Meta Semantic Template for Evaluation of Large Language Models
This addresses the need for robust evaluation methods in the LLM community to assess semantic understanding, though it is an incremental contribution focused on evaluation techniques.
The paper tackles the problem of evaluating whether large language models (LLMs) genuinely understand semantics or merely memorize data by proposing MSTemp, an approach that generates out-of-distribution evaluation sets using existing datasets as seeds, with initial experiments showing it significantly reduces LLM performance.
Do large language models (LLMs) genuinely understand the semantics of the language, or just memorize the training data? The recent concern on potential data contamination of LLMs has raised awareness of the community to conduct research on LLMs evaluation. In this paper, we propose MSTemp, an approach that creates meta semantic templates to evaluate the semantic understanding ability of LLMs. The core of MSTemp is not to perform evaluation directly on existing benchmark datasets, but to generate new out-of-distribution (OOD) evaluation sets using existing datasets as seeds. Specifically, for a given sentence, MSTemp leverages another language model to generate new samples while preserving its semantics. The new samples are called semantic templates to the original sentence. Then, MSTemp generates evaluation samples via sentence parsing and random word replacement on the semantic templates. MSTemp is highly flexible, dynamic, and cost-effective. Our initial experiments show that MSTemp-generated samples can significantly reduce the performance of LLMs using existing datasets as seeds. We hope this initial work can shed light on future research of LLMs evaluation.