India

Contemporary vision-language models (VLMs) perform well on existing multimodal reasoning benchmarks (78-85\% accuracy on MMMU, MathVista). Yet, these results fail to sufficiently distinguish true scientific reasoning articulation capabilities from pattern-matching. To address this gap, we introduce \textbf{mmJEE-Eval}, a multimodal bilingual (English and Hindi) benchmark comprising 1,460 questions from India&#39;s JEE Advanced examination (2019-2025) spanning pre-college Physics, Chemistry, and Mathematics domains. Our evaluation of 17 state-of-the-art models reveals that while frontier VLMs (GPT-5, Gemini 2.5 Pro/Flash) achieve 77-84\% accuracy on held-out 2025 questions, open-source models plateau at 37-45\% despite scaling to 400B parameters, a significant difference not observed on existing benchmarks. While closed frontiers from Google and OpenAI show high problem-solving accuracies (up to 100\% pass@3 scores), they fully collapse when the reasoning load is increased meta-cognitively (GPT-5 fixes just 5.2\% errors). Systematic ablations show mmJEE-Eval&#39;s difficulty stems from complexity and reasoning depth rather than memorization. Effectively, our benchmark segregates superior training and reasoning methodologies where alternatives fail. We publicly release our code and data: https://mmjee-eval.github.io

IJCNLP-AACL 2025

mmJEE-Eval: A Bilingual Multimodal Benchmark for Evaluating Scientific Reasoning in Vision-Language Models

benchmarks

multimodality

reasoning

Contemporary vision-language models (VLMs) perform well on existing multimodal reasoning benchmarks (78-85\% accuracy on MMMU, MathVista). Yet, these results fail to sufficiently distinguish true scientific reasoning articulation capabilities from pattern-matching. To address this gap, we introduce \textbf{mmJEE-Eval}, a multimodal bilingual (English and Hindi) benchmark comprising 1,460 questions from India's JEE Advanced examination (2019-2025) spanning pre-college Physics, Chemistry, and Mathematics domains. Our evaluation of 17 state-of-the-art models reveals that while frontier VLMs (GPT-5, Gemini 2.5 Pro/Flash) achieve 77-84\% accuracy on held-out 2025 questions, open-source models plateau at 37-45\% despite scaling to 400B parameters, a significant difference not observed on existing benchmarks. While closed frontiers from Google and OpenAI show high problem-solving accuracies (up to 100\% pass@3 scores), they fully collapse when the reasoning load is increased meta-cognitively (GPT-5 fixes just 5.2\% errors). Systematic ablations show mmJEE-Eval's difficulty stems from complexity and reasoning depth rather than memorization. Effectively, our benchmark segregates superior training and reasoning methodologies where alternatives fail. We publicly release our code and data: https://mmjee-eval.github.io

poster

### 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).

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

Socio-demographic prompting (SDP) - prompting Large Language Models (LLMs) using demographic proxies to generate culturally aligned outputs - often shows LLM responses as stereotypical and biased. While effective in assessing LLMs' cultural competency, SDP is prone to confounding factors such as prompt sensitivity, decoding parameters, and the inherent difficulty of generation over discrimination tasks due to larger output spaces. These factors complicate interpretation, making it difficult to determine if the poor performance is due to bias or the task design. To address this, we use inverse socio-demographic prompting (ISDP), where we prompt LLMs to discriminate and predict the demographic proxy from actual and simulated user behavior from different users. We use the Goodreads-CSI dataset (Saha et al., 2025), which captures difficulty in understanding English book reviews for users from India, Mexico, and the USA, and test four LLMs: Aya-23, Gemma-2, GPT-4o, and LLaMA-3.1 with ISDP. Results show that models perform better with actual behaviors than simulated ones, contrary to what SDP suggests. However, performance with both behavior types diminishes and becomes nearly equal at the individual level, indicating limits to personalization.

To Generate or Discriminate? Methodological Considerations for Measuring Cultural Alignment in LLMs

Understanding how large language models (LLMs) reason across semantically distinct domains remains an open challenge. In this work, we investigate whether LLMs can connect personality traits to musical preferences, specifically chord progressions. Drawing on psychological theory and symbolic music structure, we introduce a novel benchmark that evaluates two interdependent tasks: (1) inferring personality traits from a textual context and (2) selecting a musically appropriate chord progression aligned with the inferred trait. We release a synthetic, expert-guided dataset grounded in Cattell's 16 Personality Factors (PF16), genre-conditioned chord structures, and diverse situational contexts. We explore multiple learning strategies, including fine-tuning task-specific corpora, model merging with LoRA adapters, and advanced prompt-based reasoning techniques such as verbalization. Additionally, we propose a teacher-student framework to evaluate the quality of model-generated explanations using a five-dimensional rubric. Our findings show that verbalization outperforms standard reasoning methods, achieving up to 11% improvement over zero-shot baselines.

