RECSIP is a novel framework that improves the precision and reliability of Large Language Models through repeated clustering of output scores, critical for high-risk environments.

• Addresses the stochastic nature of LLMs that makes response reliability difficult to assess
• Introduces a clustering methodology to evaluate response consistency and reliability
• Significantly reduces harmful outputs in security-sensitive applications
• Provides a systematic approach to measure confidence in LLM responses

For security applications, this research is vital as it helps prevent potentially harmful or dangerous outputs from LLMs deployed in critical infrastructure, healthcare, or safety systems where incorrect responses could lead to serious consequences.

RECSIP: REpeated Clustering of Scores Improving the Precision