I have a collection of documents related to a specific industry, and I want to fine-tune an existing LLM to create a chatbot that can handle question-answering, summarization, and text generation based on these documents.
The key requirements are:
The chatbot should generate responses strictly within the domain and avoid answering questions outside its scope.
It should prioritize accuracy by leveraging the provided industry-specific data.
It should support question-answering, summarization, and content generation efficiently.
What are the best practices for fine-tuning an LLM for this use case? Should I consider instruction tuning, retrieval-augmented generation (RAG), or both? Also, how can I effectively restrict responses to ensure the chatbot does not generate hallucinated or out-of-domain answers?
Hi aitude,
If you’re interested in an alternative method to fine-tuning - I have achived this by actually not using fine-tuning. Fine tuning will not prevent hallucination as this is an inherent problem of LLMs. Fine-tuning can help restrict to domain knowledge but at the cost of general knowledge.
It’s also worth considering that the developers of LLMs usually have already spent quite a decent amount of time and budget in the process of fine-tuning their models so they are ready for production and public use across a wide range of use cases. So trying to replicate the same level of quality fine-tuning including both your dataset and testing methods, whilst trying to prevent a breakdown of general knowledge etc may not be worth the budget and time. Just something to consider.
Preservation of general knowledge can potentially help with better reasoning for application to domain specific knowledge by using prompt engineering to focus the LLM on the domain specific criteria. I should also mention, LLMs don’t exactly have knowledge as such but rather probabilities of the most likely next token to generate (whether that be a word, sub-word, letter, sentance, etc based on the model you are using and it’s selected tokeniser). So any knowledge it is trained on does not mean the generated output can be 100% trusted to not hallucinate as it is just a probability.
If you want to minimise on hallucinations, I would try prompt engineering for a system prompt along with RAG. Additionally I would consider another step for validation that includes RAG to further reduce hallucinations.
Overall I think RAG should be where your context for factual ground truth comes from and the LLM should only be relied upon for NLP based processing of the data supplied via RAG. Hence it is useful for the LLM to have a wide range of general knowledge to help it better “understand” how the context should be applied to the real world.
I should also mention, getting RAG right for your use case is very important. This includes various methods used and the models used in the process. Otherwise you can end up with the wrong contextual data or an incomplete context supplied to the prompt which could lead to the LLM making assumptions with incomplete or out of context data applied for the response generation. If your knowledge is complex and nuanced, you may want to even consider methods like GraphRAG which can help with better contextual retrieval on both a global scope and local scope of the data, depending on what is relevant context.