RankRAG instruction-tunes a single large language model to perform both context ranking and answer generation for retrieval-augmented generation (RAG).
Notes on verification
Confirmed by the original arXiv paper, NeurIPS proceedings, and OpenReview listing, all describing the same method in nearly identical language. [tier=gold indep_score=0.867 clusters=3 claim_tier=notable]
Sources
- unifying context ranking with retrieval-augmented generation in LLMs (seed:technology_and_ai)
- https://arxiv.org/abs/2407.02485 (corroboration)
- https://papers.nips.cc/paper_files/paper/2024/hash/db93ccb6cf392f352570dd5af0a223d3-Abstract-Conference.html (corroboration)
- https://openreview.net/forum?id=S1fc92uemC (corroboration)