Morocco

As Vision Language Models (VLMs) become integral to real-world applications, understanding their demographic biases is critical. We introduce GRAS, a benchmark for uncovering demographic biases in VLMs across gender, race, age, and skin tone, offering the most diverse coverage to date. We further propose the GRAS Bias Score, an interpretable metric for quantifying bias. We benchmark five state-of-the-art VLMs and reveal concerning bias levels, with the least biased model attaining a GRAS Bias Score of 98, far from the unbiased ideal of 0. Our findings also reveal a methodological insight: evaluating bias in VLMs with visual question answering (VQA) requires considering multiple formulations of a question. Our code, data, and evaluation results are publicly available at https://anonymous.4open.science/r/vqa-skin-bias-5DA3/

EACL 2026

Ask Me Again Differently: GRAS for Measuring Bias in Vision Language Models on Gender, Race, Age, and Skin Tone

gras benchmark

vision language models

visual question answering

bias

## Welcome to EMNLP 2024! 
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EMNLP 2024

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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)* 

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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. 

Modern neural translation models based on the Transformer architecture are known for their high performance, particularly when trained on high-resource datasets. A standard next-token prediction training strategy, while widely adopted in practice, may lead to overlooked artifacts such as representation collapse. Previous works have shown that this problem is especially pronounced in the representation of the deeper Transformer layers, where it often fails to efficiently utilize the geometric space. Representation collapse is even more evident in end-to-end training of continuous-output neural machine translation, where the trivial solution would be to set all vectors to the same value. In this work, we analyze the dynamics of representation collapse at different levels of discrete and continuous NMT transformers throughout training. We incorporate an existing regularization method based on angular dispersion and demonstrate empirically that it not only mitigates collapse but also improves translation quality. Furthermore, we show that quantized models exhibit similar collapse behavior and that the benefits of regularization are preserved even after quantization.

Representation Collapse in Machine Translation Through the Lens of Angular Dispersion

Multimodal models excel in English, supported by abundant imageвЂ“text and audioвЂ“text data, but performance drops sharply for other languages due to limited multilingual multimodal resources. Existing solutions rely on machine translation, while advances in multilingual text modeling remain underutilized. We introduce M2M, a lightweight alignment method that learns only a few linear layersвЂ”using English text aloneвЂ”to map multilingual text embeddings into multimodal space. Despite its simplicity, M2M matches baseline performance in English (94.9% Recall@10) and achieves strong zero-shot transfer (89.5% Recall@10 averaged across 11 languages, 10 unseen) on XTD Text-to-Image retrieval. Qualitative t-SNE visualizations show that multilingual embeddings align tightly with multimodal representations, while weight analysis reveals that the transformation reshapes embedding geometry rather than performing trivial rotations. Beyond imageвЂ“text retrieval, M2M generalizes to AudioвЂ“Text retrieval and cross-lingual Text-to-Image generation. We release code, checkpoints, and multilingual evaluation datasets (https://github.com/m2m-codebase/M2M) to facilitate further research.

Multilingual-To-Multimodal (M2M): Unlocking New Languages with Monolingual Text

Generative models are known to have reduced performance in different global cultural contexts and languages. While continual data updates have been known to be conducted to improve overall model performance, bolstering and evaluating this cross-cultural competence of generative AI models requires data resources to be intentionally expanded to include global contexts and languages. In this work, we construct a multi-pronged pipeline to collect and contribute culturally salient, multilingual data. We posit that such data can assess the state of the global applicability of our models and thus, in turn, help identify and improve upon cross-cultural gaps.

Scaling Cultural Resources for Improving Generative Models

Retrieval-Augmented Generation (RAG) and Supervised Finetuning (SFT) have become the predominant paradigms for equipping Large Language Models (LLMs) with external knowledge for diverse, knowledge-intensive tasks. However, while such knowledge injection improves performance, it also exposes new attack surfaces. Membership Inference Attacks (MIAs), which aim to determine whether a given data sample was included in a model's training set, pose serious threats to privacy and trust in sensitive domains. To this end, we first systematically evaluate the vulnerability of RAG- and SFT-based LLMs to various MIAs. Then, to address the privacy risk, we further introduce a novel, model-agnostic defense framework, Ensemble Privacy Defense (EPD), which aggregates and evaluates the outputs of a knowledge-injected LLM, a base LLM, and a dedicated judge model to enhance resistance against MIAs. Comprehensive experiments show that, on average, EPD reduces MIA success by up to 27.8% for SFT and 526.3% for RAG compared to inference-time baseline, while maintaining answer quality.

Ensemble Privacy Defense for Knowledge-Intensive LLMs against Membership Inference Attacks

Instruction optimization provides a lightweight, model-agnostic approach to enhancing the reasoning performance of large language models (LLMs). This paper presents the first systematic comparison of instruction optimization for tabular fact verification, a task that requires numerical, compositional, and schema-grounded reasoning over structured data. We evaluate four prompting paradigms, direct prediction, Chain-of-Thought (CoT), ReAct with SQL tools, and CodeAct with Python execution, across three benchmarks (TabFact, PubHealthTab, SciTab) and two model families. Using the DSPy framework, we study three optimizers: COPRO, MiPROv2, and SIMBA. Instruction optimization consistently improves verification accuracy, with MiPROv2 yielding the most stable gains for CoT, and SIMBA providing the largest benefits for tool-augmented agents, particularly at larger model scales. Behavioral analyzes reveal that SIMBA encourages more direct reasoning paths by applying heuristics, which enhances numerical comparison in CoT reasoning and helps avoid unnecessary tool calls in ReAct agents. Comparing different pipelines, CoT remains effective for tabular fact checking, especially with smaller models. ReAct agent built with larger models can achieve competitive performance but requires careful instruction optimization.

