Revisiting Feedback Models for HyDE

• URL: http://arxiv.org/abs/2511.19349v1
• Date: Mon, 24 Nov 2025 17:50:18 GMT
• Title: Revisiting Feedback Models for HyDE
• Authors: Nour Jedidi, Jimmy Lin,
• Abstract summary: HyDE is a method that enriches query representations with LLM-generated hypothetical answer documents.<n>Our experiments show that HyDE's effectiveness can be substantially improved when leveraging feedback algorithms such as Rocchio to extract and weight expansion terms.
• Score: 49.53124785319461
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent approaches that leverage large language models (LLMs) for pseudo-relevance feedback (PRF) have generally not utilized well-established feedback models like Rocchio and RM3 when expanding queries for sparse retrievers like BM25. Instead, they often opt for a simple string concatenation of the query and LLM-generated expansion content. But is this optimal? To answer this question, we revisit and systematically evaluate traditional feedback models in the context of HyDE, a popular method that enriches query representations with LLM-generated hypothetical answer documents. Our experiments show that HyDE's effectiveness can be substantially improved when leveraging feedback algorithms such as Rocchio to extract and weight expansion terms, providing a simple way to further enhance the accuracy of LLM-based PRF methods.

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