Morocco

We present STAMP (Selective Task-Aware Mechanism for Text Privacy), a new framework for task-aware text privatization that achieves an improved privacy–utility trade-off. STAMP selectively allocates privacy budgets across tokens by jointly considering (i) each token’s importance to the downstream task (as measured via a task- or query-specific representation), and (ii) its privacy sensitivity (e.g., names, dates, identifiers). This token-level partitioning enables fine-grained, group-wise control over the level of noise applied to different parts of the input, balancing privacy protection with task relevance. To privatize individual token embeddings, we introduce the polar mechanism, which perturbs only the direction of embeddings on the unit sphere while preserving their magnitude. Decoding is performed via cosine nearest-neighbor search, aligning the perturbation geometry with the decoding geometry. Unlike isotropic noise mechanisms, the polar mechanism maintains semantic neighborhoods in the embedding space and better preserves downstream utility. Experimental evaluations on SQuAD, Yelp, and AG News datasets demonstrate that STAMP, when combined with the normalized polar mechanism, consistently achieves superior privacy–utility trade-offs across varying per-token privacy budgets.

EACL 2026

STAMP: Selective Task-Aware Mechanism for Text Privacy

text privacy

security and privacy

differential privacy

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

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.
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. 

With recent advancements in large language models, web agents have been greatly improved. However, dealing with complex and dynamic web environments requires more advanced planning and search abilities. Previous studies usually adopt a greedy one-way search strategy, which may struggle to recover from erroneous states. In this work, we enhance web agents with an explicit rollback mechanism, enabling the agent to revert back to a previous state in its navigation trajectory. This mechanism gives the model the flexibility to directly control the search process, leading to an effective and efficient web navigation method. We conduct experiments on two live web navigation benchmarks with zero-shot and fine-tuning settings. The results demonstrate the effectiveness of our proposed approach.

WebRollback: Enhancing Web Agents with Explicit Rollback Mechanisms

Large Language Models (LLMs) suffer severe catastrophic forgetting when adapted sequentially to new tasks in a continual learning (CL) setting. Existing approaches are fundamentally limited: replay-based methods are impractical and privacy-violating, while strict orthogonality-based methods collapse under scale: each new task is projected onto an orthogonal complement, progressively reducing the residual degrees of freedom and eliminating forward transfer by forbidding overlap in shared representations. In this work, we introduce ELLA, a training framework built on the principle of selective subspace de-correlation. Rather than forbidding all overlap, ELLA explicitly characterizes the structure of past updates and penalizes alignments along their high-energy, task-specific directions, while preserving freedom in the low-energy residual subspaces to enable transfer. Formally, this is realized via a lightweight regularizer on a single aggregated update matrix. We prove this mechanism corresponds to an anisotropic shrinkage operator that bounds interference, yielding a penalty that is both memory- and compute-constant regardless of task sequence length. ELLA requires no data replay, no architectural expansion, and negligible storage. Empirically, it achieves state-of-the-art CL performance on three popular benchmarks, with relative accuracy gains of up to 9.6% and a 35x smaller memory footprint. Further, ELLA scales robustly across architectures and actively enhances the model's zero-shot generalization performance on unseen tasks, establishing a principled and scalable solution for constructive lifelong LLM adaptation.

ELLA: Efficient Lifelong Learning for Adapters in Large Language Models

Explicit representations of predicate-argument relations form the basis of interpretable semantic analysis, supporting reasoning, generation, and evaluation. However, attaining such semantic structures requires costly annotation efforts and has remained largely confined to English. We leverage the Question-Answer driven Semantic Role Labeling (QA-SRL) framework --- a natural-language formulation of predicate-argument relations --- as the foundation for extending semantic annotation to new languages. To this end, we introduce a cross-linguistic projection approach that reuses an English QA-SRL parser within a constrained translation and word-alignment pipeline to automatically generate question-answer annotations aligned with target-language predicates. Applied to Hebrew, Russian, and French --- spanning diverse language families --- the method yields high-quality training data and fine-tuned, language-specific parsers that outperform strong multilingual LLM baselines (GPT-4o, LLaMA-Maverick). By leveraging QA-SRL as a transferable natural-language interface for semantics, our approach enables efficient and broadly accessible predicate-argument parsing across languages.

