Morocco

We examine in how far a person&#39;s value system has an impact on their perception of offensiveness. For instance, a scholar is likely to be offended by being accused of reporting unverified claims whereas many non-scholars would not feel that way. Thus, we move away from the assumption that offensiveness can be defined through a universal perspective. Ultimately, such research aims to support personalized approaches to content moderation. Our main contribution is the introduction of a dataset consisting of neutrally-phrased sentences on controversial topics, evaluated by individuals from 4 different value systems. This allows us to identify offensiveness patterns across value systems and conduct classification experiments.

EACL 2026

The Relevance of Value Systems for Offensive Language Detection

hate-speech detection; language/cultural bias analysis; nlp datasets

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

We examine in how far a person's value system has an impact on their perception of offensiveness. For instance, a scholar is likely to be offended by being accused of reporting unverified claims whereas many non-scholars would not feel that way. Thus, we move away from the assumption that offensiveness can be defined through a universal perspective. Ultimately, such research aims to support personalized approaches to content moderation. Our main contribution is the introduction of a dataset consisting of neutrally-phrased sentences on controversial topics, evaluated by individuals from 4 different value systems. This allows us to identify offensiveness patterns across value systems and conduct classification experiments.

technical 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.
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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. 

Extending Large Language Models (LLMs) to new languages is challenging, with most methods proposed suffering from high computational cost and catastrophic forgetting of original model capabilities. Embedding relearning~\citep{artetxe-etal-2020-cross}, a technique that creates new tokenizers and tunes embeddings on fixed model weights for target language adaptation, is both light-weight and performant. However, it has only been shown to work for older generation encoder-only models and for high resource languages. In this paper, we extend this framework to decoder-only LLMs focusing on joint adaptation to many languages, including low-resource ones. We experiment in three language groups over 100 languages each. We adapt a pre-trained LLM via switching to a customized tokenizer, and relearning the embedding layer. Across 96 diverse languages spanning both classification and generation tasks, we show embedding relearning improves \texttt{Gemma2} models by up to 20%, being highly competitive with full-weight updating baselines while vastly more computationally efficient and mitigating catastrophic forgetting. This translates into better results in transferring the improved multilingual performance to tasks that build on core English abilities (e.g., multilingual math reasoning), compared to various baselines. Further analysis reveals the critical role of customizing tokenizers in achieving effective language transfer, particularly for non-Latin script languages.

Tokenizer-Aware Cross-Lingual Adaptation of Decoder-Only LLMs through Embedding Relearning and Swapping

Large language models (LLMs) are safety-aligned to prevent harmful response generation, yet still remain vulnerable to jailbreak attacks. While prior works have focused on improving jailbreak attack effectiveness, they offer little explanation for why safety alignment fails. We address this gap by framing jailbreaks as inference-time alignment, connecting attack design and safety alignment within a unified optimization framework. This framing allows us to extend best-of-N inference-time alignment to the adversarial setting, called LIAR (Leveraging Inference-time Alignment to jailbReak), and derive suboptimality bounds that show LIAR provably approaches an optimal jailbreak as compute scales. Interestingly, our framework allows us to develop the notion of a Safety-Net, a measure of how vulnerable an LLM is to jailbreaks, which helps to explain why safety alignment can fail. Empirically, LIAR produces natural, hard-to-detect prompts that achieve a competitive attack success rate while running 10 to 100x faster than prior suffix-based jailbreaks.

Jailbreaks as Inference-Time Alignment: A Framework for Understanding Safety Failures in LLMs

Large language models (LLMs) often respond confidently to questions even when they lack the necessary information, leading to hallucinated answers. In this work, we study the problem of (un)answerability detection in extractive question answering (QA), where the model should determine if a passage contains sufficient information to answer a given question. We propose a simple approach that identifies a direction in the model's activation space that captures unanswerability and uses it for classification. This direction is selected by applying activation additions during inference and measuring their impact on the model's abstention behavior. We show that projecting hidden activations onto this direction yields a reliable score for (un)answerability classification. Experiments on two open-weight LLMs and four QA benchmarks show that our method effectively detects unanswerable questions and generalizes better across datasets than existing prompt-based and classifier-based approaches. Causal interventions reveal that adding the direction increases abstention, while ablating it suppresses it, further indicating that it captures an unanswerability signal.