Analyzing LLM Instruction Optimization for Tabular Fact Verification

Automatic workflow generation is the process of automatically synthesizing sequences of LLM calls, tool invocations, and post-processing steps for complex end-to-end tasks. Most prior methods cast this task as an optimization problem with limited theoretical grounding. We propose to cast workflow generation as Bayesian inference over a posterior distribution on workflows, and introduce \textbf{Bayesian Workflow Generation (BWG)}, a sampling framework that builds workflows step-by-step using parallel look-ahead rollouts for importance weighting and a sequential in-loop refiner for pool-wide improvements. We prove that, without the refiner, the weighted empirical distribution converges to the target posterior. We instantiate BWG as \textbf{BayesFlow}, a training-free algorithm for workflow construction. Across six benchmark datasets, BayesFlow improves accuracy by up to 9 percentage points over SOTA workflow generation baselines and by up to 65 percentage points over zero-shot prompting, establishing BWG as a principled upgrade to search-based workflow design.

BayesFlow: A Probability Inference Framework for Meta-Agent Assisted Workflow Generation

Scientific research is inherently global. However, the vast majority of academic journals are published exclusively in English, creating barriers for non-native-English-speaking researchers. In this study, we leverage large language models (LLMs) to translate published scientific articles while preserving their native JATS XML formatting, thereby developing a practical, automated approach for implementation by academic journals. Using our approach, we translate articles across multiple scientific disciplines into 28 languages. To evaluate translation accuracy, we introduce a novel question-and-answer (QA) benchmarking method and show an average performance of 95.9%, indicating that the key scientific details are accurately conveyed. In a user study, we translate the scientific papers of 15 researchers into their native languages. Interestingly, a third of the authors found many technical terms вЂњovertranslated,вЂќ expressing a preference to keep terminology more familiar in English untranslated. Finally, we demonstrate how in-context learning techniques can be used to align translations with domain-specific preferences such as mitigating overtranslation, highlighting the adaptability and utility of LLM-driven scientific translation.

Science Across Languages: Assessing LLM Multilingual Translation of Scientific Papers

Statutory article retrieval (SAR) targets retrieval of legislative provisions relevant to a natural language question. The lexical gap between everyday queries and specialized legal language, as well as the structural dependencies of statute law, makes it a challenging task. Here, we introduce JuriFindIT, the first SAR dataset for the Italian legal domain and the first to explicitly encode cross-article references extracted from national legal code. The dataset covers four macro-areasвЂ”civil law, criminal law, anti-money laundering and counter-terrorism, and privacyвЂ”and includes 895 expert-authored questions and 169,301 generated ones, linked to more than 23,000 statutory articles. We provide retrieval models fine-tuned on JuriFindIT, proposing a pipeline that integrates dense encoders with an heterogeneous legislative graph, achieving consistent improvements over prior SAR approaches.

JuriFindIT: an Italian legal retrieval dataset

This paper examines how Large Language Models (LLMs) reproduce societal norms, particularly heterocisnormativity, and how these norms translate into measurable biases in their text generations. We investigate whether explicit information about a subject's gender or sexuality influences LLM responses across three subject categories: queer-marked, non-queer-marked, and the normalized "unmarked" category. Representational imbalances are operationalized as measurable differences in English sentence completions across four dimensions: sentiment, regard, toxicity, and prediction diversity. Our findings show that Masked Language Models (MLMs) produce the least favorable sentiment, higher toxicity, and more negative regard for queer-marked subjects. Autoregressive Language Models (ARLMs) partially mitigate these patterns, while closed-access ARLMs tend to produce more harmful outputs for unmarked subjects. Results suggest that LLMs reproduce normative social assumptions, though the form and degree of bias depend strongly on specific model characteristics, which may redistributeвЂ”but not eliminateвЂ”representational harms.

QueerGen: How LLMs Reflect Societal Norms on Gender and Sexuality in Sentence Completion Task

Language models (LMs) may memorize personally identifiable information (PII) from training data, enabling adversaries to extract it during inference. Existing defense mechanisms such as differential privacy (DP) reduce this leakage, but incur large drops in utility. Based on a comprehensive study using circuit discovery to identify the computational circuits responsible PII leakage in LMs, we hypothesize that specific PII leakage circuits in LMs should be responsible for this behavior. Therefore, we propose PATCH: Privacy-Aware Targeted Circuit Patching, a novel approach that first identifies and subsequently directly edits PII circuits to reduce leakage. PATCH achieves better privacy-utility trade-off than existing defenses, e.g., reducing recall of PII leakage from LMs by up to 65%. Finally, PATCH can be combined with DP to reduce recall of residual leakage of an LM to as low as 0.01%. Our analysis shows that PII leakage circuits persist even after the application of existing defense mechanisms. In contrast, PATCH can effectively mitigate their impact.

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Representation Collapse in Machine Translation Through the Lens of Angular Dispersion

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A Unified Framework and Dataset for Assessing Societal Bias in Vision-Language Models