CITBench: A Comprehensive Benchmark for Interactive Tabular Data Processing with LLMs
Tabular data processing is central to data work, and LLM-based assistants have recently shown promising capabilities in supporting such tasks. However, existing benchmarks primarily focus on table reasoning under single-turn, fully specified instructions, underrepresenting complex table processing that unfolds through multi-turn interactions with evolving user requirements. To bridge this gap, we introduce CITBench, a comprehensive benchmark for evaluating LLMs on interactive tabular data processing. CITBench features a comprehensive taxonomy across four high-level categories--table matching, cleaning, augmentation, and transformation--spanning 18 task types and 1,296 instances curated fr...
| Authors | Zihan Nan, Yang Gu, Wei Liu et al. |
|---|---|
| Primary category | cs.DB |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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