@inproceedings{park-etal-2024-enhancing,
title = "Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning",
author = "Park, SeongIl and
Choi, Seungwoo and
Kim, Nahyun and
Lee, Jay-Yoon",
editor = "Yu, Wenhao and
Shi, Weijia and
Yasunaga, Michihiro and
Jiang, Meng and
Zhu, Chenguang and
Hajishirzi, Hannaneh and
Zettlemoyer, Luke and
Zhang, Zhihan",
booktitle = "Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowledgenlp-1.7",
pages = "93--102",
abstract = "Retrieval-Augmented Language Models (RALMs) have significantly improved performance in open-domain question answering (QA) by leveraging external knowledge. However, RALMs still struggle with unanswerable queries, where the retrieved contexts do not contain the correct answer, and with conflicting information, where different sources provide contradictory answers due to imperfect retrieval. This study introduces an in-context learning-based approach to enhance the reasoning capabilities of RALMs, making them more robust in imperfect retrieval scenarios. Our method incorporates Machine Reading Comprehension (MRC) demonstrations, referred to as $\textit{cases}$, to boost the model{'}s capabilities to identify unanswerabilities and conflicts among the retrieved contexts. Experiments on two open-domain QA datasets show that our approach increases accuracy in identifying unanswerable and conflicting scenarios without requiring additional fine-tuning. This work demonstrates that in-context learning can effectively enhance the robustness of RALMs in open-domain QA tasks.",
}
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<abstract>Retrieval-Augmented Language Models (RALMs) have significantly improved performance in open-domain question answering (QA) by leveraging external knowledge. However, RALMs still struggle with unanswerable queries, where the retrieved contexts do not contain the correct answer, and with conflicting information, where different sources provide contradictory answers due to imperfect retrieval. This study introduces an in-context learning-based approach to enhance the reasoning capabilities of RALMs, making them more robust in imperfect retrieval scenarios. Our method incorporates Machine Reading Comprehension (MRC) demonstrations, referred to as cases, to boost the model’s capabilities to identify unanswerabilities and conflicts among the retrieved contexts. Experiments on two open-domain QA datasets show that our approach increases accuracy in identifying unanswerable and conflicting scenarios without requiring additional fine-tuning. This work demonstrates that in-context learning can effectively enhance the robustness of RALMs in open-domain QA tasks.</abstract>
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%0 Conference Proceedings
%T Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning
%A Park, SeongIl
%A Choi, Seungwoo
%A Kim, Nahyun
%A Lee, Jay-Yoon
%Y Yu, Wenhao
%Y Shi, Weijia
%Y Yasunaga, Michihiro
%Y Jiang, Meng
%Y Zhu, Chenguang
%Y Hajishirzi, Hannaneh
%Y Zettlemoyer, Luke
%Y Zhang, Zhihan
%S Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F park-etal-2024-enhancing
%X Retrieval-Augmented Language Models (RALMs) have significantly improved performance in open-domain question answering (QA) by leveraging external knowledge. However, RALMs still struggle with unanswerable queries, where the retrieved contexts do not contain the correct answer, and with conflicting information, where different sources provide contradictory answers due to imperfect retrieval. This study introduces an in-context learning-based approach to enhance the reasoning capabilities of RALMs, making them more robust in imperfect retrieval scenarios. Our method incorporates Machine Reading Comprehension (MRC) demonstrations, referred to as cases, to boost the model’s capabilities to identify unanswerabilities and conflicts among the retrieved contexts. Experiments on two open-domain QA datasets show that our approach increases accuracy in identifying unanswerable and conflicting scenarios without requiring additional fine-tuning. This work demonstrates that in-context learning can effectively enhance the robustness of RALMs in open-domain QA tasks.
%U https://aclanthology.org/2024.knowledgenlp-1.7
%P 93-102
Markdown (Informal)
[Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning](https://aclanthology.org/2024.knowledgenlp-1.7) (Park et al., KnowledgeNLP-WS 2024)
ACL