Adding only a small fraction of ranking data to the training blend enabled instruction-tuned LLMs to outperform existing expert ranking models.
Notes on verification
Directly corroborated by the RankRAG paper's abstract and body (arXiv, NeurIPS proceedings, OpenReview), with consistent wording across multiple independent sources. [tier=gold indep_score=0.838 clusters=4 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://openreview.net/forum?id=S1fc92uemC (corroboration)
- https://proceedings.neurips.cc//paper_files/paper/2024/hash/db93ccb6cf392f352570dd5af0a223d3-Abstract-Conference.html (corroboration)
- https://simg.baai.ac.cn/paperfile/68c487ed-fafd-4bac-81e0-f8be1b56e845.pdf (corroboration)