Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs

TL;DR

This paper surveys a comprehensive framework for evaluating LLMs to enhance trust, transparency, and safety across domains. It integrates traditional metrics (perplexity, BLEU, ROUGE, etc.) with advanced interpretability and governance tools (LLMMaps, Bloom’s taxonomy visualization, Shapley values, attention visualizations, counterfactual explanations) and emphasizes human-in-the-loop assessment. Key contributions include a stratified and hierarchical approach to analysis (knowledge stratification, taxonomy-based visualization), a suite of robustness, fairness, and adversarial tests, and practical protocols for benchmarking, leaderboards, and domain-specific evaluation. The work highlights the practical significance of combining quantitative metrics with qualitative judgments to guide responsible development, deployment, and continuous improvement of LLMs in real-world settings, including education, healthcare, and law.

Abstract

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.