Synthetic Hallucinations, Real Gains: Hard Negatives from Frontier Models for FIM Hallucination Mitigation
arXiv:2606.03130v1 Announce Type: new Abstract: Small open-source code models that power IDE autocomplete still emit hallucinated Fill-in-the-Middle (FIM) completions: syntactically natural calls to methods, parameters, variables, and imports that do not exist in the surrounding project. Existing mitigations either require per-language execution sandboxes that do not apply at mid-keystroke or preference-optimisation pipelines that need large human-labelled corpora. We propose an execution-free alternative: use frontier code models to synthesise plausible-but-wrong completions as hard negatives
The proliferation of open-source code models necessitates robust and efficient methods to mitigate common errors like hallucinations, especially for productivity tools like IDE autocompletion.
Improving the reliability and accuracy of AI-powered coding assistants directly impacts developer productivity, software quality, and the broader adoption of AI in software development workflows.
This research introduces a novel, execution-free method for training code models to avoid hallucinations by using frontier models to generate 'hard negatives', potentially making AI-driven development tools more robust and less resource-intensive to refine.
- · Software developers
- · Companies offering IDEs and coding assistants
- · Open-source AI model developers
- · Developers reliant on error-prone FIM completions
- · Previous FIM mitigation methods requiring extensive human-labelled data or costl
Reduced code errors and refactoring time due to more accurate FIM completions in IDEs.
Increased trust and integration of AI-powered coding tools across the software development lifecycle.
Accelerated innovation in software development through more reliable and efficient human-AI collaboration.
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Read at arXiv cs.LG