activity
20162024
most citedUnderstanding the Capabilities of Large Language Models for Automated Planning

6 citations · 24 across the 18 of their papers we have counts for

collaborators

18 papers

cs.CL2024

PLANTS: A Novel Problem and Dataset for Summarization of Planning-Like (PL) Tasks

Vishal Pallagani, Biplav Srivastava, Nitin Gupta

Text summarization is a well-studied problem that deals with deriving insights from unstructured text consumed by humans, and it has found extensive business applications. However,…

cs.AI2024

BEACON: Balancing Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes

Vansh Nagpal, Siva Likitha Valluru, Kausik Lakkaraju +1

A common, yet regular, decision made by people, whether healthy or with any health condition, is to decide what to have in meals like breakfast, lunch, and dinner, consisting of a…

cs.LG2024

Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lens

Kausik Lakkaraju, Rachneet Kaur, Zhen Zeng +4

AI systems are notorious for their fragility; minor input changes can potentially cause major output swings. When such systems are deployed in critical areas like finance, the cons…

cs.AI20241 cited

The Case for Developing a Foundation Model for Planning-like Tasks from Scratch

Biplav Srivastava, Vishal Pallagani

Foundation Models (FMs) have revolutionized many areas of computing, including Automated Planning and Scheduling (APS). For example, a recent study found them useful for planning p…

cs.AI20246 cited

From Cloud to Edge: Rethinking Generative AI for Low-Resource Design Challenges

Sai Krishna Revanth Vuruma, Ashley Margetts, Jianhai Su +2

Generative Artificial Intelligence (AI) has shown tremendous prospects in all aspects of technology, including design. However, due to its heavy demand on resources, it is usually…

cs.CL2024

The Effect of Human v/s Synthetic Test Data and Round-tripping on Assessment of Sentiment Analysis Systems for Bias

Kausik Lakkaraju, Aniket Gupta, Biplav Srivastava +2

Sentiment Analysis Systems (SASs) are data-driven Artificial Intelligence (AI) systems that output polarity and emotional intensity when given a piece of text as input. Like other…