Listwise Generative Retrieval Models via a Sequential Learning Process

Abstract

Recently, a novel generative retrieval (GR) paradigm has been proposed, where a single sequence-to-sequence model is learned to directly generate a list of relevant document identifiers (docids) given a query. Existing generative retrieval (GR) models commonly employ maximum likelihood estimation (MLE) for optimization: this involves maximizing the likelihood of a single relevant docid given an input query, with the assumption that the likelihood for each docid is independent of the other docids in the list. We refer to these models as the pointwise approach in this paper. While the pointwise approach has been shown to be effective in the context of generative retrieval (GR), it is considered sub-optimal due to its disregard for the fundamental principle that ranking involves making predictions about lists. In this paper, we address this limitation by introducing an alternative listwise approach, which empowers the generative retrieval (GR) model to optimize the relevance at the docid list level. Specifically, we view the generation of a ranked docid list as a sequence learning process: at each step we learn a subset of parameters that maximizes the corresponding generation likelihood of the i-th docid given the (preceding) top i − 1 docids. To formalize the sequence learning process, we design a positional conditional probability for generative retrieval (GR). To alleviate the potential impact of beam search on the generation quality during inference, we perform relevance calibration on the generation likelihood of model-generated docids according to relevance grades. We conduct extensive experiments on representative binary and multi-graded relevance datasets. Our empirical results demonstrate that our method outperforms state-of-the-art generative retrieval (GR) baselines in terms of retrieval performance.