Publications
Conference proceedings — for shared task system papers you may check Scholar.
Dressing the Imagination: A Dataset for AI-Powered Translation of Text into Fashion Outfits and A Novel NeRA Adapter for Enhanced Feature Adaptation
Gayatri Deshmukh, Somsubhra De, Chirag Sehgal, Jishu Sen Gupta, Sparsh Mittal
WACV 2026, 2026
TL;DR: We present FLORA (Fashion Language Outfit Representation for Apparel Generation), the 1st curated dataset of fashion outfit sketches paired with rich, industry-grade textual descriptions. FLORA aims to advance AI-driven fashion design and assist designers and end-users in bringing creative ideas to life. We also propose NeRA (Nonlinear low-rank Expressive Representation Adapter), a novel parameter-efficient adapter based on Kolmogorov-Arnold Networks (KANs).
@InProceedings{Deshmukh_2026_WACV,
author = {Deshmukh, Gayatri and De, Somsubhra and Sehgal, Chirag and Gupta, Jishu Sen and Mittal, Sparsh},
title = {Dressing the Imagination: A Dataset for AI-Powered Translation of Text into Fashion Outfits and A Novel NeRA Adapter for Enhanced Feature Adaptation},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2026},
pages = {2094-2103}
}
LLMs – the Good, the Bad or the Indispensable?: A Use Case on Legal Statute Prediction and Legal Judgment Prediction on Indian Court Cases
Shaurya Vats, Atharva Zope, Somsubhra De, Anurag Sharma, Upal Bhattacharya, Shubham Kumar Nigam, Shouvik Guha, Koustav Rudra, Kripabandhu Ghosh
EMNLP 2023 (Findings), 2023
The Large Language Models (LLMs) have impacted many real-life tasks. To examine the efficacy of LLMs in a high-stake domain like law, we have applied state-of-the-art LLMs for two popular tasks: Statute Prediction and Judgment Prediction, on Indian Supreme Court cases. We see that while LLMs exhibit excellent predictive performance in Statute Prediction, their performance dips in Judgment Prediction when compared with many standard models. The explanations generated by LLMs (along with prediction) are of moderate to decent quality. We also see evidence of gender and religious bias in the LLM-predicted results. In addition, we present a note from a senior legal expert on the ethical concerns of deploying LLMs in these critical legal tasks.
@inproceedings{vats-etal-2023-llms,
title = "{LLM}s {--} the Good, the Bad or the Indispensable?: A Use Case on Legal Statute Prediction and Legal Judgment Prediction on {I}ndian Court Cases",
author = "Vats, Shaurya and
Zope, Atharva and
De, Somsubhra and
Sharma, Anurag and
Bhattacharya, Upal and
Nigam, Shubham Kumar and
Guha, Shouvik and
Rudra, Koustav and
Ghosh, Kripabandhu",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.831/",
doi = "10.18653/v1/2023.findings-emnlp.831",
pages = "12451--12474",
abstract = "The Large Language Models (LLMs) have impacted many real-life tasks. To examine the efficacy of LLMs in a high-stake domain like law, we have applied state-of-the-art LLMs for two popular tasks: Statute Prediction and Judgment Prediction, on Indian Supreme Court cases. We see that while LLMs exhibit excellent predictive performance in Statute Prediction, their performance dips in Judgment Prediction when compared with many standard models. The explanations generated by LLMs (along with prediction) are of moderate to decent quality. We also see evidence of gender and religious bias in the LLM-predicted results. In addition, we present a note from a senior legal expert on the ethical concerns of deploying LLMs in these critical legal tasks."
}