Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs (2406.01943v1)

Abstract: This paper surveys evaluation techniques to enhance the trustworthiness and understanding of 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.

The paper "Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs" published in June 2024, discusses the imperative of building trust in LLMs as their adoption increases across various applications. Trust in LLMs is tied to their reliability, fairness, and transparency, which this paper addresses by surveying a range of evaluation techniques.

The authors categorize the evaluation methods into several key areas:

1. Performance Metrics:
   • Perplexity Measurement: Used for evaluating the quality of text generated by LLMs by quantifying how well a probabilistic model predicts a sample.
   • NLP Metrics: This includes BLEU, ROUGE, METEOR, BERTScore, GLEU, Word Error Rate, and Character Error Rate, which are standard metrics in NLP for assessing the quality of language generation and comprehension.
   • Zero-Shot and Few-Shot Learning Performance: Measuring how well LLMs can generalize to new, unseen tasks with little to no task-specific training.
   • Transfer Learning Evaluation: Assessing the ability of LLMs to transfer knowledge from pre-trained models to solve specific tasks effectively.
2. Adversarial Testing: Examining LLMs’ robustness by exposing them to adversarial inputs to identify vulnerabilities and areas for improvement.
3. Fairness and Bias Evaluation: Methods to detect and mitigate biases within LLMs, ensuring equitable treatment across different demographic groups and content contexts.

The authors introduce several innovative approaches to enhance the evaluation and interpretability of LLMs:

• LLMMaps: A strategy for stratified evaluation, systematically breaking down performance across different dimensions to understand model strengths and weaknesses.
• Benchmarking and Leaderboards: Providing a competitive framework for LLM evaluation, promoting transparency and continual improvement.
• Stratified Analysis: Offering deep insights by dissecting performance into finer granularities.
• Visualization of Bloom's Taxonomy: Mapping LLM outputs to cognitive levels in Bloom's Taxonomy to assess the distribution of cognitive capabilities.
• Hallucination Score: Quantifying inaccuracies and false information generated by LLMs.
• Knowledge Stratification Strategy: Evaluating the hierarchical knowledge and structured reasoning capabilities of LLMs.
• Machine Learning Models for Hierarchy Generation: Using machine learning to create and analyze hierarchical structures within LLMs' knowledge base.

Crucially, the paper emphasizes the role of Human Evaluation in capturing subtleties and nuances that automated metrics might overlook. Human feedback is utilized to provide a comprehensive understanding of the model's practical applicability and trustworthiness.

The presented framework is designed to enhance transparency, guide the development of more robust and fair LLMs, and establish user trust. The authors also mention ongoing and future work focusing on better visualizing these evaluation metrics and applying their techniques to practical examples to demonstrate effectiveness. This approach aims to bridge the gap between raw performance metrics and real-world trust and usability of LLMs.