Large Language Models (LLMs) like Llama or Qwen have shown incredible promise in answering questions based on provided context. Traditionally, many systems rely on an initial retrieval step—searching through documents based on the question, then augmenting the prompt with the most relevant excerpts for the LLM to summarize a final answer. But what if you want to fine-tune your LLM to answer questions without this initial retrieval step? In this post, we explore the process and considerations for creating a training dataset to enable your model to internalize document content and answer questions in a “closed-book” fashion.
1. Defining the Task
Before diving into the dataset design, it’s critical to articulate exactly what your goal is:
- Closed-Book QA: You want the model to internalize facts and answer questions based solely on the training it has received, without using external search.
- Simulated Retrieval: The model learns to extract and synthesize answers from internalized representations instead of performing an explicit retrieval.
Understanding these nuances helps shape the style and format of your training data.
2. Curating or Generating Training Data
You might think you need examples for every possible question related to your documents. Fortunately, that’s not the case. A well-designed, diverse dataset capturing the main topics, concepts, and entities of your documents can suffice.
- Representative Examples: Focus on creating a set of question–answer pairs that covers the key content areas.
- Contextual Prompts: You can incorporate context directly into the training examples. For instance, structure prompts as:
“Given the following text, answer the question:” followed by the article excerpt and then the question. This style reinforces the connection between the document content and the answer.
3. Strategies for Data Construction
There are several approaches you can take to create or assemble your training dataset:
a. Manual Curation
- Expert Involvement: Domain experts can write questions and answers directly from the documents.
- High-Quality Data: Manual curation ensures precise alignment between questions and content—ideal for niche or technical areas.
- Scalability: The downside is that manual curation may not scale well if you have a vast amount of content.
b. Synthetic Data Generation
- Utilize Existing LLMs: Prompt an LLM to generate questions from your documents, e.g., “Generate questions that someone might ask about this text.”
- Quality Control: It’s important to review or filter these questions to ensure they are accurate and relevant.
- Diverse Dataset: This approach broadens the question distribution and introduces variety.
c. Hybrid Approach
- Combine Both Methods: Begin with machine-generated examples and have them reviewed or refined by experts.
- Balanced Outcome: This strategy offers a trade-off between scale and data quality.
4. Data Formatting for Fine-Tuning
When designing your examples, consider the following formatting tips:
- Instructional Cues: Incorporate clear instructions such as:
“Answer the following question based on your internal knowledge of the documents.”
This helps the model understand its role. - Contextual Embedding: Optionally include key document excerpts directly in the prompt during training. This allows the model to “absorb” detailed information it can later recall without an external search.
5. Managing Input Length and Context
- Chunking Long Documents: Break large documents into manageable pieces. Create separate training examples for each chunk while ensuring critical links between sections remain intact.
- Summaries vs. Detailed Text: For complex domains, consider training on summaries or extracted key points, depending on how detailed you want the responses to be.
6. Addressing the “All Possible Questions” Conundrum
Rather than covering every conceivable query:
- Emphasize Diversity: Focus on capturing a diverse set of scenarios that reflect the main facets of your content.
- Data Augmentation: Use paraphrasing, reordering, and slight rewording of questions to help the model generalize to unseen queries.
7. Evaluation and Iteration
- Holdout Set: Always keep a separate set of question–answer pairs for validation.
- Iterative Improvement: Use gap analysis on the holdout data to identify areas where the model struggles, and augment your dataset accordingly.
8. Considerations and Trade-Offs
- Model Capacity: Closed-book QA places the burden on the model’s memory. If your documents are vast and detailed, even a highly fine-tuned model might struggle to recall every detail.
- Update Mechanism: Unlike retrieval-based systems where updating content only means refreshing the index, any change in the dataset may require additional rounds of fine-tuning.
Conclusion
Transitioning to a closed-book QA system by fine-tuning an LLM involves a thoughtful approach to dataset design. By curating or generating a diverse and high-quality dataset, formatting your examples appropriately, and iteratively refining your approach, you can shift knowledge retrieval from runtime search to internalized expertise within the model’s weights.
By carefully balancing quality, diversity, and scalability, you can build a fine-tuned model that answers questions accurately—without needing to search for context every time.
