The Entropy of Large Language Model Output: An Information-Theoretic Perspective

This post explores the output of large language models (LLMs like ChatGPT) from an information-theoretic perspective. The author calculates the entropy of each output token to measure the model's certainty in predicting the next token. Experiments show lower entropy (higher certainty) at sentence endings or word fragments, and higher entropy (more uncertainty) in descriptive statements. A comparison of token types at low vs. high entropy reveals that low entropy often correlates with factual statements (containing proper nouns), while high entropy correlates with descriptive statements. A Tamil language example is used for further illustration, emphasizing the importance of discerning truth from falsehood.