Papers by Mitesh M Khapra
FairI Tales: Evaluation of Fairness in Indian Contexts with a Focus on Bias and Stereotypes (2025.acl-long)
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Janki Atul Nawale, Mohammed Safi Ur Rahman Khan, Janani D, Mansi Gupta, Danish Pruthi, Mitesh M Khapra
| Challenge: | Existing studies on fairness of LLMs are largely Western-focused, making them inadequate for culturally diverse countries such as India. |
| Approach: | They propose a benchmark to evaluate fairness of LLMs across 85 identity groups . they consult domain experts to curate over 1,800 socio-cultural topics . |
| Outcome: | The benchmark evaluates LLMs across 85 identities across 85 castes, religions, regions, and tribes. |
Towards Building Large Scale Datasets and State-of-the-Art Automatic Speech Translation Systems for 14 Indian Languages (2025.acl-long)
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Ashwin Sankar, Sparsh Jain, Nikhil Narasimhan, Devilal Choudhary, Dhairya Suman, Mohammed Safi Ur Rahman Khan, Anoop Kunchukuttan, Mitesh M Khapra, Raj Dabre
| Challenge: | Existing datasets that cover only a fraction of Indian languages lack the breadth needed to generalize beyond curated benchmarks. |
| Approach: | They propose to build the largest speech translation dataset for Indian languages . they use a three-step methodology to gather data and train a model that performs better . |
| Outcome: | The proposed model improves on existing models and is open-source with permissive licenses. |
Cross-Lingual Auto Evaluation for Assessing Multilingual LLMs (2025.acl-long)
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Sumanth Doddapaneni, Mohammed Safi Ur Rahman Khan, Dilip Venkatesh, Raj Dabre, Anoop Kunchukuttan, Mitesh M Khapra
| Challenge: | Evaluating machine-generated text remains a challenge in NLP for non-English languages . current evaluation frameworks focus on English, revealing a gap in multilingual evaluations . |
| Approach: | They propose a cross-lingual auto evaluation framework that includes evaluator LLMs and a test set specifically designed for multilingual evaluation. |
| Outcome: | The proposed model aligns more closely with human judgments than proprietary models on non-English language evaluations. |
Can Vision-Language Models Evaluate Handwritten Math? (2025.acl-long)
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| Challenge: | Recent advances in Vision-Language Models (VLMs) have significantly enhanced the ability to interpret both textual and visual data. |
| Approach: | They propose a benchmark to assess VLMs’ ability to detect, localize and correct errors in handwritten mathematical content. |
| Outcome: | The proposed benchmark covers over 2,200 handwritten math solutions from 609 manually curated problems from grades 7-12 with intentionally introduced perturbations. |