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arXiv AI By Saleha Muzammil, Rahul Reddy, Vishal Kamalakrishnan, Hadi Ahmadi, Wajih Ul Hassan

Effective and Efficient Threat Hunting with Small Language Models

Read the original on arXiv AI →

The paper presents a framework for translating natural‑language queries into Kusto Query Language (KQL) using small language models (SLMs). It introduces lightweight retrieval, error‑aware prompting, LoRA fine‑tuning with rationale distillation, and a two‑stage architecture that pairs an SLM drafter with a low‑cost LLM judge. Evaluations on Microsoft’s NL2KQL Defender dataset show the two‑stage approach achieving high syntax and schema‑valid accuracy while dramatically reducing cost compared to larger LLM baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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