Automated formal specification generation is a key step towards program understanding and formal verification. Recently, due to the success of large language models (LLMs) in code generation, researchers have made early attempts to adopt LLMs for generating formal specifications. However, the lack of formal specification language corpora in the wild often makes LLMs fail to generate syntactically correct and semantically verifiable specifications. To mitigate this gap, we propose KBSpec, which augments LLMs with dual-source knowledge of formal specification language: external knowledge from official documentation, and internal knowledge distilled from verifier feedback on LLM-generated specifications. KBSpec maintains a self-evolving knowledge base that is continuously updated from successful generation and repair trajectories, without any LLM parameter tuning or labeled training data. We evaluate KBSpec on Java Modeling Language (JML) specification generation with three LLM backends, and results show that KBSpec improves verification pass rates by 10-25% over state-of-the-art LLM-based approaches, while producing the largest number of high-completeness specifications.

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