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        <title>Citation: A Key to Building Responsible and Accountable Large Language Models</title>
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            <namePart type="given">Helena</namePart>
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    <abstract>Large Language Models (LLMs) bring transformative benefits alongside unique challenges, including intellectual property (IP) and ethical concerns. This position paper explores a novel angle to mitigate these risks, drawing parallels between LLMs and established web systems. We identify “citation”—the acknowledgement or reference to a source or evidence—as a crucial yet missing component in LLMs. Incorporating citation could enhance content transparency and verifiability, thereby confronting the IP and ethical issues in the deployment of LLMs. We further propose that a comprehensive citation mechanism for LLMs should account for both non-parametric and parametric content. Despite the complexity of implementing such a citation mechanism, along with the potential pitfalls, we advocate for its development. Building on this foundation, we outline several research problems in this area, aiming to guide future explorations towards building more responsible and accountable LLMs.</abstract>
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    <identifier type="doi">10.18653/v1/2024.findings-naacl.31</identifier>
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%0 Conference Proceedings
%T Citation: A Key to Building Responsible and Accountable Large Language Models
%A Huang, Jie
%A Chang, Kevin
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F huang-chang-2024-citation
%X Large Language Models (LLMs) bring transformative benefits alongside unique challenges, including intellectual property (IP) and ethical concerns. This position paper explores a novel angle to mitigate these risks, drawing parallels between LLMs and established web systems. We identify “citation”—the acknowledgement or reference to a source or evidence—as a crucial yet missing component in LLMs. Incorporating citation could enhance content transparency and verifiability, thereby confronting the IP and ethical issues in the deployment of LLMs. We further propose that a comprehensive citation mechanism for LLMs should account for both non-parametric and parametric content. Despite the complexity of implementing such a citation mechanism, along with the potential pitfalls, we advocate for its development. Building on this foundation, we outline several research problems in this area, aiming to guide future explorations towards building more responsible and accountable LLMs.
%R 10.18653/v1/2024.findings-naacl.31
%U https://aclanthology.org/2024.findings-naacl.31
%U https://doi.org/10.18653/v1/2024.findings-naacl.31
%P 464-473

Markdown (Informal)

[Citation: A Key to Building Responsible and Accountable Large Language Models](https://aclanthology.org/2024.findings-naacl.31) (Huang & Chang, Findings 2024)

• Citation: A Key to Building Responsible and Accountable Large Language Models (Huang & Chang, Findings 2024)

ACL

• Jie Huang and Kevin Chang. 2024. Citation: A Key to Building Responsible and Accountable Large Language Models. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 464–473, Mexico City, Mexico. Association for Computational Linguistics.