Batch Universal Prediction
This work provides a theoretical framework for assessing LLM performance, but it is incremental as it adapts existing regret concepts to a batch setting.
The paper tackled the problem of evaluating large language models (LLMs) from a universal prediction perspective by introducing batch regret as a modified metric, and it derived asymptotic results for add-constant predictors on memoryless and first-order Markov sources.
Large language models (LLMs) have recently gained much popularity due to their surprising ability at generating human-like English sentences. LLMs are essentially predictors, estimating the probability of a sequence of words given the past. Therefore, it is natural to evaluate their performance from a universal prediction perspective. In order to do that fairly, we introduce the notion of batch regret as a modification of the classical average regret, and we study its asymptotical value for add-constant predictors, in the case of memoryless sources and first-order Markov sources.