Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs
(2312.05934)Abstract
LLMs encapsulate a vast amount of factual information within their pre-trained weights, as evidenced by their ability to answer diverse questions across different domains. However, this knowledge is inherently limited, relying heavily on the characteristics of the training data. Consequently, using external datasets to incorporate new information or refine the capabilities of LLMs on previously seen information poses a significant challenge. In this study, we compare two common approaches: unsupervised fine-tuning and retrieval-augmented generation (RAG). We evaluate both approaches on a variety of knowledge-intensive tasks across different topics. Our findings reveal that while unsupervised fine-tuning offers some improvement, RAG consistently outperforms it, both for existing knowledge encountered during training and entirely new knowledge. Moreover, we find that LLMs struggle to learn new factual information through unsupervised fine-tuning, and that exposing them to numerous variations of the same fact during training could alleviate this problem.
Overview
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This paper compares fine-tuning and retrieval-augmented generation (RAG) as methods of knowledge injection into LLMs.
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The study evaluates the effectiveness of these methods across various knowledge-intensive tasks.
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RAG allows models to access new information beyond the training data and outperforms fine-tuning in augmenting an LLM’s knowledge base.
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The research highlights RAG's potential to inject new knowledge without impacting other model capabilities, like avoiding catastrophic forgetting.
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Future research directions include combining knowledge injection techniques and improving our understanding of knowledge representation in LLMs.
Introduction
LLMs are capable of capturing extensive factual knowledge due to their comprehensive pre-training datasets. Nonetheless, their knowledge is both static and non-specific, presenting potential limitations for domain-specific applications. Addressing these limitations involves boosting the model's knowledge base, a process referred to as knowledge injection. This study explores the efficacy of two prominent methods for knowledge injection into LLMs: fine-tuning and retrieval-augmented generation (RAG), evaluating their performance across various knowledge-intensive tasks.
Understanding Knowledge Injection
Knowledge for LLMs can be quantified by their performance on factual question-answering tasks. A model is said to possess knowledge about a set of questions if it can consistently outperform random guessing. The study distinguishes between knowledge previously encountered during training and entirely new facts. Injecting knowledge into LLMs is essential for two main reasons: enhancing their expert knowledge within a domain and updating their factual information base.
Comparative Analysis of Fine-Tuning and RAG
Fine-tuning involves adapting a pre-trained model on task-specific data, while RAG employs retrieval methods to supplement a model's responses with information from a knowledge source. The study utilized both methods to determine which more effectively augments an LLM’s knowledge base. RAG, especially, enables a model to access novel information that was not part of the original training data, which is pivotal for staying current with latest knowledge.
Key Findings and Future Directions
The study demonstrates that while fine-tuning does offer some improvement in model knowledge, RAG consistently outperforms fine-tuning on a variety of tasks. It is more reliable for injecting new information into LLMs and, unlike fine-tuning, does not affect other capabilities of the model, such as catastrophic forgetting. One limitation observed was the variability in choosing the number of documents to retrieve in RAG, indicating a need for further research in this area. This investigation could benefit from exploring combinations of knowledge injection techniques, including supervised and reinforcement learning-based methods. Additional studies could help in understanding knowledge representation in LLMs and contribute to advancing the field of AI.
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