Improving Topic Relevance Model by Mix-structured Summarization and LLM-based Data Augmentation

arXiv:2404.02616v1 Announce Type: cross
Abstract: Topic relevance between query and document is a very important part of social search, which can evaluate the degree of matching between document and user's requirement. In most social search scenarios such as Dianping, modeling search relevance always faces two challenges. One is that many documents in social search are very long and have much redundant information. The other is that the training data for search relevance model is difficult to get, especially for multi-classification …

abstract arxiv augmentation challenges cs.cl cs.ir data document improving llm modeling part query search social summarization type