Harmonious Minds: Benchmarking Intertwined Reasoning of Human Personality and Musical Preference

Multiple Choice Question (MCQ) answering is a widely used method for evaluating the performance of Large Language Models (LLMs). However, LLMs often exhibit selection bias in MCQ tasks, where their choices are influenced by factors like answer position or option symbols rather than the content. This bias undermines the reliability of MCQ as an evaluation framework. Most existing selection bias metrics require answer labels and measure divergences between prediction and answer distributions, but do not fully capture the consistency of a model’s predictions across different orderings of answer choices. Existing selection bias mitigation strategies have notable limitations: majority voting, though effective, is computationally prohibitive; calibration-based methods require validation sets and often fail to generalise across datasets. To address these gaps, we propose three key contributions: (1) a new unsupervised label-free \textbf{Permutation Bias Metric (PBM)} that directly quantifies inconsistencies in model predictions across answer permutations, providing a more precise measure of selection bias, (2) an efficient majority voting approach called Batch Question-Context KV caching (BaQCKV), to significantly reduce computational costs while preserving bias mitigation effectiveness, and (3) an unsupervised Low-Rank Adaptation (LoRA)-1 fine-tuning strategy based on our proposed metric and the BaQCKV that mitigates selection bias, providing a computationally efficient alternative that maintains model generalizability. Experiments across multiple MCQ benchmarks demonstrate that our approaches reduce bias, increasing consistency in accuracy while minimizing computational costs.

Quantifying and Mitigating Selection Bias in LLMs: A Transferable LoRA Fine-Tuning and Efficient Majority Voting Approach

Large language models (LLMs), despite their remarkable text generation capabilities, often hallucinate and generate text that is factually incorrect and not grounded in real-world knowledge. This poses serious risks in domains like healthcare, finance, and customer support. A typical way to use LLMs is via the APIs provided by LLM vendors where there is no access to model weights or options to fine-tune the model. Existing methods to detect hallucinations in such settings where the model access is restricted or constrained by resources typically require making multiple LLM API calls, increasing latency and API cost. We introduce CONFACTCHECK, an efficient hallucination detection approach that does not leverage any external knowledge base and works on the simple intuition that responses to factual probes within the generated text should be consistent within a single LLM and across different LLMs. Rigorous empirical evaluation on multiple datasets that cover both the generation of factual texts and the open generation shows that CONFACTCHECK can detect hallucinated facts efficiently using fewer resources and achieves higher accuracy scores compared to existing baselines that operate under similar conditions. Our code is available here.

Consistency Is the Key: Detecting Hallucinations in LLM Generated Text By Checking Inconsistencies About Key Facts

Vision-Language Models (VLMs) have demonstrated impressive capabilities across a range of tasks, yet concerns about their potential biases persist. This work investigates the cultural biases in state-of-the-art VLMs by evaluating their performance on an image-based country identification task at the country level. Utilizing the geographically diverse Country211 \citep{country211} dataset, we probe VLMs via open-ended questions, multiple-choice questions (MCQs), and include challenging multilingual and adversarial task settings. Our analysis aims to uncover disparities in model accuracy across different countries and question formats, providing insights into how training data distribution and evaluation methodologies may influence cultural biases in VLMs. The findings highlight significant variations in performance, suggesting that while VLMs possess considerable visual understanding, they inherit biases from their pre-training data and scale, which impact their ability to generalize uniformly across diverse global contexts.

Uncovering Cultural Representation Disparities in Vision-Language Models

Large Language Models (LLMs) are increasingly used as daily recommendation systems for tasks like education planning, yet their recommendations risk perpetuating societal biases. This paper empirically examines geographic, demographic, and economic biases in university and program suggestions from three open-source LLMs: LLaMA-3.1-8B, Gemma-7B, and Mistral-7B. Using 360 simulated user profiles varying by gender, nationality, and economic status, we analyze over 25,000 recommendations. Results show strong biases: institutions in the Global North are disproportionately favored, recommendations often reinforce gender stereotypes, and institutional repetition is prevalent. While LLaMA-3.1 achieves the highest diversity, recommending 481 unique universities across 58 countries, systemic disparities persist. To quantify these issues, we propose a novel, multi-dimensional evaluation framework that goes beyond accuracy by measuring demographic and geographic representation. Our findings highlight the urgent need for bias consideration in educational LMs to ensure equitable global access to higher education.

