Augmented Relevance Datasets with Fine-Tuned Small LLMs
This provides a practical solution for search engine optimization by reducing the resource-intensive nature of manual dataset labeling, though it is incremental as it builds on existing LLM fine-tuning methods.
The paper tackled the problem of automating relevance assessment for dataset creation in ranking models by using fine-tuned small LLMs, resulting in substantial improvements in ranking model performance and outperforming certain closed-source models on their dataset.
Building high-quality datasets and labeling query-document relevance are essential yet resource-intensive tasks, requiring detailed guidelines and substantial effort from human annotators. This paper explores the use of small, fine-tuned large language models (LLMs) to automate relevance assessment, with a focus on improving ranking models' performance by augmenting their training dataset. We fine-tuned small LLMs to enhance relevance assessments, thereby improving dataset creation quality for downstream ranking model training. Our experiments demonstrate that these fine-tuned small LLMs not only outperform certain closed source models on our dataset but also lead to substantial improvements in ranking model performance. These results highlight the potential of leveraging small LLMs for efficient and scalable dataset augmentation, providing a practical solution for search engine optimization.