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Ashish Kulkarni

4 accepted papers

2026

BhashaKritika: Building Synthetic Pretraining Data at Scale for Indic Languages

AAAI 2026technical

In the context of pretraining of Large Language Models (LLMs), synthetic data has emerged as an alternative for generating high-quality pretraining data at scale. This is particularly beneficial in low resource language settings where the benefits of the recent LLMs have been unevenly distributed ac

Cited by 0SourcePDFScholar
2024

MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning

NAACL 2024long

Tool-augmented Large Language Models (TALMs) are known to enhance the skillset of large language models (LLMs), thereby, leading to their improved reasoning abilities across many tasks. While, TALMs have been successfully employed in different question-answering benchmarks, their efficacy on complex…

2023

Improving Speech Prosody of Audiobook Text-To-Speech Synthesis with Acoustic and Textual Contexts

ICASSP 2023accepted

We present a multi-speaker Japanese audiobook text-to-speech (TTS) system that leverages multimodal context information of preceding acoustic context and bilateral textual context to improve the prosody of synthetic speech. Previous work either uses unilateral or single-modality context, which does…

Cited by 0SourceScholar
2021

Optimizing Part Placement for Improving Accuracy of Robot-Based Additive Manufacturing

ICRA 2021poster

Robotic manipulators are increasingly being used to perform additive manufacturing. The accuracy of a built part is dependent on the trajectory execution error of the manipulator. For articulated manipulators, the trajectory execution error and achievable build accuracy vary considerably over the wo…

Cited by 19SourceScholar