If you want to weight how much retrieved items influence the LLM response, there are several strategies you can use. The idea is to control how much importance each retrieved document has in the final answer. Here’s how you can do it:
Before passing retrieved documents into the LLM, you can modify their presence or formatting based on importance:
- If an item is more important, include it multiple times in the context to reinforce it.
context = "\n\n".join([doc.text * int(doc.weight * 10) for doc in retrieved_docs])
- More important documents appear multiple times, making them more likely to influence the response.
- Highlight more important items explicitly:
- Prefix key information with “IMPORTANT:”
- Use bold or uppercase text
- Place important content at the start of the context
f"IMPORTANT: {doc.text}" if doc.weight > 0.8 else doc.text
for doc in retrieved_docs
- LLMs pay more attention to the beginning of the context, so putting high-weight items first increases their influence.
- Dynamically allocate tokens based on weight.
- If your LLM has a context limit, allow high-weight documents to take up more tokens.
Example:
total_weight = sum(doc.weight for doc in retrieved_docs)
(doc.text[:int(doc.weight / total_weight * max_tokens)])
for doc in retrieved_docs
context = "\n\n".join(allocated_context)
- This ensures that high-weight documents get a larger share of the context window.
Instead of modifying documents, adjust how the prompt guides the LLM:
Use the following sources to answer the question.
- Important sources: {high_weight_docs}
- Other sources: {low_weight_docs}
Prioritize important sources when responding.
- This method tells the LLM to focus more on high-weight documents.
- If using an open-weight LLM (e.g., Llama, Mistral), fine-tune it to recognize metadata like “importance score” and adjust its response accordingly.
- Example fine-tuning data:
Context: Important: X. Less important: Y.
User: What is the best approach?
LLM: X is the best approach because it is more reliable.
- A lightweight LoRA adapter can also help if full fine-tuning is impractical.
Instead of including everything in the prompt, choose how much to pass based on weight.
- Only pass the top N highest-weighted documents if context size is limited.
- Use a separate re-ranker (e.g., ColBERT, GPT-4-turbo) to decide which retrieved results to prioritize.