Batch Universal Prediction

TL;DR: Large language models are good at generating human-like sentences, evaluated using batch regret.

Summary:

• Large language models (LLMs) have gained popularity for generating human-like English sentences.
• LLMs are predictors that estimate the probability of a sequence of words given the past.
• The article introduces the notion of batch regret and studies its asymptotical value for add-constant predictors in memoryless and first-order Markov sources.

Major Findings:

1. LLMs are essentially predictors that estimate the probability of the next words in an online fashion.
2. The article introduces the concept of batch regret as a modification of the classical average regret to evaluate LLMs from a universal prediction perspective.
3. The study focuses on the asymptotical batch regret for add-constant predictors in memoryless and first-order Markov sources.

Analysis and Critique:

• The article provides valuable insights into the evaluation of large language models from a universal prediction perspective.
• However, the article lacks practical examples or applications of the proposed concepts.
• The theoretical nature of the study may limit its immediate applicability in real-world scenarios.
• Further research is needed to validate the proposed concepts in practical settings and to explore their potential impact on language model evaluation.

Appendix

Model	gpt-3.5-turbo-1106
Date Generated	2024-02-26
Abstract	https://arxiv.org/abs/2402.03901v1
HTML	https://browse.arxiv.org/html/2402.03901v1
Truncated	False
Word Count	8083