@inproceedings{intrator-etal-2024-streamlining,
title = "Streamlining Conformal Information Retrieval via Score Refinement",
author = "Intrator, Yotam and
Cohen, Regev and
Kelner, Ori and
Goldenberg, Roman and
Rivlin, Ehud and
Freedman, Daniel",
editor = "Schlichtkrull, Michael and
Chen, Yulong and
Whitehouse, Chenxi and
Deng, Zhenyun and
Akhtar, Mubashara and
Aly, Rami and
Guo, Zhijiang and
Christodoulopoulos, Christos and
Cocarascu, Oana and
Mittal, Arpit and
Thorne, James and
Vlachos, Andreas",
booktitle = "Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.fever-1.22",
pages = "186--191",
abstract = "Information retrieval (IR) methods, like retrieval augmented generation, are fundamental to modern applications but often lack statistical guarantees. Conformal prediction addresses this by retrieving sets guaranteed to include relevant information, yet existing approaches produce large-sized sets, incurring high computational costs and slow response times. In this work, we introduce a score refinement method that applies a simple monotone transformation to retrieval scores, leading to significantly smaller conformal sets while maintaining their statistical guarantees. Experiments on various BEIR benchmarks validate the effectiveness of our approach in producing compact sets containing relevant information.",
}
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<abstract>Information retrieval (IR) methods, like retrieval augmented generation, are fundamental to modern applications but often lack statistical guarantees. Conformal prediction addresses this by retrieving sets guaranteed to include relevant information, yet existing approaches produce large-sized sets, incurring high computational costs and slow response times. In this work, we introduce a score refinement method that applies a simple monotone transformation to retrieval scores, leading to significantly smaller conformal sets while maintaining their statistical guarantees. Experiments on various BEIR benchmarks validate the effectiveness of our approach in producing compact sets containing relevant information.</abstract>
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%0 Conference Proceedings
%T Streamlining Conformal Information Retrieval via Score Refinement
%A Intrator, Yotam
%A Cohen, Regev
%A Kelner, Ori
%A Goldenberg, Roman
%A Rivlin, Ehud
%A Freedman, Daniel
%Y Schlichtkrull, Michael
%Y Chen, Yulong
%Y Whitehouse, Chenxi
%Y Deng, Zhenyun
%Y Akhtar, Mubashara
%Y Aly, Rami
%Y Guo, Zhijiang
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Mittal, Arpit
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F intrator-etal-2024-streamlining
%X Information retrieval (IR) methods, like retrieval augmented generation, are fundamental to modern applications but often lack statistical guarantees. Conformal prediction addresses this by retrieving sets guaranteed to include relevant information, yet existing approaches produce large-sized sets, incurring high computational costs and slow response times. In this work, we introduce a score refinement method that applies a simple monotone transformation to retrieval scores, leading to significantly smaller conformal sets while maintaining their statistical guarantees. Experiments on various BEIR benchmarks validate the effectiveness of our approach in producing compact sets containing relevant information.
%U https://aclanthology.org/2024.fever-1.22
%P 186-191
Markdown (Informal)
[Streamlining Conformal Information Retrieval via Score Refinement](https://aclanthology.org/2024.fever-1.22) (Intrator et al., FEVER 2024)
ACL
- Yotam Intrator, Regev Cohen, Ori Kelner, Roman Goldenberg, Ehud Rivlin, and Daniel Freedman. 2024. Streamlining Conformal Information Retrieval via Score Refinement. In Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER), pages 186–191, Miami, Florida, USA. Association for Computational Linguistics.