Papers by Pretam Ray
Chandomitra: Towards Generating Structured Sanskrit Poetry from Natural Language Inputs (2026.eacl-long)
Copied to clipboard
Manoj Balaji Jagadeeshan, Samarth Bhatia, Pretam Ray, Harshul Raj Surana, Akhil Rajeev P, Priya Mishra, Annarao Kulkarni, Ganesh Ramakrishnan, Prathosh Ap, Pawan Goyal
| Challenge: | Large language models are capable of creative generation tasks but prominently for high-resource languages. |
| Approach: | They propose to use large language models for structured poetry generation in Sanskrit . their constrained decoding method achieves 99.86% syntactic accuracy . |
| Outcome: | The proposed model outperforms the existing model in generating metrically valid Sanskrit poetry. |
AdaptEvolve: Improving Efficiency of Evolutionary AI Agents through Adaptive Model Selection (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing routing strategies rely on static heuristics or external controllers to optimize performance. |
| Approach: | They propose a framework that leverages intrinsic generation confidence to estimate solvability. |
| Outcome: | Empirical results show that confidence-driven selection yields favorable Pareto frontier . computational cost of state-of-the-art large language models remains a key barrier to scalable deployment . |
CSSL: Contrastive Self-Supervised Learning for Dependency Parsing on Relatively Free Word Ordered and Morphologically Rich Low Resource Languages (2024.emnlp-main)
Copied to clipboard
| Challenge: | Neural dependency parsing has achieved remarkable performance for low resource morphologically rich languages. |
| Approach: | They propose a self-supervised learning method to make the model robust to word order variations. |
| Outcome: | The proposed model shows a substantial gain of 3.03/2.95 points in 7 relatively free word order languages when compared to the best performing baseline. |