Understanding Large Language Model Behaviors through Interactive Counterfactual Generation and Analysis

Understand

Understanding the behavior of large language models (LLMs) is crucial for ensuring their safe and reliable use.

• However, existing explainable AI (XAI) methods for LLMs primarily rely on word-level explanations, which are often computationally inefficient and misaligned with human reasoning processes.
• Moreover, these methods often treat explanation as a one-time output, overlooking its inherently interactive and iterative nature.
• In this paper, we present LLM Analyzer, an interactive visualization system that addresses these limitations by enabling intuitive and efficient exploration of LLM behaviors through counterfactual analysis.