Detecting (Un)answerability in Large Language Models with Linear Directions

We consider coverless steganography where a Large Language Model (LLM) is used to generate stego-texts in combination with arithmeticic coding. An efficient method should embed secret bits in as few language tokens as possible while keeping the stego-text as natural as possible. We show that this problem is equivalent to maximizing the entropy of a replacement probability distribution of the next token generation, subject to a constraint on the divergence between the new distribution and the original one produced by the LLM. A closed-form solution is provided under either the KL divergence or the total variation constraint. Several important practical issues are also tackled: 1) An often-overlooked tokenization mismatch issue is resolved with a simple prompt selection approach, 2) The combination of the optimized distribution and the vocabulary truncation technique is considered, and 3) The incorporation of the proposed approach with existing (potentially non arithemtic coding based) techniques, e.g., the Discop technique.

OD-Stega: LLM-Based Relatively Secure Steganography via Optimized Distributions

As text-to-image models become increasingly prevalent, ensuring their equitable performance across diverse cultural contexts is critical. Efforts to mitigate cross-cultural biases have been hampered by trade-offs, including a loss in performance, factual inaccuracies, or offensive outputs. Despite widespread recognition of these challenges, an inability to reliably measure these biases has stalled progress. To address this gap, we introduce CAIRE (https://github.com/siddharthyayavaram/CAIRE), an evaluation metric that assesses the degree of cultural relevance of an image, given a user-defined set of labels. Our framework grounds entities and concepts in the image to a knowledge base and uses factual information to give independent graded judgments for each culture label. On a manually curated dataset of culturally salient but rare items built using language models, CAIRE surpasses all baselines by 22% F1 points. Additionally, we construct two datasets for culturally universal concepts, one comprising of T2I generated outputs and another retrieved from naturally-occurring data. CAIRE achieves Pearson’s correlations of 0.56 and 0.66 with human ratings on these sets, based on a 5-point Likert scale of cultural relevance. This demonstrates its strong alignment with human judgment across diverse image sources.

CAIRE: Cultural Attribution of Images with Retrieval

1960s Tamil cinema's musical heritage lacks adequate metadata identifying playback singers in archival recordings. We present a quality-aware adversarial ensemble approach addressing two critical challenges: (1) variable audio degradation requiring adaptive model selection, and (2) instrumentation leakage confounding singer-specific features. We curate 348 annotated clips (12 hours) spanning 48 singers from 179 films. Our methodology introduces: a reliability estimation network dynamically gating five complementary pre-trained speaker models (Wav2Vec2, ECAPA-TDNN, WeSpeaker, CAM++, ERes2NetV2) based on degradation characteristics; adversarial training disentangling singer identity from accompaniment style; and uncertainty-calibrated predictions for human-in-the-loop workflows. On a held-out test set of 52 clips, we achieve 96.2\% accuracy (95\% CI: [87.5\%, 99.2\%]) and 2.0\% EER (95\% CI: [1.2\%, 3.1\%]), representing 7.7\% absolute improvement over the best single model and 2.0\% over static ensemble fusion. Ablations show quality-aware gating contributes 2.0\% and adversarial disentanglement 2.0\% beyond standard ensembles. We publicly release the dataset and code with fixed splits.

