LLM Confidence Metrics for Code Completion Evaluated
A new study on arXiv (2508.16131v2) explores using intrinsic metrics like perplexity, entropy, and mutual information to measure LLM confidence in code completion tasks. The authors argue these metrics are simpler and more universal than downstream metrics, serving as proxies for functional correctness and hallucination risk. Code completion, which provides missing tokens from context, has been enhanced by code LLMs. The paper evaluates confidence across various models, aiming to improve reliability in code generation.
Key facts
- Study appears on arXiv with ID 2508.16131v2
- Focuses on LLM confidence in code completion
- Uses intrinsic metrics: perplexity, entropy, mutual information
- Intrinsic metrics are simpler and more universal than downstream metrics
- Code completion provides missing tokens from surrounding context
- Code LLMs are fine-tuned on code for this task
- Intrinsic metrics can proxy for functional correctness and hallucination risk
- The study evaluates confidence across diverse LLMs
Entities
Institutions
- arXiv