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Prompt recovery in large language models (LLMs) is the task of inferring the communicative intent and stylistic framing of the original instruction from model-generated output. This task is especially challenging for low-resource Dravidian languages such as Telugu, where agglutinative morphology, register variation, and scarce annotated data complicate stylistic modelling. In this paper, we present our system for the Shared Task on Prompt Recovery for LLM in Telugu at DravidianLangTech @ ACL 2026, which aims to classifiy Telugu transcript excerpts into nine communicative style categories: Formal, Informal, Optimistic, Pessimistic, Humorous, Serious, Inspiring, Authoritative, and Persuasive. We have implemented a transformer-based approach using ai4bharat/IndicBERTv2-MLM-only, MuRIL-base and Telugu-BERT for Telugu communicative style classification. Our system fine-tunes the pretrained Indic language training samples to capture stylistic patterns in Telugu transcripts. The training setup incorporates label smoothing, the AdamW optimizer with linear warmup and decay scheduling, and early stopping based on macro-averaged F1 to ensure stable convergence. Our approach achieved a macro F1 score of 0.2993 on the evaluation set, demonstrating the effectiveness of Indic-focused pretrained models for stylistic analysis in low-resource language settings.

ACL 2026

Cuet Yet Another Baseline@DravidianLangTech 2026: Shared Task on Prompt Recovery for LLM in Telugu

telugu style classification

dravidianlangtech

indicbertv2

prompt recovery

multilingual transformers

sequence classification

low-resource nlp

large language models

label smoothing

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>
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workshop paper

Detecting abusive language in Tamil social media is a genuinely difficult problem. The language is morphologically rich, speakers routinely mix Tamil with English, and informal romanised Tamil is common enough to confuse models trained primarily on formal text. This work presents a system for binary classification of Tamil comments into Abusive and Non-Abusive categories, submitted to the DravidianLangTech@ACL 2026 shared task. MuRIL, a BERT-based encoder pre-trained on 17 Indian languages and their transliterated equivalents, is fine-tuned, and it is shown that this Indian-language-specific pre-training provides a meaningful advantage over generic multilingual baselines. The system achieves a macro-averaged F1 of 0.83 on the validation set, compared to 0.79 for XLM-RoBERTa and 0.77 for mBERT under identical training conditions, establishing a strong transformer-based baseline for abusive language detection in code-mixed Tamil.

PrimeLine@DravidianLangTech 2026: Abusive Tamil Comment Detection Using MuRIL

Hope speech detection in low-resource, code-mixed languages presents a genuine challenge for natural language processing. Tulu, a Dravidian language spoken along the coastal regions of Karnataka and Kerala, is one such language where social media content is deeply code-mixed, blending Tulu, Kannada script, and English within a single comment. Two classification tasks are addressed: a four-class coarse-grained setting (Track 1) and a five-class fine-grained setting (Track 2). XLM-RoBERTa, a cross-lingual transformer pre-trained on more than 100 languages, is fine-tuned on the task-provided datasets using Google Colab with an NVIDIA T4 GPU. The system achieves a Macro F1-score of 0.34 on Track 1 and 0.19 on Track 2 on the official Codabench evaluation, establishing the first transformer-based baseline for hope speech classification in Tulu.

PrimeLine@DravidianLangTech 2026: Hope Speech Detection in Tulu Using XLM-RoBERTa for Coarse and Fine-Grained Classification

An increased amount of violence against women has resulted from social media. Automatic detection of Tamil text that is abusive toward women is now an important area for research in natural language processing. This paper describes an experimental comparison between several classification algorithms, including support vector machine (SVM), decision tree, and random forest, to classify Tamil-language text as abusive or non-abusive. The data set used in evaluating the models contains Tamil-language social media text that is gender-targeted and abusive. The results show clearly that transformer models outperform traditional machine-learning models. In particular, RoBERTa had an accuracy rate of 82.35% for abusive language detection, suggesting that transformer models effectively capture contextual and semantic features of Tamil and can thus be applied to abuse detection across all low-resource Dravidian languages.

VISION@DravidianLangTech 2026: Abusive Tamil Text Detection Targeting Women on Social Media

Hope speech detection is an important task in understanding emotionally constructive communication in online platforms, especially in low-resource and code-mixed languages. This paper describes our system submitted to the first shared task on Hope Speech Detection in Code-Mixed Tulu, organized by DravidianLangTech@ACL 2026. The shared task consists of two tasks: Task 1 - Coarse-Grained Hope Tone Classification and Task 2 - Fine-Grained Hope Type Classification, with the objective of detecting and classifying the tone and type of hope expressed in code-mixed Tulu texts. We experimented with Logistic Regression (LR) and Linear Support Vector Classifier (LinearSVC) - classical Machine Learning (ML) approaches, trained with Term Frequency and Inverse Document Frequency (TF-IDF) of word ngrams (n = 1, 2). For Task 1, we employed both models, whereas for Task 2, we employed only the LR model. Linear SVC obtained a macro F1-score of 0.51 in Task 1 and secured 4th rank, while the LR model obtained a macro F1-score of 0.37 in Task 2 and secured 5th rank. The results demonstrate that traditional ML approaches remain effective for low-resource code-mixed language scenarios.

