1 citations · 1 across the 4 of their papers we have counts for
5 papers
The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs
Akshit Sinha, Arvindh Arun, Shashwat Goel +2
Does continued scaling of large language models (LLMs) yield diminishing returns? In this work, we show that short-task benchmarks may give an illusion of slowing progress, as even…
A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models
Soham Petkar, Hari Aakash K, Anirudh Vempati +3
Developments in Graph-Language Models (GLMs) aim to integrate the structural reasoning capabilities of Graph Neural Networks (GNNs) with the semantic understanding of Large Languag…
Topo Goes Political: TDA-Based Controversy Detection in Imbalanced Reddit Political Data
Arvindh Arun, Karuna K Chandra, Akshit Sinha +4
The detection of controversial content in political discussions on the Internet is a critical challenge in maintaining healthy digital discourse. Unlike much of the existing litera…
A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks
Varshita Kolipaka, Akshit Sinha, Debangan Mishra +4
Graph Neural Networks (GNNs) are increasingly being used for a variety of ML applications on graph data. Because graph data does not follow the independently and identically distri…
Higher Order Structures For Graph Explanations
Akshit Sinha, Sreeram Vennam, Charu Sharma +1
Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations of graph-structured data, demonstrating remarkable performance across various tasks. Recogn…