The field of natural language processing is moving towards a deeper understanding of language model interpretability and generalization. Recent research has focused on developing new methods to assess model performance, such as circuit stability, which refers to a model's ability to apply a consistent reasoning process across various inputs. This has led to a better understanding of how models generalize to new tasks and datasets. Another area of research has been on improving model performance through techniques such as grammar prompting, which has shown promising results in enhancing grammatical acceptability judgments. Furthermore, there has been a growing interest in evaluating language models from a cognitive perspective, with a focus on understanding the comprehension process of large language models. Noteworthy papers include: Circuit Stability Characterizes Language Model Generalization, which introduces a new method to assess model performance. Explain-then-Process: Using Grammar Prompting to Enhance Grammatical Acceptability Judgments, which presents a novel approach to improving model performance. SCOP: Evaluating the Comprehension Process of Large Language Models from a Cognitive View, which provides a systematic framework for evaluating the comprehension process of large language models.