Research on the Dependence of Large Language Models on Internal Parameterized Knowledge Under Retrieval-Augmented Techniques
Abstract
In recent years, artificial intelligence technologies represented by large language models (LLMs) have achieved breakthrough progress, driving the development of intelligent applications across various domains. However, LLMs still face several challenges in practical applications, including knowledge update latency, hallucination in generated content, and persistent biases that are difficult to eliminate. This paper investigates the decision-making preferences and behavioral patterns of LLMs when inconsistencies arise between their internal parameterized knowledge and externally retrieved information enhanced by retrieval-augmented techniques. We propose a systematic evaluation method for assessing LLMs’ internal knowledge dependency. First, we establish a data augmentation method for knowledge-intensive question-answering tasks to construct an evaluation dataset. Using the spaCy entity categorization tool combined with an LLM-based verification and filtering mechanism, we classify and annotate entities in QA datasets and corpora. Through LLM-integrated entity recognition and substitution techniques, we generate logically consistent transformed facts and corresponding questions on the TriviaQA dataset, automatically constructing a synthetic transformed fact dataset. Next, we integrate retrieval-augmented LLMs and conduct a systematic evaluation of multiple mainstream LLMs. By comparing model performance differences on original versus transformed facts, we quantitatively…
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