Related papers: Augmented Relevance Datasets with Fine-Tuned Small LLMs

Augmented Relevance Datasets with Fine-Tuned Small LLMs

URL: http://arxiv.org/abs/2504.09816v1
Date: Mon, 14 Apr 2025 02:35:00 GMT
Title: Augmented Relevance Datasets with Fine-Tuned Small LLMs
Authors: Quentin Fitte-Rey, Matyas Amrouche, Romain Deveaud,
Abstract summary: This paper explores the use of small, fine-tuned large language models (LLMs) to automate relevance assessment.<n>We fine-tuned small LLMs to enhance relevance assessments, thereby improving dataset creation quality for downstream ranking model training.
Score: 0.7022492404644501
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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