“It is a fundamental challenge to evaluate whether a model can truly capture the meaning ofsentences. Evaluation of whether a model well captures the meaning of individual words, how-ever, can be effectively achieved by analyzing whether the model encodes words in a vectorspace where semantically similar words form clusters. Inspired by this approach, we propose theSentence-Space Metrics (SSM) to evaluate model interpretation of sentences, and the sentencespace is constructed based on the pairwise entailment relationships between all sentence pairswithin a sentence pool. We use three metrics to evaluate a sentence space, i.e., (1) sparsity, (2)clustering of related sentences, and (3) similarity with the sentence space measured from hu-mans. The SSM is applied to evaluate 20 models, including ChatGPT, 18 BERT-family modelsfine-tuned for Natural Language Inference (NLI) task, as well as SimCSE, a sentence representa-tion model. The SSM reveals dramatic differences among models: Although all models achievehigh accuracy on standard NLI datasets such as MNLI, none of them mirrors the human behaviorunder the SSM. These results demonstrate that, compared with traditional accuracy measures,the SSM considers pairwise relationships between hundreds of sentences and therefore providea more fine-grained evaluation of model interpretation of sentences.Introduction”

Large language models possess remarkable capacity for processing language, but it remains unclear whether these models can further generate creative content. The present study aims to investigate the creative thinking of large language models through a cognitive perspective. We utilize the divergent association task (DAT), an objective measurement of creativity that asks models to generate unrelated words and calculates the semantic distance between them. We compare the results across different models and decoding strategies. Our findings indicate that: (1) When using the greedy search strategy, GPT-4 outperforms 96% of humans, while GPT-3.5-turbo exceeds the average human level. (2) Stochastic sampling and temperature scaling are effective to obtain higher DAT scores for models except GPT-4, but face a trade-off between creativity and stability. These results imply that advanced large language models have divergent semantic associations, which is a fundamental process underlying creativity.