TY - GEN
T1 - Citation
T2 - 2024 Findings of the Association for Computational Linguistics: NAACL 2024
AU - Huang, Jie
AU - Chang, Kevin Chen Chuan
N1 - We thank the reviewers for their constructive feedback. This material is based upon work supported by the National Science Foundation IIS 16-19302 and IIS 16-33755, Zhejiang University ZJU Research 083650, IBM-Illinois Center for Cognitive Computing Systems Research (C3SR) and IBM-Illinois Discovery Accelerator Institute (IIDAI), grants from eBay and Microsoft Azure, UIUC OVCR CCIL Planning Grant 434S34, UIUC CSBS Small Grant 434C8U, and UIUC New Frontiers Initiative. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the funding agencies.
PY - 2024
Y1 - 2024
N2 - Large Language Models (LLMs) bring trans-formative 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.
AB - Large Language Models (LLMs) bring trans-formative 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.
UR - http://www.scopus.com/inward/record.url?scp=85197922029&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85197922029&partnerID=8YFLogxK
U2 - 10.18653/v1/2024.findings-naacl.31
DO - 10.18653/v1/2024.findings-naacl.31
M3 - Conference contribution
AN - SCOPUS:85197922029
T3 - Findings of the Association for Computational Linguistics: NAACL 2024 - Findings
SP - 464
EP - 473
BT - Findings of the Association for Computational Linguistics
A2 - Duh, Kevin
A2 - Gomez, Helena
A2 - Bethard, Steven
PB - Association for Computational Linguistics (ACL)
Y2 - 16 June 2024 through 21 June 2024
ER -