Large Language Models (LLMs) have revolutionized how we interact with machines, enabling us to converse, generate creative text formats, and even translate languages with impressive fluency. However, under the surface of these impressive capabilities lies a critical challenge for current LLMs: effectively handling questions within specialized domains like medicine, law, or technical support. These domains demand not only general language understanding but also the ability to process intricate details and access specific knowledge not typically found in open-domain training data. In this exploration, we dive into the research paper "Exploring Language Model Generalization in Low-Resource Extractive QA" to uncover why this performance gap exists. Specifically, the research delves into the field of Extractive Question Answering (EQA), where the AI's task is to pinpoint the precise text span within a given document that directly answers a question. This task is particularly crucial in closed domains like medicine or law, where the accuracy and reliability of answers are paramount. Surprisingly, even LLMs specifically trained for EQA stumble when faced with closed-domain questions. One key finding reveals that LLMs struggle to generate the longer, more nuanced answer spans often required in these specialized fields. They tend to overfit to the shorter, factoid-style answers prevalent in their training data, limiting their ability to provide comprehensive responses to complex questions. Another issue lies in understanding polysemy—words with multiple meanings. LLMs can struggle to discern the appropriate meaning of a term based on its context within a specific domain. For example, the word “bank” has different meanings in finance and geography, and current LLMs often fail to distinguish these subtle differences in specialized domains. Moreover, simply scaling up the size of LLM models doesn't necessarily solve this problem. The research indicates that careful pre-processing of training data, such as whole-word masking rather than piecemeal masking, can significantly impact an LLM's ability to detect and comprehend technical terms. Further research into specialized tokenization techniques might hold the key to finer-grained language understanding. From the dataset perspective, the analysis emphasizes the difference between closed and open-domain datasets. The research explored quantitative measures using techniques like the Force-Directed Algorithm and text/task embeddings. These analyses revealed the inherent differences in structure, wording, and average question/answer lengths between datasets like SQuAD (a general knowledge dataset) and those focused on legal documents (CUAD) or technical support (TechQA). This underscores the need for targeted training datasets that reflect the specific linguistic characteristics of each closed domain. In conclusion, while LLMs are adept at tackling open-domain questions, they face a significant hurdle when venturing into specialized fields. This research pinpoints the limitations of existing architectures and pre-processing techniques, paving the way for future model enhancements that address the unique challenges of closed-domain question answering. The findings suggest that refining training methodologies, developing domain-specific datasets, and focusing on enhanced sense detection could unlock the true potential of LLMs to reliably answer complex questions across various fields of knowledge.