Morocco

Vision Language Action (VLA) models are widely used in Embodied AI, enabling robots to interpret and execute language instructions. However, their robustness to natural language variability in real-world scenarios has not been thoroughly investigated.
In this work, we present a novel systematic study of the robustness of state-of-the-art VLA models under linguistic perturbations. 
Specifically, we evaluate model performance under two types of instruction noise: (1) human-generated paraphrasing and (2) the addition of irrelevant context. We further categorize irrelevant contexts into two groups according to their length and their semantic and lexical proximity to robot commands. In this study, we observe consistent performance degradation as context size expands. We also demonstrate that the model can exhibit relative robustness to random context, with a performance drop within 10\%, while semantically and lexically similar context of the same length can trigger a quality decline of around 50\%. Human paraphrases of instructions lead to a drop of nearly 20\%. Our results highlights a critical gap in the safety and efficiency of modern VLA models for real-world deployment.

EACL 2026

Bring the Apple, Not the Sofa: Impact of Irrelevant Context in Embodied AI Commands on VLA Models

embodied

robotics

robustness

## 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. 

Large language models (LLMs) are highly sensitive to subtle changes in prompt phrasing, posing challenges for reliable auditing. Prior methods often apply unconstrained prompt paraphrasing, which risk missing linguistic and demographic factors that shape authentic user interactions. We introduce AUGMENT (Automated User-Grounded Modeling and Evaluation of Natural Language Transformations), a framework for generating controlled paraphrases, grounded in user behaviors. AUGMENT leverages linguistically informed rules and enforces quality through checks on instruction adherence, semantic similarity, and realism, ensuring paraphrases are both reliable and meaningful for auditing. Through case studies on the BBQ and MMLU datasets, we show that controlled paraphrases uncover systematic weaknesses that remain obscured under unconstrained variation. These results highlight the value of the AUGMENT framework for reliable auditing.

Say It Another Way: Auditing LLMs with a User-Grounded Automated Paraphrasing Framework

Judeo-Arabic refers to Arabic variants historically spoken by Jewish communities across the Arab world, primarily during the Middle Ages. Unlike standard Arabic, it is written in Hebrew script by Jewish writers and for Jewish audiences. Transliterating Judeo-Arabic into Arabic script is challenging due to ambiguous letter mappings, inconsistent orthographic conventions, and frequent code-switching into Hebrew and Aramaic. In this paper, we introduce a two-step approach to automatically transliterate Judeo-Arabic into Arabic script: simple character-level mapping followed by post-correction to address grammatical and orthographic errors. We also present the first benchmark evaluation of LLMs on this task. Finally, we show that transliteration enables Arabic NLP tools to perform morphosyntactic tagging and machine translation, which would have not been feasible on the original texts.

A Tale of Two Scripts: Transliteration and Post-Correction for Judeo-Arabic

This paper presents an evaluation framework for probing large language models' linguistic knowledge of Indigenous languages of the Americas using zero- and few-shot prompting. The framework consists of three tasks: (1) language identification, (2) cloze completion of Spanish sentences supported by Indigenous-language translations, and (3) grammatical feature classification. We evaluate models from five major families (GPT, Gemini, DeepSeek, Qwen, and LLaMA) on 13 Indigenous languages, including Bribri, Guarani, and Nahuatl. The results show substantial variation across both languages and model families. While a small number of model-language combinations demonstrate consistently stronger performance across tasks, many others perform near chance, highlighting persistent gaps in current models' abilities on Indigenous languages.

Measuring Linguistic Competence of LLMs on Indigenous Languages of the Americas

Test-time compute methods can significantly improve the reasoning capabilities and problem-solving accuracy of large language models (LLMs). However, these approaches require substantially more computational resources, with most compute wasted on exploring low-diversity branches where the model already exhibits high confidence. We observe that a small subset of uncertain reasoning steps has a disproportionately large impact on final prediction accuracy, and branching at these critical junctures tends to yield more diverse and higher-quality candidate reasoning steps. We propose Entropy-Gated Branching (EGB), which branches only at high-uncertainty steps and prunes expansions with a lightweight verifier. On mathematical and financial reasoning benchmarks, EGB improves accuracy by 22.6% over standard inference while operating 31%-75% faster across math benchmarks than test-time beam search with higher performance. Our results show that dynamic resource allocation during inference can substantially improve both efficiency and effectiveness, offering a more scalable pathway to enhanced LLM reasoning capabilities.

Entropy-Gated Branching for Efficient Test-Time Reasoning

Large language models are being rapidly applied across many fields such as healthcare, finance, transportation, and energy, among many others. These applications often involve analyzing time-series data alongside contextual information in the form of natural language to support informed decisions. However, current time-series models are limited in their ability to perform reasoning that involves both time-series and their textual content. In this work, we address this gap by introducing Chat-TS, a large language model (LLM) based framework designed to support reasoning over time series and textual data. Unlike traditional models, Chat-TS integrates time-series tokens into LLMs' vocabulary, enhancing its reasoning ability over both modalities without compromising core natural language capabilities. To support learning and evaluation, we contribute new datasets: the TS Instruct Training Dataset (pairing diverse time-series data with relevant text instructions and responses for instruction tuning), the TS Instruct Question and Answer (QA) Gold Dataset (multiple-choice questions to evaluate multimodal reasoning), and a TS Instruct Quantitative Probing Set (a small subset of TS Instruct QA reasoning tasks alongside math and decision-making questions for LLM evaluation). We design a training strategy to preserve the inherent reasoning capabilities of LLMs while augmenting them for time-series reasoning. Experiments show that Chat-TS achieves state-of-the-art performance in multimodal reasoning tasks by maintaining strong natural language proficiency while improving time-series reasoning.

Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and Natural Language Data

Research on bot detection in social media exhibits imbalance in several areas — across platforms, languages, and detection levels. Addressing these gaps, this study focuses on comment-level bot detection within Polish Reddit communities. We describe in detail the construction of a comprehensive dataset (~40,000 comments, 58% bot-comment prevalence), which provides labels for the subsequent model training. Polish Reddit is inherently multilingual, we therefore take advantage of the linguistic signals, treating language composition of a comment as a feature on its own. We develop novel platform-specific, language-specific, and culturally informed features, and train comment-level classifiers from multiple model families on the manually annotated dataset. The resulting models achieve strong performance and temporal generalization to 2025 data. We analyze the importance and direction of these novel features across models and report that our 'cross-level' interaction features, 'Bottiquette' compliance signals, formatting markers, language indicators, repetition and randomness measures — especially the entropy of non-alphabetic characters — rank among the most decisive features. Finally, we complement our quantitative findings with a qualitative characterization of the Polish Reddit bot ecosystem. Overall, this study provides an important baseline for an underexplored setting and contributes to an open discussion on how to approach detection where data is linguistically mixed.

Pioneering Bot Detection on Polish Reddit at the Comment Level

Modern large language model (LLM) systems frequently route inputs to specialized experts to improve accuracy, efficiency, and robustness. Routers determine which expert to activate based on the input, typically represented as a single vector. The construction of this vector limits the distinctions the router can make. Prior work rarely isolates how this vector representation affects routing behavior. We isolate the role of the representation by holding the routing pipeline fixed and vary only how this representation is formed in multilingual settings. We find that representation choice systematically reshapes the available routing partitions. In multilingual routing settings, the routers single-vector input often only encodes shallow features (language/format), resulting in domains that are organized by these features rather than by topic. To mitigate this, we introduce Funnel pooling, a lightweight trainable in-model readout that constructs the routing vector directly from token-level hidden states and does not require a separate embedding encoder. Funnel pooling reduces language and source-dataset driven clustering and results in more topic-aligned domains. Despite this shift, downstream routing performance remains competitive with introducing only a minor inference overhead.

What the Router Sees Matters: Funnel Pooling for Fast, Content Driven Expert Routing

Quantitative text analysis relies on high-quality corpora, but keyword-based collection often retrieves irrelevant material, undermining validity. We show that active learning with a transformer-based classifier can iteratively refine corpora by excluding irrelevant documents, prompting researchers to clarify inclusion criteria and address edge cases. Applied to German newspaper articles on depression and schizophrenia, this approach improves construct validity and reduces labeling effort. The document relevance classifiers reached an F1-score of 0.8 with just 100–150 labeled snippets, with further gains from tuning, outperforming both random sampling and a weakly supervised sampling baseline. Filtering non-medical articles further had little effect on downstream depression stigmatization measures but increased schizophrenia stigmatization. Active learning thus enables efficient corpus validation and clearer concept boundaries with minimal preprocessing. The source code is publicly available at https://github.com/jakobstgl/active-learning-corpus-refinement.

Active Learning for Corpus Refinement: Cost-Effective Preprocessing to Improve Validity of Applied Quantitative Text Analysis

Developing a machine translation (MT) system requires a considerable amount of high-quality parallel data, which is often limited for low-resource languages. This paper explores the use of synthetic data for training an LLM-based MT system in the English-to-Basque direction. Using Basque monolingual corpora as a starting point, we apply back-translation to generate parallel corpora, taking advantage of the fact that current LLMs do not translate well from English to Basque, but they yield an acceptable performance in the reverse direction. We conduct experiments in a multi-stage approach, from a simple Supervised Fine-tuning (SFT) step, to preference learning with the Direct Preference Optimization (DPO) technique. We then evaluate the approach with both automatic metrics and manual assessment. Experimental results suggest that for this task, SFT brings a clear improvement in translation quality, while DPO only yields marginal enhancement.

Machine Translation for Low-Resource Languages through Monolingual Data and LLM: A Case Study of English-to-Basque

Existing linguistic knowledge bases such as URIEL+ provide valuable geographic, genetic and typological distances for cross-lingual transfer but suffer from two key limitations. First, their one-size-fits-all vector representations are ill-suited to the diverse structures of linguistic data. Second, they lack a principled method for aggregating these signals into a single, comprehensive score. In this paper, we address these gaps by introducing a framework for type-matched language distances. We propose novel, structure-aware representations for each distance type: speaker-weighted distributions for geography, hyperbolic embeddings for genealogy, and a latent variables model for typology. We unify these signals into a robust, task-agnostic composite distance. Across multiple zero-shot transfer benchmarks, we demonstrate that our representations significantly improve transfer performance when the distance type is relevant to the task, while our composite distance yields gains in most tasks.

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