Researchers propose CLIC to compare coding behaviors of large language models
Researchers introduce CLIC (Code Learning for Identification and Comparison), a method to analyze and compare how different large language models generate code. Instead of relying only on performance metrics like pass@k, CLIC examines token-frequency patterns in code samples to distinguish model behaviors. The approach uses decision trees and two new metrics—robustness and concentration—to…
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Key points
- CLIC analyzes token frequencies in code to compare LLM coding behaviors
- Two new metrics, robustness and concentration, measure distinguishability and token distribution
- Case studies compare 10 LLMs across 22 Kaggle ML tasks
Read the original at arXiv cs.CL · by Junpeng Wang, Yuzhong Chen, Menghai Pan, Uday Singh Saini, Yiwei Cai primary sourceOpen source ↗
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