  This paper surveys evaluation techniques to enhance the trustworthiness and
understanding of Large Language Models (LLMs). As reliance on LLMs grows,
ensuring their reliability, fairness, and transparency is crucial. We explore
algorithmic methods and metrics to assess LLM performance, identify weaknesses,
and guide development towards more trustworthy applications. Key evaluation
metrics include Perplexity Measurement, NLP metrics (BLEU, ROUGE, METEOR,
BERTScore, GLEU, Word Error Rate, Character Error Rate), Zero-Shot and Few-Shot
Learning Performance, Transfer Learning Evaluation, Adversarial Testing, and
Fairness and Bias Evaluation. We introduce innovative approaches like LLMMaps
for stratified evaluation, Benchmarking and Leaderboards for competitive
assessment, Stratified Analysis for in-depth understanding, Visualization of
Blooms Taxonomy for cognitive level accuracy distribution, Hallucination Score
for quantifying inaccuracies, Knowledge Stratification Strategy for
hierarchical analysis, and Machine Learning Models for Hierarchy Generation.
Human Evaluation is highlighted for capturing nuances that automated metrics
may miss. These techniques form a framework for evaluating LLMs, aiming to
enhance transparency, guide development, and establish user trust. Future
papers will describe metric visualization and demonstrate each approach on
practical examples.
