@inproceedings{cattan-etal-2025-doubledipper,
title = "{D}ouble{D}ipper: Recycling Contexts for Efficient and Attributed In-Context Learning",
author = "Cattan, Arie and
Jacovi, Alon and
Fabrikant, Alex and
Herzig, Jonathan and
Aharoni, Roee and
Rashkin, Hannah and
Marcus, Dror and
Hassidim, Avinatan and
Matias, Yossi and
Szpektor, Idan and
Caciularu, Avi",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-ijcnlp.42/",
pages = "722--737",
ISBN = "979-8-89176-303-6",
abstract = "In this work, we propose DoubleDipper, a novel In-Context-Learning method that automatically generates few-shot examples for several QA tasks by {\_}recycling{\_} contexts. Specifically, given an input context (1-3k tokens) and a query, we generate additional query-output pairs from the given context as few-shot examples, while introducing the context only once. This ensures that the demonstrations are leveraging the same context as the target query while only adding a small number of tokens to the prompt. We further enhance each demonstration by instructing the model to {\_}explicitly{\_} identify the relevant paragraphs before the answer, which improves performance while providing fine-grained attribution to the answer source. We apply our method on multiple LLMs and obtain substantial improvements (+16 absolute points on average across models) on various QA datasets. Surprisingly, despite introducing only single-hop ICL examples, LLMs successfully generalize to multi-hop QA using our approach."
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<abstract>In this work, we propose DoubleDipper, a novel In-Context-Learning method that automatically generates few-shot examples for several QA tasks by _recycling_ contexts. Specifically, given an input context (1-3k tokens) and a query, we generate additional query-output pairs from the given context as few-shot examples, while introducing the context only once. This ensures that the demonstrations are leveraging the same context as the target query while only adding a small number of tokens to the prompt. We further enhance each demonstration by instructing the model to _explicitly_ identify the relevant paragraphs before the answer, which improves performance while providing fine-grained attribution to the answer source. We apply our method on multiple LLMs and obtain substantial improvements (+16 absolute points on average across models) on various QA datasets. Surprisingly, despite introducing only single-hop ICL examples, LLMs successfully generalize to multi-hop QA using our approach.</abstract>
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%0 Conference Proceedings
%T DoubleDipper: Recycling Contexts for Efficient and Attributed In-Context Learning
%A Cattan, Arie
%A Jacovi, Alon
%A Fabrikant, Alex
%A Herzig, Jonathan
%A Aharoni, Roee
%A Rashkin, Hannah
%A Marcus, Dror
%A Hassidim, Avinatan
%A Matias, Yossi
%A Szpektor, Idan
%A Caciularu, Avi
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-303-6
%F cattan-etal-2025-doubledipper
%X In this work, we propose DoubleDipper, a novel In-Context-Learning method that automatically generates few-shot examples for several QA tasks by _recycling_ contexts. Specifically, given an input context (1-3k tokens) and a query, we generate additional query-output pairs from the given context as few-shot examples, while introducing the context only once. This ensures that the demonstrations are leveraging the same context as the target query while only adding a small number of tokens to the prompt. We further enhance each demonstration by instructing the model to _explicitly_ identify the relevant paragraphs before the answer, which improves performance while providing fine-grained attribution to the answer source. We apply our method on multiple LLMs and obtain substantial improvements (+16 absolute points on average across models) on various QA datasets. Surprisingly, despite introducing only single-hop ICL examples, LLMs successfully generalize to multi-hop QA using our approach.
%U https://aclanthology.org/2025.findings-ijcnlp.42/
%P 722-737
Markdown (Informal)
[DoubleDipper: Recycling Contexts for Efficient and Attributed In-Context Learning](https://aclanthology.org/2025.findings-ijcnlp.42/) (Cattan et al., Findings 2025)
ACL
- Arie Cattan, Alon Jacovi, Alex Fabrikant, Jonathan Herzig, Roee Aharoni, Hannah Rashkin, Dror Marcus, Avinatan Hassidim, Yossi Matias, Idan Szpektor, and Avi Caciularu. 2025. DoubleDipper: Recycling Contexts for Efficient and Attributed In-Context Learning. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 722–737, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.