Code completer's ghost text feature fails eval due to self-prediction gap

A developer has created a code completion tool called pycomplete that utilizes a transformer model blended with n-gram models and a cache. While the tool's next-token prediction accuracy is 54.6%, its performance on generating "ghost text" (multi-token continuations) drops significantly to 7%, or one correct completion in fourteen attempts. This discrepancy arises because the evaluation metrics are trained on human-written code, whereas the ghost text feature relies on the model predicting its own generated output, leading to a performance cliff when evaluated on this different distribution. AI

IMPACT Highlights a critical gap in evaluating AI code generation models, suggesting current metrics may not capture real-world performance for features like ghost text completion.

RANK_REASON The item describes a specific software tool and its performance evaluation, not a general industry trend or release.

Read on dev.to — LLM tag →

• central processing unit
• n-gram
• NumPy
• pycomplete
• transformer

• product
• other

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