Document Re-ranking using LLMs (Paper Summary)

Document Re-ranking is an important addition to RAG pipelines. The absence of a domain-specific Ranker can potentially diminish the quality of your initial ranked outputs. Additionally, obtaining sufficient training data for your ranker can be challenging in many cases.


This paper from Google Research proposes a Pairwise Ranking Prompting (PRP) paradigm, which uses the query and a pair of documents as the prompt for LLMs to perform ranking tasks optimized to O(n) time 🤯

They also show that PRP based …

cases data document domain google google research languagetechnology llms paper paradigm pipelines prompting quality rag ranking research summary training training data