Effective QA-Driven Annotation of PredicateвЂ“Argument Relations Across Languages

Historical language models play a crucial role in the study of languages, and can benefit tasks such as named-entity recognition (NER), part-of-speech (PoS) tagging, and post-OCR correction, among others. Despite their relevance, most efforts have been concentrated on English. To the best of our knowledge, no such model exists for historical Portuguese. In this work, we introduce PortOldBERT, the first historical Portuguese encoder language model. We demonstrate its usefulness by comparing PortOldBERT's performance with Albertina, the encoder on which it is based, across multiple tasks---pseudo-perplexity, NER, PoS tagging, word error rate (WER) prediction, and OCR error detection---and for different historical periods. PortOldBERT consistently outperforms Albertina in historical data, demonstrating its ability to effectively integrate historical linguistic contexts while retaining the ability to process contemporary text.

PortOldBERT: Portuguese Historical Language Models

Emotion classification, a vital aspect of natural language processing (NLP), plays a significant role in emotion prediction and harmful content detection. Recent advancements in NLP, particularly through large language models (LLMs), have greatly improved outcomes in this field. This study introduces ViGoEmotion - a Vietnamese emotion corpus comprising 20,664 social media comments in which each comment is classified into 27 fine-grained distinct emotions. To evaluate the quality of the dataset and its impact on emotion classification, eight pre-trained Transformer-based models were evaluated under three preprocessing strategies: preserving original emojis with rule-based normalization, converting emojis into textual descriptions, and applying ViSoLex, a model-based lexical normalization system. Results show that converting emojis into text often improves the performance of several BERT-based baselines, while preserving emojis yields the best results for ViSoBERT and CafeBERT. In contrast, removing emojis generally leads to lower performance. ViSoBERT achieved the highest Macro F1-score of 61.50% and Weighted F1-score of 63.26%. Strong performance was also observed from CafeBERT and PhoBERT.These findings highlight that while the proposed corpus can support diverse architectures effectively, preprocessing strategies and annotation quality remain key factors influencing downstream performance.

ViGoEmotions: A Benchmark Dataset For Fine-grained Emotion Detection on Vietnamese Texts

Current evaluations of sentence embedding models typically rely on static test beds such as the Massive Text Embedding Benchmark (MTEB). While invaluable, repeated tuning on a fixed suite can inflate reported performance and obscure real-world robustness. We introduce the Paraphrasing Text Embedding Benchmark (PTEB), a dynamic protocol that stochastically generates meaning-preserving paraphrases at evaluation time and aggregates results across multiple runs. Using a cost-efficient LLM-based method grounded in semantic textual similarity gold ratings, we show that LLMs generate token-diverse but semantically preserving, paraphrases. Across 7 MTEB tasks, we validate our hypothesis that the performance of sentence encoders is sensitive to changes in token space even when semantics remain fixed. We also observe that smaller models are not disproportionately affected relative to larger ones. Our results are statistically robust over multiple runs and we extended our experiments to 3 multilingual datasets covering 10 languages. More generally, we aim to propose a new evaluation paradigm in NLP that relies less on static, pre-defined benchmarks but shifts towards dynamic, stochastic evaluation leveraging eval-time compute. We make the code to run PTEB publicly available.

