India

The detection of hallucinations in multilingual scientific text generated by Large Language Models (LLMs) presents significant challenges for reliable AI systems. This paper describes our submission to the SHROOM-CAP 2025 shared task on scientific hallucination detection across 9 languages. Unlike most approaches that focus primarily on model architecture, we adopted a data-centric strategy that addressed the critical issue of training data scarcity and imbalance. We unify and balance five existing datasets to create a comprehensive training corpus of 124,821 samples (50% correct, 50% hallucinated), representing a 172x increase over the original SHROOM training data. Our approach fine-tuned XLM-RoBERTa-Large with 560 million parameters on this enhanced dataset, achieves competitive performance across all languages, including \textbf{2nd place in Gujarati} (zero-shot language) with Factuality F1 of 0.5107, and rankings between 4th-6th place across the remaining 8 languages. Our results demonstrate that systematic data curation can significantly outperform architectural innovations alone, particularly for low-resource languages in zero-shot settings.

IJCNLP-AACL 2025

“AGI” team at SHROOM-CAP: Data-Centric Approach to Multilingual Hallucination Detection using XLM-RoBERTa

data-centric ai

hallucination detection

prompt engineering

synthetic data generation

xlm-roberta

multilingual nlp

cross-lingual transfer

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s 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.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

workshop paper

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
Read full message and download the Conference Handbook [**here**](https://drive.google.com/file/d/1UTwxkAqSqI-GAoJC3wE1zZt5VP1Y8GX0/view?usp=sharing).

The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

One of the key challenges of deploying Large Language Models (LLMs) in multilingual scenarios is maintaining output quality across two conditions: factual correctness and linguistic fluency. LLMs are liable to produce text with factual hallucinations, solid-sounding but false information, and fluency errors that take the form of grammatical mistakes, repetition, or unnatural speech patterns. In this paper, we address a two-framework solution for the end-to-end quality evaluation of LLM-generated text in low-resource languages. (1) For hallucination detection, we introduce a retrieval-augmented classification model that utilizes hybrid document retrieval, along with gradient boosting.(2) For fluency detection, we introduce a deep learning model that combines engineered statistical features with pre-trained semantic embeddings using an attention-based mechanism.

Scalar_NITK at SHROOM-CAP: Multilingual Factual Hallucination and Fluency Error Detection in Scientific Publications Using Retrieval-Guided Evidence and Attention-Based Feature Fusion

Large Language Models (LLMs) often generate hallucinations, a critical issue in domains like scientific communication where factual accuracy and fluency are essential. The SHROOM-CAP shared task addresses this challenge by evaluating Factual Mistakes and Fluency Mistakes across diverse languages, extending earlier SHROOM editions to the scientific domain. We present Smurfcat, our system for SHROOM-CAP, which integrates three complementary approaches: uncertainty estimation (white-box and black-box signals), encoder-based classifiers (Multilingual Modern BERT), and decoder-based judges (instruction-tuned LLMs with classification heads). Results show that decoder-based judges achieve the strongest overall performance, while uncertainty methods and encoders provide complementary strengths. Our findings highlight the value of combining uncertainty signals with encoder and decoder architectures for robust, multilingual detection of hallucinations and related errors in scientific publications.

SmurfCat at SHROOM-CAP: Factual but Awkward? Fluent but Wrong? Tackling Both in LLM Scientific QA

Emotions are a fundamental aspect of artistic expression. Due to their abstract nature, there is a broad spectrum of emotion realization in artworks. These are subject to historical change and their analysis requires expertise in art history. In this article, we investigate which aspects of emotional expression can be detected by current (2025) vision language models (VLMs). We present a case study of three VLMs (Llava-Llama and two Qwen models) in which we ask these models four sets of questions of increasing complexity about artworks (general content, emotional content, expression of emotions, and emotion symbols) and carry out a qualitative expert evaluation. We find that the VLMs recognize the content of the images surprisingly well and often also which emotions they depict and how they are expressed. The models perform best for concrete images but fail for highly abstract or highly symbolic images. Reliable recognition of symbols remains fundamentally difficult. Furthermore, the models continue to exhibit the well-known LLM weakness of providing inconsistent answers to related questions.

Artwork Interpretation with Vision Language Models: A Case Study on Emotions and Emotion Symbols

Memes are now a common means of communication on social media. Their humor and short format help messages spread quickly and easily. Propagandistic memes use both words and images to influence opinions and behaviors, often appealing to emotions or ideologies. While propaganda detection has been well-studied in high-resource languages (HRLs), there has been a limited focus on low-resource languages (LRLs), such as Bengali. In this study, we introduce MemeGuard, a new dataset of 3,745 memes for detecting propaganda in Bengali. We tested more than 45 different methods, including both single and combined approaches with fusion. For text, BanglaBERT-1 achieved the best macro F1 score of 80.34%, whereas the CLIP vision transformer scored 78.94% for images. The proposed multimodal model,
which combines BanglaBERT-2 and CLIP via Adaptive Modality Fusion, achieved the highest macro-F1 score of 85.36%. This work establishes a strong baseline and offers valuable insights for future research in Bengali multimodal content analysis.

