our rule-based baseline, indicating that simple pattern-matching of answer options against LLM outputs might still be the most competitive solution for cleaning LLM responses to multiple-choice questionnaires.

An Evaluation of Classifiers for Mapping Generative LLM Responses to Answer Options of Multiple-choice Questionnaires

When language models are trained on synthetic data, they (student model) can covertly acquire behavioral traits from the data-generating model (teacher model). "Subliminal learning" refers to the transmission of traits from a teacher to a student model via training on data unrelated to those traits. Prior work demonstrated this in the training domains of number sequences, code, and math Chain-of-Thought traces including transmission of misaligned behaviors. 
We investigate whether transmission occurs through natural language paraphrases with fixed semantic content, and whether content explicitly contradicting the teacher's preference can block it. 
We find that training on paraphrases from a teacher system-prompted to love a particular animal increases a student's preference for that animal by up to 19 percentage points. This occurs when paraphrased content is semantically unrelated to the animal, or even when it explicitly expresses dislike.
The transmission succeeds despite aggressive filtering to ensure paraphrase fidelity.
This raises concerns for pipelines where models generate their own training data: content-based inspection cannot detect such transmission, and even preference-contradicting content fails to prevent it.

You Didn't Have to Say It Like That: Subliminal Learning from Faithful Paraphrases

The objective of this paper is to enhance machine translation for manga (Japanese comics) by developing and employing an image encoder that is capable of more accurately comprehending its visual context. Conventional manga machine translation systems have faced the challenge of lacking sufficient manga comprehension capabilities when utilizing image information. To address this issue, we propose a domain-adapted image encoder training method for manga. The proposed method involves training encoders to acquire visual features that consider the structural and sequential characteristics of the manga. This approach draws upon a technique that has proven to be highly effective in training language models. The image encoders trained by the proposed methods are used as visual processors in a multimodal machine translation model, and they are evaluated in a Japanese-English translation task. The experimental results demonstrate that the proposed method enhances the performance metrics for translation evaluation, such as BLEU and xCOMET, in comparison to the conventional method.

Domain Adaptation of Image Encoder for Multimodal Manga Translation

The landscape of Large Language Models remains predominantly English-centric, resulting in a significant performance gap for other major languages, such as French, especially in the context of Small Language Models (SLMs). Existing multilingual models demonstrate considerably lower performance in French compared to English, and research on efficient adaptation methods for French remains limited. To address this, we introduce Luth, a family of French-specialized SLMs: through targeted post-training on curated, high-quality French data, our models outperform all open-source counterparts of comparable size on multiple French benchmarks while retaining their original English capabilities. We further show that strategic model merging enhances performance in both languages, establishing Luth as a new state of the art for French SLMs and a robust baseline for future French-language research.

Luth: Efficient French Specialization for Small Language Models and Cross-Lingual Transfer

Existing user simulation approaches focus on generating user-like responses in dialogue. They often assume that the provided persona is sufficient for producing such responses, without verifying whether critical personas are supplied. This raises concerns about the validity of simulation results.
To address this issue, we study the task of identifying persona dimensions (e.g., ''whether the user is price-sensitive'') that are relevant but missing in simulating a user's reply for a given dialogue context.
We introduce PICQ-drama (constructed from TVShowGuess), a benchmark of context-aware choice questions, annotated with missing persona dimensions whose absence leads to ambiguous user choices. We further design diverse evaluation criteria for missing persona identification.
Benchmarking leading LLMs on our PICQ-drama dataset demonstrates the feasibility of this task. Evaluation across diverse criteria, along with further analyses, reveals cognitive differences between LLMs and humans and highlights the distinct roles of different persona categories in shaping responses.

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