Human Evaluation in Large Language Model Testing: Assessing the Quality of AI Model Output

Abstract

LLMs excel in language tasks, but testing them effectively is tricky. Automated metrics help, but human evaluation is crucial for aspects like clarity, relevance, and ethics. This chapter explores methods and challenges of human LLM testing, including factors like fairness and user experience. The authors discuss a sample evaluation method and highlight ongoing efforts for robust evaluation to ensure responsible LLM development. Finally, they explore the use of LLMs in cybersecurity, showcasing their potential and challenges.