Practical Solutions for Attributable Information-Seeking with AI
Challenges in Information-Seeking
Search engines use generative methods to provide accurate answers with citations, but open-ended queries pose challenges due to potential incorrect information.
AI Framework for Information-Seeking
A reproducible AI framework supports various LLM architectures for attributed information seeking and is adaptable to any dataset. It benchmarks attributed information-seeking tasks with different LLM architectures.
Approaches in the Framework
The framework includes the Generate approach, Retrieve Then Generate approach, and Generate Then Retrieve approach, each with variations to improve retrieval accuracy and answer correctness.
Evaluating Performance
Performance evaluations using the HAGRID dataset show that RTG approaches outperform other methods, demonstrating better overall performance in answer correctness and citation quality.
Insights and Benchmarks
The framework offers valuable insights and benchmarks for future research, focusing on both answer correctness and citation quality. The RTG-query-gen approach demonstrates significant improvements in citation accuracy.
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