REVIEW 2 cited by
Citation: A Key to Building Responsible and Accountable Large Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 2 Pith papers
-
On Mechanistic Circuits for Extractive Question-Answering
One attention head from the extracted context-faithfulness circuit provides reliable extractive QA attribution and improves context faithfulness when its attributions are added to the prompt.
-
A Multi-Task Evaluation of LLMs' Processing of Academic Text Input
The abstract reports Gemini underperforms on four academic text tasks, but the attached full text is an unrelated biomedical retrieval paper, leaving the claims unverifiable.
Discussion (0). Continue with ORCID to comment.