To enhance the instruction-tuning process for generating more relevant synthetic queries, several strategies can be employed:
Diverse Instruction Sets: Expanding the pool of instructions used during the tuning phase can lead to a broader range of query types. By incorporating various instruction templates that target different aspects of the corpus, such as context extraction, sentiment analysis, or specific domain-related queries, the model can learn to generate more nuanced and contextually relevant queries.
Feedback Mechanisms: Implementing a feedback loop where the generated queries are evaluated against a set of relevance criteria can help refine the instruction-tuning process. This could involve human-in-the-loop evaluations or automated metrics that assess the quality and relevance of the generated queries, allowing for iterative improvements.
Contextual Embedding Utilization: Leveraging contextual embeddings from the encoder-decoder model during the instruction-tuning phase can provide richer semantic information. By conditioning the query generation on the embeddings of the corpus, the model can produce queries that are more closely aligned with the content and intent of the documents.
Fine-tuning with Domain-Specific Data: If available, incorporating domain-specific datasets during the instruction-tuning phase can significantly improve the relevance of the synthetic queries. This targeted fine-tuning can help the model better understand the nuances and specificities of the domain, leading to more accurate query generation.
Multi-Task Learning: Integrating multi-task learning approaches where the model is trained on related tasks (e.g., summarization, classification) alongside query generation can enhance its overall understanding and ability to generate relevant queries. This approach allows the model to leverage shared knowledge across tasks, improving its performance in generating synthetic queries.