Core Concepts
Abstention mechanisms improve IR system reliability by refraining from making predictions when uncertainty is detected.
Abstract
The study focuses on proposing a lightweight abstention mechanism for real-world constraints in the reranking phase of Information Retrieval systems. It introduces a protocol for evaluating abstention strategies and a data-driven mechanism for improved performance. The research highlights the importance of confidence assessment in document reranking and the effectiveness of reference-based abstention methods. The study also addresses the computational overhead and ethical implications of implementing abstention mechanisms in AI technologies.
Directory:
- Introduction
- NIR advancements in addressing IR challenges.
- Importance of retrieval and reranking stages.
- Problem Statement & Related Work
- Notations and dataset definitions.
- Relevance scoring and document ranking functions.
- Confidence Assessment for Document Reranking
- Confidence scoring methods in reranking.
- Reference-free and data-driven confidence assessment.
- Experimental Setup
- Models and datasets used for evaluation.
- Instance-wise metrics for performance evaluation.
- Results
- Abstention performance analysis.
- Correlation between abstention effectiveness and model performance.
- Threshold calibration and domain adaptation study.
- Impact of reference set size on method performance.
- Computational overhead analysis.
- Conclusion
- Summary of findings and future research directions.
Stats
Neural Information Retrieval has improved upon heuristic-based IR systems.
Abstention mechanisms offer a pragmatic approach to address challenges in IR.
Reference-based abstention methods outperform reference-free baselines.
Linear-regression-based abstention method shows effectiveness across various settings.
Abstention mechanisms incur minimal computational overhead.
Quotes
"Abstention mechanisms improve the accuracy and effectiveness of retrieval systems."
"Reference-based abstention methods demonstrate superior performance in practical settings."