Axioms for AI Alignment from Human Feedback
Authors: Luise Ge, Daniel Halpern, Evi Micha, Ariel D. Procaccia, Itai Shapira, Yevgeniy Vorobeychik, Junlin Wu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The answer NA means that the paper does not include experiments. |
| Researcher Affiliation | Academia | Luise Ge Washington University in St. Louis g.luise@wustl.edu Daniel Halpern Harvard University dhalpern@g.harvard.edu Evi Micha Harvard University emicha@seas.harvard.edu Ariel D. Procaccia Harvard University arielpro@g.harvard.edu Itai Shapira Harvard University itaishapira@g.harvard.edu Yevgeniy Vorobeychik Washington University in St. Louis yvorobeychik@wustl.edu Junlin Wu Washington University in St. Louis junlin.wu@wustl.edu |
| Pseudocode | No | The paper describes mathematical proofs and theoretical concepts but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The answer NA means that paper does not include experiments requiring code. |
| Open Datasets | No | The answer NA means that the paper does not include experiments. |
| Dataset Splits | No | The answer NA means that the paper does not include experiments. |
| Hardware Specification | No | The answer NA means that the paper does not include experiments. |
| Software Dependencies | No | The answer NA means that the paper does not include experiments. |
| Experiment Setup | No | The answer NA means that the paper does not include experiments. |