All Leaks Count, Some Count More: Interpretable Temporal Contamination Detection and Mitigation in LLM Backtesting
📰 ArXiv cs.AI
Learn to detect and mitigate temporal contamination in LLM backtesting using Shapley-DCLR, a framework that quantifies decision impact of atomic claims
Action Steps
- Apply Shapley values to decompose prediction rationales into atomic claims
- Configure the Shapley-DCLR framework to quantify decision impact of each claim
- Run backtesting experiments on LLMs using the Shapley-DCLR framework
- Test the effectiveness of Shapley-DCLR in detecting temporal contamination
- Build a mitigation strategy to address detected contamination
Who Needs to Know This
Data scientists and AI engineers working on LLMs can benefit from this framework to improve model reliability and fairness, while product managers can use it to inform model deployment decisions
Key Insight
💡 Shapley values can be used to quantify the decision impact of individual claims in LLM predictions
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🚨 Detect temporal contamination in LLMs with Shapley-DCLR! 🚨
Key Takeaways
Learn to detect and mitigate temporal contamination in LLM backtesting using Shapley-DCLR, a framework that quantifies decision impact of atomic claims
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