TRACER: A Semantic-Aware Framework for Fine-Grained Contamination Detection in Code LLMs
📰 ArXiv cs.AI
Learn how TRACER detects fine-grained contamination in code LLMs using semantic-aware framework and apply it to improve model reliability
Action Steps
- Build a contamination detection model using TRACER's semantic-aware framework
- Configure the model to detect three levels of semantic overlap: Functionally Identical, Nearly Identical, and Shared Logic
- Apply TRACER to a code LLM dataset to detect fine-grained contamination
- Test the effectiveness of TRACER in improving model reliability
- Compare TRACER's performance with existing contamination detection methods
Who Needs to Know This
AI engineers and researchers working with code LLMs can benefit from TRACER to detect contamination and improve model evaluation
Key Insight
💡 TRACER's semantic-aware framework detects fine-grained contamination in code LLMs using three levels of semantic overlap
Share This
🚨 Detect contamination in code LLMs with TRACER! 🚨
Key Takeaways
Learn how TRACER detects fine-grained contamination in code LLMs using semantic-aware framework and apply it to improve model reliability
Full Article
Title: TRACER: A Semantic-Aware Framework for Fine-Grained Contamination Detection in Code LLMs
Abstract:
arXiv:2605.24079v1 Announce Type: cross Abstract: Data contamination is a known threat to the reliability of model evaluation. However, it remains underexplored in code large language models (LLMs), where contamination often goes beyond exact duplication. We present TRACER, a semantic-aware framework for fine-grained code contamination detection. TRACER models contamination using three levels of semantic overlap - Functionally Identical, Nearly Identical, and Shared Logic - and detects them thro
Abstract:
arXiv:2605.24079v1 Announce Type: cross Abstract: Data contamination is a known threat to the reliability of model evaluation. However, it remains underexplored in code large language models (LLMs), where contamination often goes beyond exact duplication. We present TRACER, a semantic-aware framework for fine-grained code contamination detection. TRACER models contamination using three levels of semantic overlap - Functionally Identical, Nearly Identical, and Shared Logic - and detects them thro
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