RAG.
Fine-tuning Embedding Models
After getting the proper size of Chunks, we need to Em-
bedding the chunks and query in the semantic space by an
Embedding model, so it is crucial whether Embedding can
represent the corpus effectively. Nowadays, excellent Em-
bedding models have appeared, such as [UAE[AngIE, 2023],
Voyage[VoyageAI, 2023], BGE[BAAI, 2023], etc.],
they
have been pre-trained on large-scale corpus, but they may
not accurately represent domain-specific corpus information
when applied to specific domains. Furthermore, task-specific
fine-tuning of Embedding models is critical to ensure that
the model understands the user query in relation to the con-
tent relevance, whereas an un-fine-tuned model may not be
able to fulfill the needs of a specific task. Thus, fine-tuning
an Embedding model is essential for downstream applica-
tions. There are two basic paradigms in Embedding fine-
tuning methods
Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey