LLM: Evaluation of Pretrained Language Models

There are usually several ways to evaluate the pros and cons of pre-trained language models:

1. Perplexity: Perplexity is a commonly used method for evaluating language models, which can be used to measure the predictive ability of the model for new data. The lower the perplexity, the better the model fits the data.

2. Language model downstream tasks: Language model downstream tasks refer to the use of pre-trained language models for fine-tuning on a specific task in order to better adapt to the task. Generally, if a pre-trained language model performs well on downstream tasks, it indicates that the model has good generalization ability and language understanding ability.

3. Human evaluation: Human evaluation refers to whether the text generated by the pre-trained language model meets the requirements of grammar, logic and semantics through manual judgment. Although this method is time-consuming and laborious, it can provide more objective evaluation results.

4. Adversarial example attack: Adversarial example attack refers to modifying the input of the pre-trained language model to make it output wrong or misleading results. By adversarial sample attack, the robustness and security of the model can be evaluated.

5. Diversity and consistency: Diversity and consistency refer to whether the pre-trained language model is sufficiently creative and consistent when generating text. If the text generated by the model is too monotonous or inconsistent, it may affect its application value.

6. Training efficiency and storage space: In addition to the above aspects, evaluating the pros and cons of a pre-trained language model also needs to consider factors such as its training efficiency and storage space. Generally speaking, the smaller the training efficiency and storage space, the more practical the model is