Quality-Aware Adversarial Ensemble for Singer Identification in 1960s Tamil Film Music

Language of study is an aspect of computational linguistics papers that is useful for analyses of trends and diversity in computational linguistics. This study introduces the first benchmark and evaluation of automated language of study extraction from computational linguistics publications. The benchmark containing 431 publications from the ACL Anthology, with 62 languages analyzed, was annotated. SciBERT and four large language models (LLMs), GPT-4o mini, Gemini 2.5 Flash, Claude 3.5 Haiku, and DeepSeek 3.2, were evaluated on the benchmark using different parts of the ACL Anthology papers. GPT-4o mini achieved the best exact match and Jaccard agreement scores of 0.646 and 0.687, respectively, which is slightly less than the agreement in human annotation. Gemini 2.5 Flash achieved the best micro F1 of 0.633. Models using the abstract for extraction were competitive with models using the full text, showing that accuracy can be achieved in language of study extraction without high computational costs. These findings demonstrate that LLMs are able to accurately identify the languages of study in computational linguistics papers, potentially reducing the time and cost of analyses in computational linguistics.

A Benchmark and Evaluation of Automated Language of Study Extraction from Computational Linguistics Publications

Protagonists play a central role in moral discourse by structuring responsibility and authority, yet computational work has largely focused on moral values rather than the actors involved. We address this gap by studying phrase-level protagonist detection and classification in the Moralization Corpus (Becker et al., 2025), a dataset of moral arguments across different text genres. 
We decompose the task into identifying protagonist mentions and classifying them by what kind of actor they are (e.g., individual or institution) and what function they serve in the moral argument.
We compare fine-tuned lightweight models, state-of-the-art NER models, and prompting-based large language models. We further establish human baselines and analyze the impact of contextual information on human and model decisions. Our results show that fine-tuned NER models achieve competitive detection performance at substantially lower cost than prompted large language models, and that role classification benefits more strongly from contextualized prompting. Across tasks, top-performing models reach or exceed human-level performance, highlighting the value of task decomposition for modeling protagonists in moral discourse.
We release our code, predictions, and supplementary material in our project repository.

Who Plays Which Role? Protagonist Detection and Classification in Moral Discourse

Large Language Models (LLMs) are increasingly integrated into high-stakes applications, making robust safety guarantees a central practical and commercial concern. Existing safety evaluations predominantly rely on fixed collections of harmful prompts, implicitly assuming non-adaptive adversaries and thereby overlooking realistic attack scenarios in which inputs are iteratively refined to evade safeguards. In this work, we examine the vulnerability of contemporary language models to automated, adversarial prompt refinement. We repurpose black-box prompt optimization techniques, originally designed to improve performance on benign tasks, to systematically search for safety failures. Using DSPy, we apply three such optimizers to prompts drawn from HarmfulQA and JailbreakBench, explicitly optimizing toward a continuous danger score in the range 0 to 1 provided by an independent evaluator model (GPT-5.1). Our results demonstrate a substantial reduction in effective safety safeguards, with the effects being especially pronounced for open-source small language models. For example, the average danger score of Qwen 3 8B increases from 0.09 in its baseline setting to 0.79 after optimization. These findings suggest that static benchmarks may underestimate residual risk, indicating that automated, adaptive red-teaming is a necessary component of robust safety evaluation.

When Prompt Optimization Becomes Jailbreaking: Adaptive Red-Teaming of Large Language Models

Sign language lexicographers construct bilingual dictionaries by establishing word-to-sign mappings, where polysemous and homonymous words corresponding to different signs across contexts are often underrepresented. A usage-based approach examining how word senses map to signs can identify such novel mappings absent from current dictionaries, enriching lexicographic resources.
We address this by analyzing German and German Sign Language (Deutsche Gebärdensprache, DGS), manually annotating 1,404 word use–to–sign ID mappings 
derived from 32 words from the German Word Usage Graph (D-WUG) and 49 signs from the Digital Dictionary of German Sign Language (DW-DGS). We identify three correspondence types: Type 1 (one-to-many), Type 2 (many-to-one), and Type 3 (one-to-one), plus No Match cases. We evaluate computational methods: Exact Match (EM) and Semantic Similarity (SS) using SBERT embeddings. SS substantially outperforms EM overall 88.52\% vs. 71.31\%), with dramatic gains for Type 1 (+52.1 pp). Our work establishes the first annotated dataset for cross-modal sense correspondence and reveals which correspondence patterns are computationally tractable.
Our code and dataset are made publicly available

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