The Case Against LLMs as Rerankers

Large language models (LLMs) are increasingly used for tasks like reranking, but this study finds them less effective compared to specialized rerankers such as rerank-2.5. These purpose-built models are significantly cheaper, faster, and offer superior reranking accuracy, especially when paired with strong first-stage retrieval methods. The research highlights that LLMs, while convenient, are overshadowed by rerankers in performance metrics across diverse datasets and retrieval methods. Specialized rerankers optimize two-stage retrieval systems, crucial for applications like retrieval-augmented generation, by overcoming challenges such as the "lost in the middle" problem that LLMs face. Despite LLMs’ ability to handle large contexts, they underperform in practical applications due to high costs, latency, and sensitivity to setup details. This makes specialized rerankers the preferred choice for efficient and cost-effective AI applications.

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