United States

Multimodal Large Language Models (MLLMs) have achieved remarkable success in semantic visual reasoning, yet their capacity for fine-grained, low-level perception remains critically under-evaluated. This perceptual fragility limits their reliability in noisy, real-world environments where visual signals are degraded. Furthermore, existing benchmarks often entangle visual perception with language priors, masking these underlying deficits. To address this, we introduce the \textbf{FAint numeric Detection Evaluation (FADE)} dataset, a novel evaluation suite designed to probe the limits of zero-shot Optical Character Recognition (OCR) in frontier MLLMs. By embedding synthetic, strictly numerical sequences over cluttered natural backgrounds at varying levels of transparency ($\alpha$), FADE explicitly disentangles pure visual perception from semantic predictability. We evaluate state-of-the-art models including Gemini 3.0, Claude 4.5 Sonnet, and Gemma 3 against a specialized UNet segmentation baseline. Our results reveal a striking limitation in frontier architectures: while they achieve near-perfect transcription at high visibility, their performance collapses under high transparency. Conversely, the UNet pipeline maintains robust spatial grounding, significantly outperforming generalist models at the lowest visibility thresholds. FADE provides a reproducible dataset to expose and diagnose the perceptual breakage points of modern multimodal systems.

ACL 2026

FADE: Probing the Limits of VLMs on fine-grained OCR

visualqa

vlms

mllm

#### *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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EACL 2026

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. 

workshop paper

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

We propose Question-Guided Sparse Attention (QGSA), a lightweight plug-and-play module that reduces visual redundancy in VQA by dynamically selecting only question-relevant image patches before cross-attention. Using a Gumbel-Softmax differentiable selector and a self-supervised grounding loss requiring no bounding-box annotations, QGSA reduces cross-attention FLOPs by 91–99% across standard resolutions while preserving exact accuracy on VQA-RAD, A-OKVQA, and RefCOCO. On SmolVLM-256M, it selects ~17 of 576 patches (34× token compression) and achieves a 31× relative improvement in grounding IoU over random selection. Wall-clock parity with the dense baseline is reached at 336px, with 76.2× theoretical speedup at 576px. These results validate the feasibility of question-conditioned sparse attention as a foundation for efficient inference in larger vision-language models.

Efficient Visual Grounding in VQA via Question-Guided Sparse Attention

Mapping linguistic to visual representations is a central objective in vision-language model (VLM) development. Consequently, current VLM benchmarks and training objectives predominantly optimize for literal, descriptive correspondence between modalities. However, in real-world multimedia contexts, text and images are rarely strictly redundant; they are often loosely coupled, interacting at higher narrative levels. To capture this dynamic, we introduce Visual Narrative Freedom (VNF): the degree to which a generative system can produce multiple plausible visual realizations from an underdetermined textual input. In this paper, we systematically evaluate text-to-image (T2I) generation across a continuum of linguistic constraints, conditioning models on visual descriptions, captions, and full news articles. Using perceptual, structural, and semantic metrics, we first demonstrate that as textual constraints loosen (and VNF increases), T2I models produce images that are significantly more diverse from ground-truth reference photographs. Human evaluation further reveals that providing generation models with greater visual narrative freedom significantly increases the likelihood that their outputs are preferred over authentic news photographs, despite the known stylistic limitations of AI imagery. Ultimately, these findings suggest that the creation of persuasive multimodal misinformation is more imminent than evaluations of T2I systems based solely on text descriptions may indicate. This highlights the need for VLM evaluation frameworks that better capture the underdetermined nature of real-world image-text alignment.

Characterizing Visual Narrative Freedom under Loose Image–Text Alignment

With 1.5 billion people speaking over 120 major languages, India exemplifies the challenges of multilingual AI evaluation. Current multilingual VLM benchmarks suffer from unverified auto-translations, narrow task coverage, small sample sizes, and lack of culturally grounded content. We present HinTel-AlignBench, a comprehensive evaluation framework and benchmark for Hindi and Telugu vision-language models with English-aligned samples. Our framework combines semi-automated translation with human verification to generate 4k QA pairs per language across five domains: adapted English datasets (VQAv2, RealWorldQA, CLEVR-Math) and native Indic sets (JEE for STEM, VAANI for cultural grounding). Evaluation of state-of-the-art open and closed-source VLMs reveals consistent performance regression from English to Indic languages, with average drops of 8.3 points for Hindi and 5.5 points for Telugu across four of five tasks. We identify key failure modes and establish reproducible baselines for multilingual multimodal evaluation.

