Evaluating the Adaptability of Large Language Models for Knowledge-aware Question and Answering

Abstract

Abstract Large language models (LLMs) have transformed open-domain abstractive summarization, delivering coherent and precise summaries. However, their adaptability to user knowledge levels is largely unexplored. This study investigates LLMs’ efficacy in tailoring summaries to user familiarity. We assess various LLM architectures across different familiarity settings using metrics like linguistic complexity and reading grade levels. Findings expose current capabilities and constraints in knowledge-aware summarization, paving the way for personalized systems. We analyze LLM performance across three familiarity levels: none, basic awareness, and complete familiarity. Utilizing established readability metrics, we gauge summary complexity. Results indicate LLMs can adjust summaries to some extent based on user familiarity. Yet, challenges persist in accurately assessing user knowledge and crafting informative, comprehensible summaries. We highlight areas for enhancement, including improved user knowledge modeling and domain-specific integration. This research informs the advancement of adaptive summarization systems, offering insights for future development.