Enhanced Facet Generation with LLM Editing
Joosung Lee Jinhong Kim
In information retrieval, facet identification of a user query is an
important task. If a search service can recognize the facets of a user's query,
it has the potential to offer users a much broader range of search results.
Previous studies can enhance facet prediction by leveraging retrieved documents
and related queries obtained through a search engine. However, there are
challenges in extending it to other applications when a search engine operates
as part of the model. First, search engines are constantly updated. Therefore,
additional information may change during training and test, which may reduce
performance. The second challenge is that public search engines cannot search
for internal documents. Therefore, a separate search system needs to be built
to incorporate documents from private domains within the company. We propose
two strategies that focus on a framework that can predict facets by taking only
queries as input without a search engine. The first strategy is multi-task
learning to predict SERP. By leveraging SERP as a target instead of a source,
the proposed model deeply understands queries without relying on external
modules. The second strategy is to enhance the facets by combining Large
Language Model (LLM) and the small model. Overall performance improves when
small model and LLM are combined rather than facet generation individually.