DatalogBench: Evaluating Large Language Models on Text-to-Datalog Synthesis
Datalog underpins reasoning tasks such as program analysis, but its programs are hard to write. Existing synthesizers automate this task but require users to state their intent as input-output examples. Large language models (LLMs) suggest a more natural route, text-to-Datalog synthesis from a natural-language question, yet how well they do so has not been systematically evaluated. We present DatalogBench, a benchmark of 136 text-to-Datalog synthesis tasks curated from existing Datalog-based artifacts. Synthesized programs are graded by execution on held-out inputs against an oracle validated by mutation analysis. Across six LLMs and four prompting configurations, exact match peaks at 68....
| Authors | Yuan Li, Hanyun Jiang, Guowei Tian et al. |
|---|---|
| Primary category | cs.PL |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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