Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- Thesis: Train end-to-end retrieval and generator that would provide more specific, diverse, and factual answers. This helps reduce hallucination, continuously update the non-parametric memory of the retriever by just indexing new documents.
- Method(s): Train end-to-end query encoder and BART (generator) on question/answer pairs. Document encoder is initialized (from DPR) and not trained.
- Contribution: End-to-end training of RAG model with generation instead of extraction for answers.
- Takeaways:
- Having up to date memory indexed by document encoder reduces hallucination and overcome cutoff knowledge of generator
- End-to-end training of query encoder and generator
- Generate more specific, diverse, and factual information
- Improvements:
- No interaction between query and document
- Document encoder is not updated and may not be optimal
- Notes:
- Fine-tune only query encoder. No updates for document encoder because it is very expensive
- Retriever is DPR bi-encoder using BERT-base uncased. Index is populated before training using document encoder
- Generator is BART
- Question and top-k documents are concatenated and passed to generator. BART conditions on them and start generating tokens
- Index can be updated independently
#nlp #rag
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