Large Language Models (LLMs) and fine-tuning techniques are often misunderstood in their application to specific question-answering tasks. While fine-tuning can improve model performance, it does not inherently enable a model to provide predetermined answers to specific questions. Instead, a more effective approach for retrieving precise information involves semantic search using embeddings technique.

Fine-tuning is not about answering a specific question with a specific answer from the fine-tuning dataset. In other words, a fine-tuned model doesn’t know what answer it should give for a given question. It can’t read your mind. You’ll get an answer based on all the knowledge a fine-tuned model has, where: knowledge of a fine-tuned model = default knowledge (i.e., knowledge that the model had before the fine-tuning) + fine-tuning knowledge (i.e., knowledge that you added to the model with the fine-tuning).

Semantic search utilizes embedding vectors to represent text as numerical data. These vectors, typically consisting of hundreds or thousands of dimensions, allow for the comparison of textual content based on semantic similarity. The process involves creating embeddings for a dataset of facts or information, as well as for user queries. By comparing these embeddings using metrics such as cosine similarity, the system can identify the most relevant information.

The implementation of such a system involves several key steps: preparing a dataset of facts, generating embeddings for each fact, creating embeddings for user queries, and comparing these embeddings to find the most similar content. If the similarity exceeds a predetermined threshold, the system provides the corresponding fact as an answer. Otherwise, it may default to a more general response from the LLM.

This approach offers a balance between providing specific, factual information when available and leveraging the broader knowledge of an LLM when necessary. It enables the creation of more accurate and controllable question-answering systems, particularly useful in scenarios requiring access to specific, curated information.