Knowledge Graphs & LLMs: Fine-Tuning Vs. Retrieval-Augmented Generation

Knowledge Graphs & LLMs: Fine-Tuning Vs. Retrieval-Augmented Generation

Large language models (LLMs) have become increasingly popular due to their ability to generate coherent and contextually relevant text. However, there are still limitations to these models, such as knowledge cutoffs and hallucinations. In this article, we will explore two different approaches to overcome these limitations: fine-tuning and retrieval-augmented generation.

Fine-tuning an LLM involves updating and expanding its internal knowledge through a supervised training phase using question-answer pairs. This approach is useful for slowly changing datasets and allows for some degree of hallucinations. However, it does not completely eliminate the knowledge cutoff problem, as it merely pushes the cutoff to a later date. Additionally, LLMs do not cite their sources when providing answers, making it difficult to determine the reliability of the information. Furthermore, fine-tuned models cannot provide different responses based on the user or have access restrictions, meaning that anyone interacting with the LLM has access to all its information.

On the other hand, retrieval-augmented generation addresses these limitations by using the LLM as a natural language interface to access external information. Instead of relying solely on the internal knowledge of the LLM, relevant documents are provided to generate an answer. This approach offers several advantages over fine-tuning. Firstly, the answer can cite its sources, allowing for validation and potential updates to the underlying information. Secondly, hallucinations are less likely to occur since the LLM relies only on information provided in the relevant documents. Thirdly, changing and updating the underlying information is easier as it shifts the problem from LLM maintenance to database maintenance and context construction. Lastly, answers can be personalized based on user context or access permissions.

While fine-tuning an LLM has its benefits, the retrieval-augmented approach offers a more robust solution for addressing the limitations of LLMs. By leveraging external information and intelligent search tools, this approach provides more reliable and customizable answers. However, it does require access to the user's knowledge base and relies on the effectiveness of the search tool to retrieve the relevant information. Additionally, it can only answer queries if it has the necessary information to address the question.

In a recent study, researchers discovered that LLMs use a surprisingly simple mechanism to retrieve stored knowledge. They found that these models decode relational information using a linear function specific to the type of fact being retrieved. This insight opens up possibilities for probing the model to identify its knowledge about new subjects and where that knowledge is stored. Even when an LLM answers a prompt incorrectly, it often still retains the correct information, which can be valuable for correcting falsehoods and improving the model's accuracy.

In conclusion, while fine-tuning and retrieval-augmented generation offer different approaches to enhancing LLMs, the latter proves to be more advantageous in terms of source-citing, reduced hallucinations, easier information maintenance, and personalization. However, it is crucial to consider the limitations of relying on external information and the need for an effective search tool. Before implementing either approach, it is essential to assess the specific requirements and constraints of the task at hand.

Actionable Advice:

1. For slowly changing datasets where some hallucinations are permissible, consider fine-tuning an LLM. However, be aware that it does not fully solve the knowledge cutoff problem.

2. If reliability, source-citation, and personalization are crucial, opt for retrieval-augmented generation. Ensure you have a robust search tool and access to the necessary knowledge base.

3. To improve the accuracy and reliability of LLMs, consider using the insights from the study on linear functions for retrieving stored facts. This approach can help identify and correct falsehoods within the model.

As the field of LLMs continues to evolve, it is essential to explore both fine-tuning and retrieval-augmented generation approaches and determine the best fit for specific use cases. By understanding the strengths and limitations of each approach, we can harness the full potential of LLMs while mitigating their inherent challenges.