Knowledgeable LLMs: Hybrid Search Approaches

Keyword-based search with LLM-guided ranking: In this approach, the search engine initially uses a traditional keyword-based search to identify potentially relevant documents. The search results are then re-ranked using an LLM-based model that takes into account the context of the query and the content of the documents.

Embedding-based search with keyword filtering: In this approach, the search engine uses pre-trained word embeddings to represent both the query and the documents. The query and documents are then compared using cosine similarity, and the top results are returned. To improve the quality of the results, the search engine can use keyword filtering to exclude documents that do not contain any of the keywords in the query.

Language modeling approach: In this approach, the search engine models the probability of a document given a query, as opposed to the relevance of a document to a query. The ranking of documents is determined by how well they fit the query language model. This approach has been shown to be effective for queries with few keywords and for queries that require some level of understanding of the query intent.

Hybrid search with ensemble models: In this approach, multiple search models are trained on different types of data and combined using an ensemble method, such as stacking or blending. For example, one model could be trained on embeddings, another on keywords, and a third on LLM-guided ranking. The ensemble model can then take the output of each individual model as input and generate a final ranking of the search results.

Context-aware search with user feedback: In this approach, the search engine uses LLMs to identify the user's intent and context, and then adapts the search results accordingly. The user can provide feedback on the relevance of the results, and the search engine can use this feedback to refine the models over time.