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Kartik Sreenivasan

7 accepted papers

2025

Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models

ICLR 2025poster

In this work, we investigate whether small language models can determine high-quality subsets of large-scale text datasets that improve the performance of larger language models. While existing work has shown that pruning based on the perplexity of a larger model can yield high-quality data, we inve…

Cited by 25SourcePDFScholar
2024

Teaching Arithmetic to Small Transformers

ICLR 2024poster

Large language models like GPT-4 exhibit emergent capabilities across general-purpose tasks, such as basic arithmetic, when trained on extensive text data, even though these tasks are not explicitly encoded by the unsupervised, next-token prediction objective. This study investigates how even small…

2023

Dissecting Chain-of-Thought: Compositionality through In-Context Filtering and Learning

NeurIPS 2023poster

Chain-of-thought (CoT) is a method that enables language models to handle complex reasoning tasks by decomposing them into simpler steps. Despite its success, the underlying mechanics of CoT are not yet fully understood. In an attempt to shed light on this, our study investigates the impact of CoT o…

2022

Finding Nearly Everything within Random Binary Networks

AISTATS 2022poster

A recent work by Ramanujan et al. (2020) provides significant empirical evidence that sufficiently overparameterized, random neural networks contain untrained subnetworks that achieve state-of-the-art accuracy on several predictive tasks. A follow-up line of theoretical work provides justification o…

Cited by 9SourcePDFScholar
2022

Rare Gems: Finding Lottery Tickets at Initialization

NeurIPS 2022accept

Large neural networks can be pruned to a small fraction of their original size, with little loss in accuracy, by following a time-consuming "train, prune, re-train" approach. Frankle & Carbin conjecture that we can avoid this by training lottery tickets, i.e., special sparse subnetworks found at ini…

2021

An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks

NeurIPS 2021poster

It is well known that modern deep neural networks are powerful enough to memorize datasets even when the labels have been randomized. Recently, Vershynin(2020) settled a long standing question by Baum(1988), proving that deep threshold networks can memorize $n$ points in $d$ dimensions using $\widet…

Cited by 21SourcePDFScholar
2020

Attack of the Tails: Yes, You Really Can Backdoor Federated Learning

NeurIPS 2020poster

Due to its decentralized nature, Federated Learning (FL) lends itself to adversarial attacks in the form of backdoors during training. The goal of a backdoor is to corrupt the performance of the trained model on specific sub-tasks (e.g., by classifying green cars as frogs). A range of FL backdoor at…