SJM_MINDS@DravidianLangTech@ACL2026: Machine Learning Approaches for Hope Speech Detection in Code-Mixed Tulu

This paper describes the system that our Still-Loading team designed to run the Telugu Prompt-Style Recovery shared task at DravidianLangTech@ACL 2026. The purpose of the given task is categorizing Telugu transcript passages as belonging to one of 9 communicative styles: Formal, Informal, Optimistic, Pessimistic, Humorous, Serious, Inspiring, Authoritative, and Persuasive. We compared several multilingual Transformer-based models, i.e. MuRIL, XLM-RoBERTa-Large, mBERT, and IndicBERTv2. We chose a "Turbo Sandwich" preprocessing strategy which helps to give more emphasis to lexical deltas, in addition to Focal Loss. Our system based on the MuRIL was rated at the 7th place in the official leaderboard with a Macro-F1 rating of 0.1703. The source code to reproduce our experiments is publicly available on Still-Loading-Prompt-Recovery-for-LLM-in-Telugu (https://github.com/Priyontee1713/Still-Loading-Prompt-Recovery-for-LLM-in-Telugu).

Still-Loading@DravidianLangTech 2026: Telugu Prompt-Style Recovery using Multilingual Transformers

Multilingual transformers have achieved re- markable performance on code-mixed senti- ment benchmarks, but their robustness under linguistic stress and domain shift remains un- derexplored. We fine-tune XLM-RoBERTa and mBERT on a carefully cleaned 25,543- tweet Hinglish sentiment dataset, where XLM- R achieves near-perfect in-distribution accu- racy (99.7%). The integrity of this result is confirmed by rigorous hash-based and 3-gram Jaccard deduplication, ruling out data leakage. However, when evaluated on a 400-example human-validated adversarial benchmark span- ning negation, sarcasm, contrast, subtle senti- ment, and true neutral, XLM-R performance collapses to 42.5% -- a drop of over 57 per- centage points. Zero-shot transfer to English TweetEval yields only 50.8% accuracy (40.8% macro F1), above . Our results highlight a crit- ical gap between benchmark scores and real- world reliability, underscoring the need for ad- versarial evaluation and cross-domain stress- testing before deploying sentiment models in practical, safety-sensitive applications.

99.7% Accuracy Is Not Enough: Adversarial Vulnerability of Hinglish Sentiment Models

This paper introduces Thiruppugazh-KG, a semantically annotated dataset and knowledge graph derived from the Thiruppugazh corpus, a 14th-century collection of 1,335 Tamil devotional hymns composed by Arunagirinathar. The dataset includes annotations for entities, devotional themes, mythological events, philosophical concepts, imagery, and sacred locations mentioned in each hymn. Using these annotations, we construct a Neo4j-based knowledge graph that models relationships between hymns and their associated cultural and narrative elements. Graph analytics, including PageRank, are applied to identify prominent entities and sacred locations within the corpus. The resulting resource provides a structured representation of Tamil devotional literature and supports computational analysis of cultural texts in low-resource languages.

Thiruppugazh-KG Dataset: A Manually Annotated Resource for Computational Analysis of Tamil Devotional Literature

Our paper explores several machine learning methods for detecting toxic language in gaming-related chat utterances. We start with the GameTox dataset, perform some data preprocessing and augment the minority classes with LLM-generated synthetic data. We then set a baseline using a classic Logistic Regression model and continue to explore several approaches to surpassing it, by leveraging the leading multilingual transformer models (XLM-RoBERTa and DeBERTa-V3) to classify our test data. We achieve a top result of 0.6725 Macro-F1 (2nd place on shared task leaderboard) using a MDeBERTa-V3 model which we pretrained on the Jigsaw dataset for 1 epoch and then fine-tuned on our GameTox data for 5 epochs.

FNLP412@EEUCA 2026: Understanding Toxic Behavioral Intent in Gaming Chat Logs using Transfer Learning and Synthetic Data Augmentation

Vaccine stance detection in multimodal memes has emerged as an important yet challenging task, requiring models to interpret both textual and visual cues that jointly convey opinions. The difficulty lies in capturing subtle semantic interactions and handling class imbalance across stance categories. In this paper, we present our system developed for the VaxMeme 2026 Shared Task at EEUCA 2026. Our approach leverages a soft-voting ensemble of complementary models, combining DeBERTa-v3-large and RoBERTa-large for rich textual representation with CLIP (ViT-B/32) for joint vision-language understanding. We incorporate domain-specific preprocessing, techniques such as random token deletion, image enhancement, and balanced class oversampling to address dataset limitations. Through extensive ablation studies, we identify balanced class oversampling as the most effective component, significantly improving performance across models. Our final system achieves a macro F1-score of 0.8306, securing 8th place among 25 teams, demonstrating the effectiveness of ensemble-based multimodal learning for stance detection.

Quasar@EEUCA 2026: Multimodal Deep Learning for Vaccine Stance Detection in Memes

Vaccine-critical memes have emerged as a growing challenge for public health communication, combining images and text to spread misinformation in ways that are difficult to detect automatically. In this paper, we have described our system for the EEUCA 2026 Shared Task on Multimodal Vaccine-Critical Meme Detection, classifying memes from the VaxMeme dataset into Vaccine-Critical, Neutral and Pro-Vaccine categories. We have experimented with multiple text encoders and visual backbones, finding that Twitter-RoBERTa fused with CLIP ViT-L/14 through gated cross-modal attention has achieved a test macro F1 of 0.8357. We have further shown that domain-specific pretraining has outperformed larger general-purpose models, highlighting the importance of domain adaptation over raw model scale. Finally, our system has secured the 3rd position on the shared task leaderboard.

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