Papers by Nishant Mishra
DRISHTIKON: A Multimodal Multilingual Benchmark for Testing Language Models’ Understanding on Indian Culture (2025.emnlp-main)
Copied to clipboard
Arijit Maji, Raghvendra Kumar, Akash Ghosh, null Anushka, Nemil Shah, Abhilekh Borah, Vanshika Shah, Nishant Mishra, Sriparna Saha
| Challenge: | DRISHTIKON is a first-of-its-kind multimodal and multilingual benchmark centered exclusively on Indian culture. |
| Approach: | They evaluate a wide range of vision-language models across zero-shot and chain-of-thought settings and use them to evaluate cultural understanding of generative AI systems. |
| Outcome: | The DRISHTIKON dataset covers 15 languages, all states and union territories, and incorporating over 64,000 aligned text-image pairs. |
LLM aided semi-supervision for efficient Extractive Dialog Summarization (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a method to extract dialog summarization data from unlabeled data is currently expensive to build. |
| Approach: | They propose a method to extract dialog summarization using unlabeled data . they frame summarizing as a question-answering problem and use pseudo-labels to fine-tune a chat summarisation model . |
| Outcome: | The proposed method achieves 65.9/57.0/61.0 ROUGE-1/-2/-L on a TWEETSUMM dataset compared with current state-of-the-art methods on the entire training dataset. |
Clustering-based Sampling for Few-Shot Cross-Domain Keyphrase Extraction (2024.findings-eacl)
Copied to clipboard
| Challenge: | Scientific domain-specific pre-training has led to state-of-the-art keyphrase extraction performance with a majority of benchmarks being within the domain. |
| Approach: | They propose to leverage topic information available in the data to build a clustering-based sampling approach that facilitates selecting a few samples to label from the target domain . |
| Outcome: | The proposed approach leads to 26.35 points in performance when compared to selecting few-shot samples uniformly at random. |