Large Language Models (LLMs) have revolutionized how we interact with computers, enabling text generation, translation, and more. However, evaluating these complex systems requires a fundamentally different approach than traditional software testing. Here's why:
LLM's Black Box Nature
Traditional software is based on deterministic logic with predictable outputs for given inputs. LLMs, on the other hand, are vast neural networks trained on massive text datasets. Their internal workings are incredibly complex, making it difficult to pinpoint the exact reasoning for any specific output. This "black box" nature poses significant challenges for traditional testing methods.