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From Data Querying to Data Investigations: Rethinking Natural Language Interfaces for Databases

From Data Querying to Data Investigations: Rethinking Natural Language Interfaces for Databases

Data Arxiv.org
D2 moves text-to-SQL beyond single queries by enabling autonomous data investigation loops that reason over intermediate results.
What: The paper introduces D2, a prototype system for data investigation that iterates through sequences of SQL queries and reasons over results to solve complex data tasks, rather than relying on one-shot query generation.
Why it matters: As AI agents become more prevalent, the bottleneck is shifting from 'translating natural language to a query' to 'planning complex, multi-step analytical investigations' that require persistent context.
Decoder
  • Text-to-SQL: A task where an AI model converts natural language into a single executable SQL query.
  • Data Investigation: A multi-step analytical process requiring reasoning, iterative searching, and synthesis of evidence from multiple queries.
Original article

From Data Querying to Data Investigations: Rethinking Natural Language Interfaces for Databases

Natural language (NL) interfaces to databases have been optimized for the wrong problem. The dominant Text-to-SQL paradigm assumes that users ask questions that can be answered by single SQL queries. In practice, however, users seek assistance with solving data problems. This requires searching a database by sequences of SQL queries while reasoning over intermediate results instead of just running one SQL query. This paper therefore introduces a new paradigm for NL interfaces to data, which we call data investigations. We present D^2, a first prototype of a data investigation system that embodies this vision by autonomously searching, reasoning over, and collecting data to solve data problems. Using a newly constructed benchmark based on the Murder Mystery dataset, we demonstrate the potential of D^2 for tasks that require data investigations with evidence-backed decisions, extending beyond the capabilities of traditional single-query question answering.
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