LLM Evaluation Guide

Large Language Model (LLM) is the industry buzz word in recent years. It can understand human language and plays crucial roles in applications like chatbots, translations, and content creation.

Evaluating LLMs is vital to ensure they produce accurate, relevant, and reliable outputs while minimizing biases and errors. Effective evaluation helps identify the strengths and weaknesses of these models, ensuring they perform well in real-world scenarios. Key metrics include BLEU and ROUGE for text quality, BERTScore and MoverScore for semantic similarity, and QuestEval for relevance and completeness. Proper evaluation guarantees that LLMs meet high standards and user expectations. Here are few dimensions on which LLMs can be evaluated.

- Evaluating Generated Text Quality

- Evaluating Semantic Similarity

- Evaluating Factual Consistency

- Evaluating Relevance and Completeness

- Detecting Hallucinations

- Evaluating User Preferences

- No References Available

What other dimension and metric do you use?