23 citations · 26 across the 5 of their papers we have counts for
5 papers
Quality Assessment of Tabular Data using Large Language Models and Code Generation
Ashlesha Akella, Akshar Kaul, Krishnasuri Narayanam +1
Reliable data quality is crucial for downstream analysis of tabular datasets, yet rule-based validation often struggles with inefficiency, human intervention, and high computationa…
Data Wrangling Task Automation Using Code-Generating Language Models
Ashlesha Akella, Krishnasuri Narayanam
Ensuring data quality in large tabular datasets is a critical challenge, typically addressed through data wrangling tasks. Traditional statistical methods, though efficient, cannot…
QUIS: Question-guided Insights Generation for Automated Exploratory Data Analysis
Abhijit Manatkar, Ashlesha Akella, Parthivi Gupta +1
Discovering meaningful insights from a large dataset, known as Exploratory Data Analysis (EDA), is a challenging task that requires thorough exploration and analysis of the data. A…
An Automatic Prompt Generation System for Tabular Data Tasks
Ashlesha Akella, Abhijit Manatkar, Brij Chavda +1
Efficient processing of tabular data is important in various industries, especially when working with datasets containing a large number of columns. Large language models (LLMs) ha…
Deep hierarchical reinforcement agents for automated penetration testing
Khuong Tran, Ashlesha Akella, Maxwell Standen +4
Penetration testing the organised attack of a computer system in order to test existing defences has been used extensively to evaluate network security. This is a time consuming pr…