Systematic Performance Degradation in Indic Vision-Language Models: Evidence from Hindi and Telugu

Text-to-image diffusion models learn a mapping from natural language to visual structure, but how robust is this mapping to perturbation? We use personalization—fine-tuning a model to learn a new face, object, or style—as a controlled stress test to probe the fragility of learned vision-language alignment. We find that fine-tuning for one concept systematically shifts the model’s ability to faithfully render unrelated concepts, and that this disruption follows structured, predictable patterns. To measure this fragility, we construct Concept Entanglement Maps: per-prompt, per-model disruption matrices that reveal which concepts are most affected and why. Using Stable Diffusion v1.5 as a controlled testbed, we evaluate 15 subjects across three personalization methods on 200 prompts and report three findings about the organization of vision-language alignment: (1) aggregate disruption is larger for vision-backbone and cross-attention perturbations than for text-embedding perturbations, despite the latter directly modifying the language representation; (2) abstract and compositional language is significantly more fragile than concrete, object-specific language; and (3) disruption does not follow semantic proximity—personalizing for a face does not preferentially disrupt other face-related prompts (
), suggesting that alignment vulnerability is organized globally rather than purely by semantic category. These findings expose a structural vulnerability in current text-to-image personalization: the same cross-attention mechanism that enables compositional generalization also creates pathways through which local fine-tuning can propagate as global alignment shift.

How Fragile Is Vision-Language Alignment? Mapping Concept Disruption Under Text-to-Image Personalization

Automatic pronunciation assessment (APA) provides L2 learners with scalable and timely feedback on pronunciation proficiency in a target language, typically through goodness of pronunciation (GOP) features. GOP quantifies how well a pronounced phoneme matches the expected target sound by comparing acoustic features against the model's posterior probabilities. Traditional GOP relies on forced alignment to obtain these posteriors, but it suffers from acoustic-induced misalignments that degrade assessment reliability. Although the standard CTC-GOP approach bypasses forced alignment, it is limited by the inherent peaky behavior of CTC-based ASR models, which produces sparse posteriors and lacks stable temporal information. To address these issues in standard CTC, we propose a context-aware CTC framework incorporating output context dependency (OCD) in the CTC topology, along with label prior (LP) and maximum conditional entropy (EnCTC) regularization, to mitigate peakiness and produce more stable ASR logits suitable for GOP computation. Experiments on the speechocean762 corpus demonstrate that our best context-aware configurations achieve superior phoneme-level performance, outperforming the TDNN-F baseline and standard CTC in unified GOPT (phoneme PCC 0.641 vs. 0.612; word total PCC 0.582 vs. 0.549) while narrowing the gap in hierarchical HierCB scoring. These improvements widen the scoring margin between correct and mispronounced phonemes from 0.708 to 0.816 in GOPT. They also reveal that mitigating CTC peakiness and incorporating context dependency significantly enhance CTC-GOP stability and robustness, especially for alignment-free APA models.

Investigating Context-aware CTC for Pronunciation Assessment: Mitigating Peaky Behavior and Context Independency Assumption

Automated coaching for oral presentations sits at the intersection of computer-assisted pronunciation training (CAPT), prosody modeling, and speech synthesis, yet no prior work has systematically surveyed and compared existing systems along these dimensions. This survey reviews and categorizes automated presentation coaching systems, spanning pronunciation tutors, fluency and prosody coaches, multimodal trainers, and conference Q&A practice tools. We introduce a five-dimensional task taxonomy - covering segmental pronunciation, lexical stress, suprasegmental prosody, pacing, and content faithfulness - and explicitly map surveyed systems onto it to reveal coverage gaps. We further review the core technical methods these systems employ: TTS-based exemplar generation and diagnostic methods for pronunciation, prosody, and fluency assessment. Key open challenges include the scarcity of annotated presentation corpora, achieving accent-fair feedback across diverse L1 backgrounds, and delivering low-latency diagnostics for real-time rehearsal.

