The field of natural language processing is moving towards a deeper understanding of the limitations and challenges of large language models (LLMs). Recent studies have highlighted the issues of natural context drift, prompt sensitivity, and robustness to linguistic variability, which can significantly impact the performance of LLMs. The development of more robust evaluation methodologies is crucial to accurately assess the capabilities of LLMs in real-world applications. Noteworthy papers in this area include:

• A study that found natural text evolution poses a significant challenge to the language understanding capabilities of LLMs, with performance declining as reading passages diverge from the versions encountered during pretraining.
• Research that suggested prompt sensitivity may be more an artifact of evaluation than a flaw in the models, and that modern LLMs are more robust to prompt templates than previously believed.
• A framework for evaluating prompt sensitivity in large multimodal models, which revealed that proprietary models exhibit greater sensitivity to prompt phrasing, while open-source models are steadier but struggle with nuanced and complex phrasing.
• An investigation into the robustness of LLMs to paraphrased benchmark questions, which found that while LLM rankings remain relatively stable, absolute effectiveness scores change and decline significantly, raising concerns about their generalization abilities and evaluation methodologies.