De-Hallucinator: Mitigating LLM Hallucinations in Code Generation Tasks via Iterative Grounding

Understand

Large language models (LLMs) trained on datasets of publicly available source code have established a new state of the art in code generation tasks.

• However, these models are mostly unaware of the code that exists within a specific project, preventing the models from making good use of existing APIs.
• Instead, LLMs often invent, or "hallucinate", non-existent APIs or produce variants of already existing code.
• This paper presents De-Hallucinator, a technique that grounds the predictions of an LLM through a novel combination of retrieving suitable API references and iteratively querying the model with increasingly suitable context information in the prompt.