United States

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.

ACL 2026

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

indic vqa benchmark

multilingual vqa

multimodal vqa

telugu

hindi

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.

workshop paper

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.

RABIT: Rationale-Based Distillation Towards Interpretable Automatic Speaking Assessment via a Small Language Model

This paper describes linguistic and technological challenges encountered within an applied project aimed at expanding a large e-learning portal from its original Czech to three other languages: Ukrainian, English and German. Although there seems to be a general belief that machine translation is a solved task in 2026, we show that translating educational content, which in our case is highly terminological, multimodal, interactive and encoded in XML, brings along many challenges of different types, some easily solvable and some not. We also compare our results from the early phase of the project (Transformer-based machine translation) with those after the switch to the LLM-based translation methods. We show that both MT methods are prone to different types of errors, some of which are quite new (such as the undesired correction of counterfactual statements) and require new ways of handling them. The resulting four-language mutation of the educational web portal will be freely available to educators, students and researchers by the end of 2026.

Challenges in Machine Translation of Interactive Multimodal Exercises

Generative AI is increasingly used to accelerate assessment content development, yet its effectiveness for generating content used in complex assessment tasks for knowledge-rich domains such as medical education is unclear. This study evaluates automated LLM-supported workflows for generating patient-centered communication assessment items that allow students to practice their communication skills. We compared two content generation approaches—constrained linear and exploratory branching—each implemented with and without anchoring in vetted multiple-choice questions (MCQs). Ten subject-matter experts (SMEs) evaluated 80 communication items across six quality dimensions using structured rubrics. The constrained linear approach yielded better ratings than exploratory branching approaches, particularly for medical accuracy and alignment with learning objectives and patient-centered behaviors. MCQ anchoring did not improve medical accuracy. Only a minority of items met all criteria without requiring revision, and no items were unanimously approved by all SMEs. These findings underscore the importance of workflow design in LLM-supported assessment content generation, the continued need for human oversight, and the current limitations of automated content generation in medical education.

Evaluating LLM Workflows for Generating Clinical Communication Assessment Items: A Comparative Study with Subject-Matter Experts

Automated essay scoring (AES) research often relies on rank-based correlation metrics to validate analytic assessment. However, such metrics obscure both intrinsic intercorrelations among analytic dimensions that arise from the structure of writing proficiency itself and halo effects, whereby holistic impressions bleed into fine-grained component scores. As a result, high correlations may mask a system's true diagnostic behaviour. In this study, we propose a novel self-referential assessment evaluation framework that focuses on identifying intra-learner strengths and weaknesses rather than assessing inter-learner rankings. We conduct experiments on the publicly available ICNALE GRA, a uniquely dense second-language writing dataset annotated holistically and analytically by up to 80 trained raters. To obtain reliable reference scores, we apply two-facet Rasch modelling to calibrate rater severity and derive fair average scores across ten analytic aspects and holistic proficiency. We compare the analytic scoring performance of human operational raters and three large language models (LLMs) in a zero-shot setting. Our results show that LLMs tend to outperform single human raters in identifying relative weaknesses (negative feedback) across several proficiency aspects, while human raters remain stronger at identifying relative strengths (positive feedback). Overall, our findings highlight the limitations of rank-based evaluation for analytic assessment and demonstrate the value of intra-learner, profile-based methods for assessing and deploying LLMs in AES.

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