# Listwise Generative Retrieval Models via a Sequential Learning Process

arXiv: https://arxiv.org/abs/2403.12499 · HTML: https://mlsanity.com/paper/2403.12499/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2024.

## Built on by (most cited first, 11 of 11)

- [From Matching to Generation: A Survey on Generative Information Retrieval](https://mlsanity.com/paper/2404.14851/) (2024)
- [RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation](https://mlsanity.com/paper/2412.11919/) (2024)
- [Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval](https://mlsanity.com/paper/2504.05181/) (2025)
- [Exploring Training and Inference Scaling Laws in Generative Retrieval](https://mlsanity.com/paper/2503.18941/) (2025)
- [Multi-Turn Multi-Modal Question Clarification for Enhanced Conversational Understanding](https://mlsanity.com/paper/2502.11442/) (2025)
- [LLM-Confidence Reranker: A Training-Free Approach for Enhancing Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2602.13571/) (2026)
- [A Parametric Memory Head for Continual Generative Retrieval](https://mlsanity.com/paper/2604.23388/) (2026)
- [CroPS: Improving Dense Retrieval with Cross-Perspective Positive Samples in Short-Video Search](https://mlsanity.com/paper/2511.15443/) (2025)
- [Lost in Decoding? Reproducing and Stress-Testing the Look-Ahead Prior in Generative Retrieval](https://mlsanity.com/paper/2604.23396/) (2026)
- [ICICLE: Expanding Retrieval with In-Context Documents](https://mlsanity.com/paper/2605.26902/) (2026)
- [The Laws of Context Allocation: Causal Measurement and Closed-Loop Orchestration in Generative Search](https://mlsanity.com/paper/2608.23252/) (2026)

## Abstract (arXiv)

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 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 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 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 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 GR baselines in terms of retrieval performance.