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AI Research
arXiv CS.AI·Jun 9·5 min read
Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments
TL;DR
Search systems struggle with a lack of expert-generated textual relevance labels compared to behavioral relevance labels. To address this, a method was developed that uses large language models (LLMs) to generate textual relevance judgments.
✦ Why It Matters
Engineers can leverage LLMs to enhance search relevance in applications with limited expert labeling resources.
Key Takeaways
- Utilize fine-tuned LLMs for generating relevance labels.
- Focus on improving textual relevance alongside behavioral metrics.
- Conduct A/B tests to validate changes in ranking systems.
- Target tail queries where behavioral data is limited.
How It Works
The study leverages a fine-tuned LLM to generate textual relevance labels, which are then used to enhance the ranking algorithm. By systematically evaluating different LLM configurations, the researchers identified the optimal model that provided the most relevant labels, thus addressing the scarcity of expert annotations.