@inproceedings{han-etal-2023-improving,
title = "Improving Sequential Model Editing with Fact Retrieval",
author = "Han, Xiaoqi and
Li, Ru and
Tan, Hongye and
Yuanlong, Wang and
Chai, Qinghua and
Pan, Jeff",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.749",
doi = "10.18653/v1/2023.findings-emnlp.749",
pages = "11209--11224",
abstract = "The task of sequential model editing is to fix erroneous knowledge in Pre-trained Language Models (PLMs) efficiently, precisely and continuously. Although existing methods can deal with a small number of modifications, these methods experience a performance decline or require additional annotated data, when the number of edits increases. In this paper, we propose a $\textbf{R}$etrieval $\textbf{A}$ugmented $\textbf{S}$equential Model $\textbf{E}$diting framework ($\textbf{RASE}$) that leverages factual information to enhance editing generalization and to guide the identification of edits by retrieving related facts from the fact-patch memory we constructed. Our main findings are: (i) State-of-the-art models can hardly correct massive mistakes stably and efficiently; (ii) Even if we scale up to thousands of edits, RASE can significantly enhance editing generalization and maintain consistent performance and efficiency; (iii) RASE can edit large-scale PLMs and increase the performance of different editors. Moreover, it can integrate with ChatGPT and further improve performance. Our code and data are available at: https://github.com/sev777/RASE.",
}
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<abstract>The task of sequential model editing is to fix erroneous knowledge in Pre-trained Language Models (PLMs) efficiently, precisely and continuously. Although existing methods can deal with a small number of modifications, these methods experience a performance decline or require additional annotated data, when the number of edits increases. In this paper, we propose a Retrieval Augmented Sequential Model Editing framework (RASE) that leverages factual information to enhance editing generalization and to guide the identification of edits by retrieving related facts from the fact-patch memory we constructed. Our main findings are: (i) State-of-the-art models can hardly correct massive mistakes stably and efficiently; (ii) Even if we scale up to thousands of edits, RASE can significantly enhance editing generalization and maintain consistent performance and efficiency; (iii) RASE can edit large-scale PLMs and increase the performance of different editors. Moreover, it can integrate with ChatGPT and further improve performance. Our code and data are available at: https://github.com/sev777/RASE.</abstract>
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%0 Conference Proceedings
%T Improving Sequential Model Editing with Fact Retrieval
%A Han, Xiaoqi
%A Li, Ru
%A Tan, Hongye
%A Yuanlong, Wang
%A Chai, Qinghua
%A Pan, Jeff
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F han-etal-2023-improving
%X The task of sequential model editing is to fix erroneous knowledge in Pre-trained Language Models (PLMs) efficiently, precisely and continuously. Although existing methods can deal with a small number of modifications, these methods experience a performance decline or require additional annotated data, when the number of edits increases. In this paper, we propose a Retrieval Augmented Sequential Model Editing framework (RASE) that leverages factual information to enhance editing generalization and to guide the identification of edits by retrieving related facts from the fact-patch memory we constructed. Our main findings are: (i) State-of-the-art models can hardly correct massive mistakes stably and efficiently; (ii) Even if we scale up to thousands of edits, RASE can significantly enhance editing generalization and maintain consistent performance and efficiency; (iii) RASE can edit large-scale PLMs and increase the performance of different editors. Moreover, it can integrate with ChatGPT and further improve performance. Our code and data are available at: https://github.com/sev777/RASE.
%R 10.18653/v1/2023.findings-emnlp.749
%U https://aclanthology.org/2023.findings-emnlp.749
%U https://doi.org/10.18653/v1/2023.findings-emnlp.749
%P 11209-11224
Markdown (Informal)
[Improving Sequential Model Editing with Fact Retrieval](https://aclanthology.org/2023.findings-emnlp.749) (Han et al., Findings 2023)
ACL
- Xiaoqi Han, Ru Li, Hongye Tan, Wang Yuanlong, Qinghua Chai, and Jeff Pan. 2023. Improving Sequential Model Editing with Fact Retrieval. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11209–11224, Singapore. Association for Computational Linguistics.