Semantic-Eval : A Semantic Comprehension Evaluation Framework for Large Language Models Generation without Training

With the increasing prominence of large language models (LLMs), evaluating their textgeneration capabilities has become an essential research challenge. Although LLM-based evaluation methods exhibit robust performance, the inherent stochastic nature of the LLM generation process introduces a degree of uncertainty in alignment with human preferences. To address this limitation, we propose Semantic-Eval, the first training-free framework designed to assess LLM-generated text based on semantic understanding. This framework computes semantic similarity between pairwise texts to evaluate the interdependence of semantic units, integrating a graph-based weighting mechanism to account for the differential contributions of individual sentences. A pre-trained natural language inference (NLI) model is also incorporated to mitigate potential semantic relationship biases. We evaluate Semantic-Eval across eight datasets that encompass four common NLP tasks. The experimental results indicate that Semantic-Eval surpasses traditional N-gram and BERT-based evaluation metrics, aligning more closely with human judgments and demonstrating a higher correlation than smaller LLMs. However, it slightly lags behind GPT-4. Finally, we demonstrate the effectiveness of Semantic-Eval in evaluating the generation quality of 13 large language models. The code is publicly available at 1 .