We work on natural language processing and computational linguistics, with a focus on Indian languages — classical and low-resource languages. Our work spans building datasets, tools, and evaluation frameworks that are grounded in real linguistic structure.
Active research areas include:
Large language models — evaluation, explainability, and hallucination mitigation
Knowledge graphs — construction, repair, and question answering
BHRAM-IL: A Benchmark for Hallucination Recognition and Assessment in Multiple Indian Languages
Hrishikesh Terdalkar, Kirtan Bhojani, Aryan Dongare, and 1 more author
In Proceedings of the 1st Workshop on Benchmarks, Harmonization, Annotation, and Standardization for Human-Centric AI in Indian Languages (BHASHA 2025), Dec 2025
Large language models (LLMs) are increasingly deployed in multilingual applications but often generate plausible yet incorrect or misleading outputs, known as hallucinations. While hallucination detection has been studied extensively in English, under-resourced Indian languages remain largely unexplored. We present BHRAM-IL, a benchmark for hallucination recognition and assessment in multiple Indian languages, covering Hindi, Gujarati, Marathi, Odia, along with English. The benchmark comprises 36,047 curated questions across nine categories spanning factual, numerical, reasoning, and linguistic tasks. We evaluate 14 state-of-the-art multilingual LLMs on a benchmark subset of 10,265 questions, analyzing cross-lingual and factual hallucinations across languages, models, scales, categories, and domains using category-specific metrics normalized to (0,1) range. Aggregation over all categories and models yields a primary score of 0.23 and a language-corrected fuzzy score of 0.385, demonstrating the usefulness of BHRAM-IL for hallucination-focused evaluation. The dataset, and the code for generation and evaluation are available on GitHub (https://github.com/sambhashana/BHRAM-IL/) and HuggingFace (https://huggingface.co/datasets/sambhashana/BHRAM-IL/) to support future research in multilingual hallucination detection and mitigation.
@inproceedings{terdalkar-etal-2025-bhram,title={{BHRAM}-{IL}: A Benchmark for Hallucination Recognition and Assessment in Multiple {I}ndian Languages},author={Terdalkar, Hrishikesh and Bhojani, Kirtan and Dongare, Aryan and Behera, Omm Aditya},booktitle={Proceedings of the 1st Workshop on Benchmarks, Harmonization, Annotation, and Standardization for Human-Centric AI in Indian Languages (BHASHA 2025)},month=dec,year={2025},address={Mumbai, India},publisher={Association for Computational Linguistics},url={https://aclanthology.org/2025.bhasha-1.9/},pages={102--116},}