Where Should I Study? Biased Language Models Decide! Evaluating Fairness in LMs for Academic Recommendations

Rapid advancements in Visual Language Models (VLMs) have transformed multimodal understanding but are often constrained by generating English responses regardless of the input language. This phenomenon has been termed as Image-induced Fidelity Loss (IFL) and stems from limited multimodal multilingual training data. To address this, we propose a continuous multilingual integration strategy that injects text-only multilingual data during visual instruction tuning, preserving the language model's original multilingual capabilities. Extensive evaluations demonstrate that our approach significantly improves linguistic fidelity across languages without degradation in visual performance. We also explore model merging, which improves language fidelity but comes at the cost of visual performance. In contrast, our core method achieves robust multilingual alignment without trade-offs, offering a scalable and effective path to mitigating IFL for global VLM adoption.

Breaking Language Barriers in Visual Language Models via Multilingual Textual Regularization

Natural Language Processing is a branch of artificial intelligence that enables man- machine interactions through regional languages. In Kannada, there are two types of Sandhi: Kannada Sandhi and Sanskrit Sandhi. A morph-phonemic word "Sandhi" is created when two words or distinct morphemes are joined or combined. Conversely, Sandhi word splitting reverses this process. Rules governing Sandhi exist across all the Dravidian languages. A rule-based method has been developed to split Sanskrit Sandhi words into their components within Kannada sentences. Once the Sanskrit Sandhi (SS) words are split, the type of Sandhi is also identified, facilitating accurate translation of the Sanskrit Sandhi words into English. This paper discusses seven types of SS words: SavarNadeergha, YaN, GuNa, Vruddhi, Jatva, Shchutva and Anunasika Sandhi. The identified split points adhere precisely to Sandhi rules. A dataset of 4900 Sanskrit
Sandhi words found in Kannada sentences was used to evaluate the proposed method, which achieved an accuracy of 90.03% for Sanskrit Sandhi Identification and 85.87% for reliable English Translation. This work has potential applications in other Dravidian languages.

A Word-Splitting Approach to Kannada Sanskrit Sandhi Words Useful in Effective English Translation

Developing robust Natural Language Understanding (NLU) for morphologically rich Dravidian languages like Kannada, Malayalam, Tamil, and Telugu presents significant challenges due to their agglutinative nature and syntactic complexity. In this work, we present the Dravidian NLP Suite tackling five core tasks: Morphological Analysis (MA), POS Tagging (POS), Named Entity Recognition (NER), Dependency Parsing (DEP), and Coreference Resolution (CR), trained for monolingual models and multilingual models. To facilitate this, we present the Dravida dataset, meticulously annotated multilingual corpus for these tasks across all four languages. Our experiments demonstrate that a multilingual model, which utilizes shared linguistic features and cross-lingual patterns inherent to the Dravidian family, consistently outperforms its monolingual counterparts across all tasks. These findings suggest that multilingual learning is an effective approach for enhancing Natural Language Understanding (NLU) capabilities, particularly for languages belonging to the same family. To the best of our knowledge, this is the first work to jointly address all these core tasks on the Dravidian languages.

Family helps one another: Dravidian NLP suite for Natural Language Understanding

Simultaneous speech translation (SiST) requires balancing translation quality and latency. While most SiST systems follow machine translation assumptions that prioritize full semantic accuracy to the source, human interpreters often omit less critical content to catch up with the speaker. This study investigates whether omission can be used to reduce latency while preserving meaning in SiST.We construct a dataset that includes omission using large language models (LLMs) and propose a Target-Duration Latency (TDL), target-based latency metric that measures the output length considering the start and end timing of translation. Our analysis shows that LLMs can omit less important words while retaining the core meaning. Furthermore, experimental results show that although standard metrics overlook the benefit of the model trained with proposed omission-involving dataset, alternative evaluation methods capture it, as omission leads to shorter outputs with acceptable quality.

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To Generate or Discriminate? Methodological Considerations for Measuring Cultural Alignment in LLMs

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