MemeGuard: Transformer-Based Fusion for Multimodal Propaganda Detection in Low-Resource Social Media Memes

Large language models excel on broad multilingual benchmarks but remains to be evaluated extensively in figurative and culturally grounded reasoning, especially in low-resource context. We present BengaliFig, a compact yet richly annotated challenge set that targets this gap in Bengali, a widely spoken low-resourced language. The dataset contains 435 unique riddles drawn from Bengali oral and literary traditions. Each item is annotated along five orthogonal dimensions capturing reasoning type, trap type, cultural depth, answer category, and difficulty, and is automatically converted to multiple–choice format through a constraint–aware, AI–assisted pipeline. We evaluate eight frontier LLMs from major providers under zero–shot and few–shot chain–of–thought prompting revealing consistent weaknesses in metaphorical and culturally specific reasoning. BengaliFig thus contributes both a diagnostic probe for evaluating LLM robustness in low-resource cultural contexts and a step toward inclusive and heritage-aware NLP evaluation. Data and evaluation code is available at https://github. com/chaoSefat/Bengali-Fig

BengaliFig: A Low-Resource Challenge for Figurative and Culturally Grounded Reasoning in Bengali

Low-resource tribal languages remain severely underrepresented in modern machine translation, especially when training data is small, noisy, and linguistically diverse. We propose Divide and Translate, a modular framework that replaces unified multilingual fine-tuning with direction-specific expert models. A single frozen NLLB encoder provides a stable multilingual representation space, while independent decoders specialize in individual translation directions. To improve robustness, we apply bitext reversal augmentation, doubling supervision and enabling balanced bi-directional learning without generating synthetic data. This design reduces gradient conflict, mitigates hallucination, and improves generalization under domain shift. Despite minimal compute and limited data, our system achieves strong leaderboard performance with low validation-test divergence, showing that specialization and parameter isolation outperform scale for under-resourced languages.

Challenge Track: Divide and Translate: Parameter Isolation with Encoder Freezing for Low-Resource Indic NM

Low-resource and tribal language translation remains a major challenge for modern machine translation systems due to extreme data sparsity, rich morphology, and structural divergence from high-resource languages. In this work, we present JHARNA-MT, a hybrid machine translation framework developed for the MMLoSo 2025 Shared Task, which achieved 2nd place on the private leaderboard. Our approach integrates retrieval-augmented generation (RAG), statistical machine translation (SMT), and neural machine translation (NMT) fine-tuned with LoRA, followed by Minimum Bayes Risk (MBR) decoding for consensus-based hypothesis selection. The system effectively leverages exact and fuzzy retrieval to exploit domain redundancy, combines SMT’s lexical reliability with NMT’s fluency, and mitigates hallucinations through MBR reranking. Experimental results on multiple high- to low-resource language pairs demonstrate that the proposed hybrid architecture consistently outperforms individual SMT or NMT models, particularly under low-resource and morphologically rich conditions. Our findings highlight the effectiveness of hybrid and decision-theoretic approaches for robust machine translation in low-resource settings.

Challenge Track: JHARNA-MT: A Copy-Augmented Hybrid of LoRA-Tuned NLLB and Lexical SMT with Minimum Bayes Risk Decoding for Low-Resource Indic Languages

This paper investigates how the specificity of natural language prompts influences zero-shot classification performance in modern vision language models (VLMs) under severe data scarcity. Using a curated 285 image subset of MS COCO containing three everyday postures (sitting, standing, and walking/running), we evaluate OpenCLIP, MetaCLIP2, and SigLIP alongside unimodal and pose-based baselines. We introduce a three tier prompt design, minimal labels, action cues, and compact geometric descriptions and systematically vary only the linguistic detail. Our results reveal a counterintuitive trend where simpler prompts consistently outperform more detailed ones, a phenomenon we term prompt overfitting. Grad-CAM attribution further shows that prompt specificity shifts attention between contextual and pose-relevant regions, explaining the model dependent behaviour. The study provides a controlled analysis of prompt granularity in low resource image based posture recognition, highlights the need for careful prompt design when labels are scarce.

Language as a Label: Zero-Shot Multimodal Classification of Everyday Postures under Data Scarcity

Detecting hateful memes is a complex task due to the interplay of text and visuals, with subtle cultural cues often determining whether content is harmful. This challenge is amplified in Bangla, a low-resource language where existing resources provide only binary labels or single dimensions of hate. To bridge this gap, we introduce BanHateME, a comprehensive Bangla hateful meme dataset with hierarchical annotations across three levels: binary hate, hate categories, and targeted groups. The dataset comprises 3,819 culturally grounded memes, annotated with substantial inter-annotator agreement. We further propose a hierarchical loss function that balances predictions across levels, preventing bias toward binary detection at the expense of fine-grained classification. To assess performance, we pair pretrained language and vision models and systematically evaluate three multimodal fusion strategies: summation, concatenation, and co-attention, demonstrating the effectiveness of hierarchical learning and cross-modal alignment. Our work establishes BanHateME as a foundational resource for fine-grained multimodal hate detection in Bangla and contributes key insights for content moderation in low-resource settings.

BanHateME : Understanding Hate in Bangla Memes thorough Detection, Categorization, and Target Profiling

Topic modeling methods identify fundamental themes within textual documents, facilitating an understanding of the insights inside them. Traditional topic modeling approaches are based on the generative probabilistic process that assumes the document-topic and topic-word distribution. Hence, those approaches fail to capture semantic similarities among words inside the documents and are less scalable with the vast number of topics and documents. This paper presents a method for capturing topics from Bangla documents by clustering the word vectors induced from LLM models. Corpus statistics are integrated into the clustering & word reordering process within each cluster or topic to extract the top words. Additionally, we deploy dimensionality reduction techniques, such as PCA, prior to clustering. Finally, we perform a comparative study and identify the best-performing combination of clustering and word embedding methods. Our top-performing combination outperforms the traditional probabilistic topic model in capturing topics and top words per topic, and excels notably in terms of computational efficiency and time complexity.

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Scalar_NITK at SHROOM-CAP: Multilingual Factual Hallucination and Fluency Error Detection in Scientific Publications Using Retrieval-Guided Evidence and Attention-Based Feature Fusion

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