Transforming LLMs into Efficient Cross-Encoders via Knowledge Distillation for RAG Reranking
Quick Answer
The study presents a method to transform LLaMA 3 (8B) into an efficient reranker for Retrieval-Augmented Generation (RAG) pipelines through knowledge distillation and 4-bit quantization.
Quick Take
This approach achieves a 14% increase in answer relevancy and reduces inference costs, outperforming traditional cross-encoders. The model demonstrates significant improvements in context precision, answer similarity, and correctness, making it suitable for real-time applications.
Key Points
- LLaMA 3 (8B) fine-tuned as a drop-in reranker for pipelines.
- Achieved 14% gain in answer relevancy and 21% in answer correctness.
- Utilized 4-bit quantization to reduce inference overhead.
- Outperformed cross-encoder baseline in a domain-specific benchmark.
- Demonstrates efficiency without quadratic complexity of traditional methods.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Cross-encoders achieve high reranking accuracy in Retrieval-Augmented Generation (RAG) pipelines but impose quadratic inference costs that limit real-time deployment. We address this by fine-tuning LLaMA 3 (8B) as a drop-in reranker using a two-stage pipeline: supervised fine-tuning on a custom query-document relevance dataset via the Unsloth framework with LoRA adapters, followed by 4-bit quantization for efficient inference. The resulting model replaces the cross-encoder in a dual-retriever RAG pipeline combining BM25 and dense vector search. Evaluated on a domain-specific question-answering benchmark using the RAGAS framework, our fine-tuned LLaMA 3 reranker achieves gains of 14% in answer relevancy, 16% in context precision, 19% in answer similarity, and 21% in answer correctness over the cross-encoder baseline, while reducing inference overhead through 4-bit quantization. These results demonstrate that instruction-tuned LLMs can be adapted into accurate, efficient rerankers without the quadratic complexity of traditional cross-encoders.
| Comments: | 6 pages, 4 figures. This work was completed in 2024 |
| Subjects: | Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.11933 [cs.CL] |
| (or arXiv:2607.11933v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.11933 arXiv-issued DOI via DataCite |
Submission history
From: Shreeya Dasa Lakshminath [view email]
[v1]
Sat, 11 Jul 2026 04:31:20 UTC (784 KB)
— Originally published at arxiv.org
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