Papers by Ashlesha Akella
QUIS: Question-guided Insights Generation for Automated Exploratory Data Analysis (2024.emnlp-industry)
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| Challenge: | Exploratory Data Analysis (EDA) is a complex task that requires thorough exploration and analysis of the data. |
| Approach: | They propose a fully automated EDA system that generates questions in iterations and analyzes data to produce multiple relevant insights. |
| Outcome: | QUIS is a fully automated EDA system that generates questions in iterations without human intervention or manually curated examples. |
Quality Assessment of Tabular Data using Large Language Models and Code Generation (2025.emnlp-industry)
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| Challenge: | Data quality is vital for business decisions; poor data quality costs organizations an average of $12.9 million annually. |
| Approach: | They propose a framework that combines statistical inliner detection with LLM-driven rule and code generation. |
| Outcome: | The proposed framework produces semantically valid quality rules and validates them with retrieval-augmented generation (RAG) Extensive evaluations on benchmark datasets confirm the effectiveness of the proposed framework. |
CodeGenWrangler: Data Wrangling task automation using Code-Generating Models (2025.naacl-industry)
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| Challenge: | Tabular datasets in industrial settings often encompass extensive data with numerous rows and columns. |
| Approach: | They propose a system that leverages large language models to generate executable code for data wrangling tasks . they identify inherent patterns in the data while leveraging external knowledge . |
| Outcome: | The proposed system detects patterns in the data while leveraging external knowledge . it generates executable code for data-wrangling tasks like missing value imputation and error correction . |