แนวคิดหลัก
Improving topic relevance modeling by using mix-structured summarization as document input and leveraging large language models for data augmentation.
บทคัดย่อ
The paper proposes two key approaches to enhance topic relevance modeling in social search scenarios:
Mix-structured Summarization:
Extracts a query-focused summary and a general document summary without considering the query.
Concatenates the two summaries as the document input to the relevance model.
This allows the model to better differentiate between strong relevance (where the document is predominantly about the query) and weak relevance (where the document only contains limited information related to the query).
LLM-based Data Augmentation:
Utilizes the language understanding and generation capabilities of large language models (LLMs) to rewrite queries and generate new queries from documents.
The rewritten and generated queries are paired with the corresponding documents to create new training samples across different relevance categories (strong, weak, irrelevant).
This helps address the challenge of obtaining sufficient and diverse training data for topic relevance modeling.
Offline experiments and online A/B tests show that the proposed approaches can significantly improve the performance of topic relevance modeling in social search scenarios.
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