Exploring How LLMs Capture and Represent Domain-Specific Knowledge

We study whether Large Language Models (LLMs) inherently capture domain-specific nuances in natural language. Our experiments probe the domain sensitivity of LLMs by examining their ability to distinguish queries from different domains using hidden states generated during the prefill phase. We reveal latent domain-related trajectories that indicate the model's internal recognition of query domains. We also study the robustness of these domain representations to variations in prompt styles and sources. Our approach leverages these representations for model selection, mapping the LLM that best matches the domain trace of the input query (i.e., the model with the highest performance on similar traces). Our findings show that LLMs can differentiate queries for related domains, and that the fine-tuned model is not always the most accurate. Unlike previous work, our interpretations apply to both closed and open-ended generative tasks. Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, yet the internal mechanisms driving these capabilities remain poorly understood. Different domains require distinct knowledge and reasoning patterns, necessitating LLMs to adjust decision-making based on-the-fly for input queries. This is crucial for applications demanding high reliability, such as legal and medical fields, where errors can lead to significant consequences. The research question of how LLMs adapt their decision-making and reasoning patterns across different domains is distinct from a growing body of work on locating factual associations from language models behavior (Meng et al., 2024; Hernandez et al., 2024a;b; Mitchell et al., 2022; Meng et al., 2023; Dai et al., 2022; Belrose et al., 2023). While these studies aim to identify the modules and computations that recall specific facts, primarily monitoring and controlling language generation, they often fall short in addressing the complexities of generative tasks. Understanding how LLMs adapt their reasoning across generative tasks is important for enhancing transparency in their decision-making processes.