DRAMA: Unifying Data Retrieval and Analysis for Open-Domain Analytic Queries
Chuxuan Hu, Maxwell Yang, James Weiland, Yeji Lim, Suhas Palawala, Daniel Kang

TL;DR
DRAMA introduces an end-to-end system that unifies data retrieval, transformation, and analysis to answer open-domain analytic queries efficiently, supported by a new benchmark and a multi-agent system that outperforms existing methods.
Contribution
This work presents DRAMA, a novel paradigm and system that integrates data collection, transformation, and reasoning for open-domain analytics, along with a benchmark and a multi-agent implementation.
Findings
DRAMA-Bot achieves 86.5% accuracy on DRAMA-Bench.
DRAMA-Bot reduces cost to less than one-sixth of baseline methods.
DRAMA outperforms state-of-the-art agents by up to 6.9 times in accuracy.
Abstract
Manually conducting real-world data analyses is labor-intensive and inefficient. Despite numerous attempts to automate data science workflows, none of the existing paradigms or systems fully demonstrate all three key capabilities required to support them effectively: (1) open-domain data collection, (2) structured data transformation, and (3) analytic reasoning. To overcome these limitations, we propose DRAMA, an end-to-end paradigm that answers users' analytic queries in natural language on large-scale open-domain data. DRAMA unifies data collection, transformation, and analysis as a single pipeline. To quantitatively evaluate system performance on tasks representative of DRAMA, we construct a benchmark, DRAMA-Bench, consisting of two categories of tasks: claim verification and question answering, each comprising 100 instances. These tasks are derived from real-world applications…
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Taxonomy
TopicsTopic Modeling · Multimodal Machine Learning Applications · Explainable Artificial Intelligence (XAI)
