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Shubhabrata Sengupta

3 accepted papers

2026

How Text Quality Interventions Reshape Neural Scaling Laws for LLMs: Empirical Study

ICLR 2026poster

Neural scaling laws are widely used for performance projection and resource planning, yet their sensitivity to data quality interventions remains poorly understood. We present an empirical study of how interventions—deduplication, heuristic filtering, and LLM-guided rewriting—reshape scaling behavi…

Cited by 0SourceScholar
2025

Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls

EMNLP 2025

Training data plays a crucial role in Large Language Models (LLM) scaling, yet high quality data is of limited supply. Synthetic data techniques offer a potential path toward sidestepping these limitations.We conduct a large-scale empirical investigation (>1000 LLMs with >100k GPU hours) using a uni

2021

CrypTen: Secure Multi-Party Computation Meets Machine Learning

NeurIPS 2021poster

Secure multi-party computation (MPC) allows parties to perform computations on data while keeping that data private. This capability has great potential for machine-learning applications: it facilitates training of machine-learning models on private data sets owned by different parties, evaluation o…

Cited by 456SourcePDFScholar