An Analysis of Fusion Functions for Hybrid Retrieval

• DOI: 10.1145/3596512
• Publication Date: 2023-05-20T08:59:21Z
• Authors: Sebastian Bruch; Siyu Gai; Amir Ingber
• Keywords: FOS: Computer and information sciences; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; 0509 other social sciences; Information Retrieval (cs.IR); Computer Science - Information Retrieval

We study hybrid search in text retrieval where lexical and semantic search are fused together with the intuition that the two are complementary in how they model relevance. In particular, we examine fusion by a convex combination of lexical and semantic scores, as well as the reciprocal rank fusion (RRF) method, and identify their advantages and potential pitfalls. Contrary to existing studies, we find RRF to be sensitive to its parameters; that the learning of a convex combination fusion is generally agnostic to the choice of score normalization; that convex combination outperforms RRF in in-domain and out-of-domain settings; and finally, that convex combination is sample efficient, requiring only a small set of training examples to tune its only parameter to a target domain.