Shashwat Singh

I am an AI research student, currently pursuing an Undergrad + MS by Research program at IIIT Hyderabad under my advisor Dr. Ponnurangam Kumaraguru. I am interested in the science of Deep Learning, AI interpretability, and pretraining.

Some specific ideas I find interesting are:

In my free time, I like to read about history and watch movies. I love Pirates of the Caribbean :)

Publications

I have had the pleasure of working with Dr. Shauli Ravfogel, Dr. Ryan Cotterell, Dr. Ponnurangam Kumaraguru, Dr. Makarand Tapaswi, Saujas Vaduguru, and Shashwat Goel. Under their mentorship and guidance I have worked on project topics including interpretability of Language Model representations, training dynamics, diffusion, and vision-language models.

Emergence of Text Semantics in CLIP Image Encoders

Published in UniReps: 2nd Edition of the Workshop on Unifying Representations in Neural Models (NeurIPS workshop 2024), 2024

Humans process text visually; our work studies the semantics of text rendered in images. We show that the semantic information captured by image representations can decisively classify the sentiment of sentences and is robust against visual attributes like font and not based on simple character frequency associations.

Authors: Sreeram Vennam*, Shashwat Singh*, Anirudh Govil, Ponnurangam Kumaraguru
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Representation Surgery: Theory and Practice of Affine Steering

Published in The Forty-first International Conference on Machine Learning, 2024

This paper investigates the formal and empirical properties of steering functions, i.e., transformation of the neural language model’s representations that alter its behavior.

Authors: Singh, S.∗, Ravfogel, S.∗, Herzig, J., Aharoni, R., Cotterell, R., Kumaraguru, P. (2024)
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Probing Negation in Language Models

Published in The 8th Workshop on Representation Learning for NLP (RepL4NLP). 61st Annual Meeting of the Association for Computational Linguistics (ACL), 2023

We hypothesize about why pretrained LMs are inconsistent under negation: when the statement could refer to multiple ground entities with conflicting properties, negation may not entail a change in output. This means negation minimal pairs in different training samples can have the same completion in pretraining corpora.

Authors: Shashwat Singh*, Shashwat Goel*, Saujas Vaduguru, Ponnurangam Kumaraguru
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