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].
The Fine-Grained Complexity of Gradient Computation for Training Large Language Models
Authors: Josh Alman, Zhao Song
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper is a purely theoretical paper, and it doesn t include any experiments. |
| Researcher Affiliation | Academia | Josh Alman Department of Computer Science Columbia University EMAIL Zhao Song Simons Institute for the Theory of Computing University of California, Berkeley EMAIL |
| Pseudocode | No | The paper describes algorithmic ideas and steps but does not include any explicit pseudocode blocks or algorithms labeled as such. |
| Open Source Code | No | This paper is a purely theoretical paper, and it doesn t include any experiments. |
| