Listwise Generative Retrieval Models: Optimizing Relevance at the Docid List Level

Core Concepts

Introducing a listwise approach to generative retrieval models, optimizing relevance at the docid list level.

Abstract

Introduces a novel listwise approach to generative retrieval models.
Addresses limitations of pointwise approaches in ranking docids.
Employs sequential learning process for generating ranked docid lists.
Conducts training with position-aware ListMLE and re-training with relevance calibration.
Demonstrates improved performance on binary and multi-graded relevance datasets compared to state-of-the-art methods.

Stats

"Our method outperforms state-of-the-art GR baselines in terms of retrieval performance."
"Achieves a significant improvement of 15.8% in nDCG@5 on the ClueWeb 200K dataset."

Quotes

"To address this limitation and enhance the capability of the GR model to generate a high-quality ranked docid list, this work focuses on modeling and optimizing the relevance at the list level."
"Our main contributions are introducing a listwise approach specifically designed for GR and formulating a listwise learning objective for optimizing relevance at the docid list level."

Key Insights Distilled From

Listwise Generative Retrieval Models via a Sequential Learning Process

by Yubao Tang,R... at arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12499.pdf

Deeper Inquiries

How can alternative weight functions be designed to enhance binary relevance data in the first stage

To enhance binary relevance data in the first stage, alternative weight functions can be designed to prioritize the generation of relevant docids. One approach could involve assigning higher weights to tokens within docids with higher relevance grades and lower weights to tokens within irrelevant docids. This would ensure that the model focuses more on generating relevant docids accurately, aligning with the goal of improving retrieval performance for binary relevance datasets.

What are potential implications of decoding inconsistency between training with ground-truth tokens and inference without them

The decoding inconsistency between training with ground-truth tokens and inference without them can have significant implications on the quality of generated lists. During training, models rely on ground-truth tokens to generate subsequent ones, leading to a more accurate representation of relevance. However, during inference, when only previously generated tokens are used for further generation, there is a risk of deviating from the intended ranking order based on relevance. This inconsistency may result in suboptimal rankings and reduced retrieval performance.

How does aligning candidate likelihoods with relevance grades improve the quality of generated docid lists

Aligning candidate likelihoods with relevance grades improves the quality of generated docid lists by ensuring that highly relevant documents are given priority in ranking. By calibrating likelihoods based on relevance grades, the model learns to assign higher probabilities to generating highly relevant docids earlier in the list. This alignment helps create more accurate and contextually relevant ranked lists that better reflect user intent and improve overall retrieval effectiveness.