Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
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 | Venue PDF | 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 EMAIL Daniel Halpern Harvard University EMAIL Evi Micha Harvard University EMAIL Ariel D. Procaccia Harvard University EMAIL Itai Shapira Harvard University EMAIL Yevgeniy Vorobeychik Washington University in St. Louis EMAIL Junlin Wu Washington University in St. Louis EMAIL |
| 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. |