Carlin Soos
Biographic Data
| ID | 9869109 |
|---|---|
| NAME | Carlin Soos |
| GIVEN NAMES | Carlin |
| FAMILY NAME | Soos |
| SIGNATURE | SOOS C |
| AFFILIATIONS | University of California, Los Angeles |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
On the Question of Authorship in Large Language Models (LLMs)
The adoption of pre-trained large language models (LLMs), like ChatGPT, across an increasingly diverse range of tasks and domains poses significant challenges for authorial attribution and other basic knowledge organization practices. This paper examines the theoretical and practical issues introduced by LLMs and describes how their use erodes the supposedly firm boundaries separating specific works and creators. Building upon the author-as-node …
Extended Abstract Out of the Ordinary
The common-law doctrine of stare decisis tasks the courts with comparing, sorting, and regulating new cases based on established standards of legality, a lineage of thought that seeks continuity between past and future rulings (Scalia 1997, 7). Regardless of this strong desire for stability and consistency, the frustrating reality is that ambiguity is often written—intentionally or accidentally—into legislation, contracts, and policy, questionabl…
No prominent works on this page.
Extended Abstract Out of the Ordinary
The common-law doctrine of stare decisis tasks the courts with comparing, sorting, and regulating new cases based on established standards of legality, a lineage of thought that seeks continuity between past and future rulings (Scalia 1997, 7). Regardless of this strong desire for stability and consistency, the frustrating reality is that ambiguity is often written—intentionally or accidentally—into legislation, contracts, and policy, questionabl…
On the Question of Authorship in Large Language Models (LLMs)
The adoption of pre-trained large language models (LLMs), like ChatGPT, across an increasingly diverse range of tasks and domains poses significant challenges for authorial attribution and other basic knowledge organization practices. This paper examines the theoretical and practical issues introduced by LLMs and describes how their use erodes the supposedly firm boundaries separating specific works and creators. Building upon the author-as-node …
Law (2 works) · Political science (2 works) · Sociology (2 works) · Ambiguity (1 works) · Appeal (1 works) · Artificial Intelligence (1 works) · Artificial Intelligence in Healthcare and Education (1 works) · Attribution (1 works) · Computer Science (1 works) · Doctrine (1 works)