performance in other relation types is comparatively weaker. In prediction tasks, LLMs exhibit
enhanced predictive capabilities for future events with increased contextual information. Riccardi
and Desai [157] explored the semantic proficiency of LLMs and showed that these models perform
poorly in evaluating basic phrases. Furthermore, GPT-3.5 and Bard cannot distinguish between
meaningful and nonsense phrases, consistently classifying highly nonsense phrases as meaningful.
GPT-4 shows significant improvements, but its performance is still significantly lower than that of
humans. In summary, the performance of LLMs in semantic understanding tasks is poor. In the
future, we can start from this aspect and focus on improving its performance on this application.
In social knowledge understanding, Choi et al. [21] evaluated how well models perform
at learning and recognizing concepts of social knowledge and the results revealed that despite
ASurveyonEvaluationofLargeLanguageModels