PTEB: Towards Robust Text Embedding Evaluation via Stochastic Paraphrasing at Evaluation Time with LLMs

The increasing reliance on Large Language Models (LLMs) across various domains extends to education, where students progressively use generative AI as a tool for learning. While prior work has examined LLMs' mathematical ability, their reliability in grading authentic student problem-solving processes and delivering effective feedback remains underexplored. This study introduces MathEDU, a dataset consisting of student problem-solving processes in mathematics and corresponding teacher-written feedback. We systematically evaluate the reliability of various models across three hierarchical tasks: answer correctness classification, error identification, and feedback generation. Experimental results show that fine-tuning strategies effectively improve performance in classifying correctness and locating erroneous steps. However, the generated feedback across models shows a considerable gap from teacher-written feedback. Critically, the generated feedback is often verbose and fails to provide targeted explanations for the student's underlying misconceptions. This emphasizes the urgent need for trustworthy and pedagogy-aware AI feedback in education.

MathEDU: Feedback Generation on Problem-Solving Processes for Mathematical Learning Support

While large language models (LLMs) offer great promise, they also pose concrete safety risks. To audit and mitigate these risks, researchers have developed automated red-teaming methods, which generate adversarial prompts to elicit unsafe behavior of target LLMs during evaluation. Recent automated red-teaming methods for LLMs face a persistent trade-off: techniques that increase prompt diversity often reduce the level of the toxicity elicited from the target LLMs, while toxicity-maximizing methods tend to collapse diversity. To address the limitations, we propose ToxiPrompt, a two-stage framework that explicitly separates exploration (diversity) from exploitation (toxicity) and reunifies them with a single selection criterion to balance between diversity and toxicity. Experimental results show that ToxiPrompt outperforms four state-of-the-art baselines in both adversarial prompt diversity and the level of elicited toxicity from target LLMs, improving 14.6% harmonic mean of toxicity and diversity against the best baseline. The approach also performs well for multiple instruction-tuned target LLMs (Llama-2/3, Qwen, Mistral) without re-tuning, achieving up to 55% harmonic mean improvement against the best baseline. Our code is available at https://anonymous.4open.science/r/ToxiPrompt-DE92

ToxiPrompt: A Two-Stage Red-Teaming Approach for Balancing Adversarial Prompt Diversity and Response Toxicity

Reasoning-augmented vision language models (VLMs) generate explicit chains of thought that promise greater capability and transparency but also introduce new failure modes: models may reach correct answers via visually unfaithful intermediate steps, or reason faithfully yet fail on the final prediction. Standard evaluations that only measure final-answer accuracy cannot distinguish these behaviors.We introduce the visual faithfulness of reasoning chains as a distinct evaluation dimension, focusing on whether the perception steps of a reasoning chain are grounded in the image. We propose a training- and reference-free framework that decomposes chains into perception versus reasoning steps and uses off-the-shelf VLM judges for step-level faithfulness, additionally verifying this approach through a human meta-evaluation. Building on this metric, we present a lightweight self-reflection procedure that detects and locally regenerates unfaithful perception steps without any training. Across multiple reasoning-trained VLMs and perception-heavy benchmarks, our method reduces Unfaithful Perception Rate while preserving final-answer accuracy, improving the reliability of multimodal reasoning.

Journey Before Destination: On the importance of Visual Faithfulness in Slow Thinking

Multi-modal large language models (MLLMs) have recently shown impressive capabilities but are also highly vulnerable to jailbreak attacks. While white-box methods can generate adversarial visual inputs via gradient-based optimization, such approaches fail in realistic black-box settings where model parameters are inaccessible. Zeroth-order (ZO) optimization offers a natural path for black-box attacks by estimating gradients from queries, yet its application to MLLMs is challenging due to sequence-conditioned objectives, limited feedback, and massive model scales. To address these issues, we propose Zer0-Jack, the first direct black-box jailbreak framework for MLLMs based on ZO optimization. Zer0-Jack focuses on generating malicious images and introduces a patch-wise block coordinate descent strategy that stabilizes gradient estimation and reduces query complexity, enabling efficient optimization on billion-scale models. Experiments show that Zer0-Jack achieves 98.2% success on MiniGPT-4 and 95% on the Harmful Behaviors Multi-modal dataset, while directly jailbreaking commercial models such as GPT-4o. These results demonstrate that ZO optimization can be effectively adapted to jailbreak large-scale multi-modal LLMs.Codes are provided in the supplement.

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