A Survey of Automated Presentation Coaching: Systems, Methods, and Open Challenges

Effective mathematics education requires identifying and responding to students' mistakes. For AI to support pedagogical applications, models must perform well across different levels of student proficiency. Our work provides an extensive, year-long snapshot of how 11 vision-language models (VLMs) perform on DrawEduMath, a QA benchmark involving real students' handwritten, hand-drawn responses to math problems. We find that models' weaknesses concentrate on a core component of math education: student error. All evaluated VLMs underperform when describing work from students who require more pedagogical help, and across all QA, they struggle the most on questions related to assessing student error. Thus, while VLMs may be optimized to be math problem solving experts, our results suggest that they require alternative development incentives to adequately support educational use cases.

The Aftermath of DrawEduMath: Vision Language Models Underperform with Struggling Students and Misdiagnose Errors

This paper presents an empirical evaluation of the so-far
under-researched German Grammar Profile, a CEFR-aligned resource of
criterial features, and its corresponding extraction system PALME. We
address this gap by designing a systematic test suite in which each
feature extractor is evaluated on controlled positive and negative
examples. The results show that PALME achieves high precision and
recall across all CEFR levels, with over 90% of features achieving
scores above 0.8. Qualitative analysis shows that lower performance
primarily results from morphological ambiguity in noun and adjective
case marking.
Turning to an evaluation of the usefulness of criterial features for
CEFR-aligned readability assessment, we assess their predictive power
using Explainable Boosting Machines on graded readers. The model
achieves strong performance (precision: 0.75, recall: 0.73). The
qualitative analysis shows that features related to split verb
constructions follow patterns consistent with developmental stages
predicted by Processability Theory. These findings underline the value
and relevance of criterial features for modeling language development
in readability assessment.

Criterial Features in German: Towards Interpretable NLP in Readability Assessment

Educational interventions are effective tools for enhancing student learning. While Large Language Models (LLMs) allow for generating adaptive feedback at scale, current studies lack clear methodologies for providing Just-in-Time (JiT) feedback in authentic instructional settings. In this paper, we present a framework that provides adaptive feedback by grounding LLMs with domain-specific expert knowledge. Our approach collects written reasoning logic (strategy essays) from students, analyzes potential error types based on the content of that reasoning, and delivers non-intrusive feedback designed to clarify missing or incorrect concepts. We deploy this framework in a large-scale college course (N > 1,000), where it improved student performance by over 80% compared to previous semesters. Lastly, we validate the framework's pedagogical utility by analyzing the learning trajectories; we demonstrate how iterative conversations with LLM facilitate shifting one's misconception to correct understanding.

Towards Just-in-Time Adaptive Feedback: Enhancing Student Learning via Knowledge-Grounded LLM

Automatic speaking assessment (ASA) manages to quantify the language competence of foreign language learners by providing a proficiency score based on their spoken response. Existing efforts in ASA typically employ a neural grader integrated with a set of handcrafted features to assess learners' oral proficiency from multiple facets. Despite decent performance, the black-box nature of these neural graders remains a significant barrier to providing interpretable explanations for the grading results. In light of this, we propose RABIT for ASA, a novel \underline{Ra}tionale-\underline{b}ased knowledge distillation framework for \underline{i}nterpre\underline{t}able grading decisions via a small language model. Specifically, RABIT first extracts multi-faceted grading rationales from a large language model (LLM) pertaining to the learner's response and the scoring guidelines. Subsequently, a compact yet efficient language model, equipped with distinct output heads, is jointly optimized to estimate a proficiency score while generating a sequence of grading rationales in an autoregressive manner. A series of experiments conducted on General English Proficiency Test (GEPT) dataset validates the feasibility and superiority of our method over several cutting-edge baselines.

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