An Adaptive Benchmark for Modeling User Exploration of Large Datasets
Summary: Proposes SIMBA, an adaptive benchmark that simulates data exploration by generating SQL-based interaction sequences to encode goals. It measures progress by equating user queries with goals, enabling goal-driven benchmarking and exposing IDEBench misses. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Joanna Purich
- 2. Anthony Wise
- 3. Leilani Battle
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