Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval
Kidist Amde Mekonnen, Yubao Tang, Maarten de Rijke·April 07, 2025
Summary
DDRO optimizes document relevance, excelling in MS MARCO and Natural Questions, surpassing reinforcement learning methods. It addresses token-level misalignment for effective retrieval, focusing on content-derived and computationally-generated docids. Challenges include aligning token generation with broader ranking tasks. Contributions focus on dense passage retrieval for open-domain question answering, with works by Yih, Karpukhin, Kishore, and others presented at EMNLP and ICML.
Introduction
Background
Overview of document retrieval systems
Importance of relevance in information retrieval
Objective
Enhancing document retrieval through DDRO
Performance in MS MARCO and Natural Questions benchmarks
Method
Token-level Misalignment
Explanation of token-level misalignment in document retrieval
DDRO's approach to addressing this issue
Content-derived and Computationally-generated Docids
Importance of accurate docid assignment
DDRO's method for improving docid relevance
Challenges
Aligning token generation with broader ranking tasks
Contributions
Dense Passage Retrieval
Focus on open-domain question answering
DDRO's role in advancing dense passage retrieval
Notable Works
Yih's contributions
Karpukhin's contributions
Kishore's contributions
Presentations at EMNLP and ICML
Applications and Impact
MS MARCO and Natural Questions
Performance metrics and benchmarks
Beyond Document Retrieval
Potential applications and future directions
Conclusion
Summary of DDRO's achievements
Future Research Directions
Basic info
papers
information retrieval
digital libraries
machine learning
artificial intelligence
Advanced features
Insights
How does DDRO optimize document relevance in comparison to reinforcement learning methods?
What are the key implementation strategies of DDRO for addressing token-level misalignment?
What are the innovative contributions of DDRO presented at EMNLP and ICML?
In what ways does DDRO enhance dense passage retrieval for open-domain question answering?