REVIEW 3 cited by
Ethical Considerations and Policy Implications for Large Language Models: Guiding Responsible Development and Deployment
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
Signed reviews
read the original abstract
This paper examines the ethical considerations and implications of large language models (LLMs) in generating content. It highlights the potential for both positive and negative uses of generative AI programs and explores the challenges in assigning responsibility for their outputs. The discussion emphasizes the need for proactive ethical frameworks and policy measures to guide the responsible development and deployment of LLMs.
Forward citations
Cited by 3 Pith papers
-
Overcoming Data Scarcity in Generative Language Modelling for Low-Resource Languages: A Systematic Review
A systematic review of 54 studies finds that generative language modelling for low-resource languages relies mostly on transformer models, covers only a small set of languages, and lacks consistent evaluation.
-
Comparing Human and LLM Generated Code: The Jury is Still Out!
On 72 Python tasks, GPT-4 code passed 87.3% of tests versus 54.9% for one student's code, but was more complex and showed more severe security issues.
-
Challenges in Guardrailing Large Language Models for Science
A position paper proposing a guardrail framework with four dimensions (trustworthiness, ethics & bias, safety, legal) and implementation strategies for scientific LLM use.
Discussion (0). Continue with ORCID to comment.