Nonparametric Decoding For Generative Retrieval

Abstract

The generative retrieval model depends solely on the information encoded in its model parameters without external memory, its information capacity is limited and fixed. To overcome the limitation, we propose Nonparametric Decoding (Np Decoding) which can be applied to existing generative retrieval models. Np Decoding uses nonparametric contextualized vocab embeddings (external memory) rather than vanilla vocab embeddings as decoder vocab embeddings. By leveraging the contextualized vocab embeddings, the generative retrieval model is able to utilize both the parametric and nonparametric space. Evaluation over 9 datasets (8 single-hop and 1 multi-hop) in the document retrieval task shows that applying Np Decoding to generative retrieval models significantly improves the performance. We also show that Np Decoding is data- and parameter-efficient, and shows high performance in the zero-shot setting.

Stats

• citations5
• S2 citations—
• github stars0
• HF likes0
• heat score5.84
• arxiv keylee2022nonparametric

Related papers

• Planning Ahead In Generative Retrieval: Guiding Autoregressive Generation Through Simultaneous Decoding (2024)8.82
• Generative Retrieval As Dense Retrieval (2023)0.00
• Does Generative Retrieval Overcome The Limitations Of Dense Retrieval? (2025)0.00
• Generative Retrieval As Multi-vector Dense Retrieval (2024)8.60
• Generative Dense Retrieval: Memory Can Be A Burden (2024)4.52
• Generative Retrieval Meets Multi-graded Relevance (2024)2.26
• Scalable And Effective Generative Information Retrieval (2023)10.48
• Hierarchical Corpus Encoder: Fusing Generative Retrieval And Dense Indices (2025)0.00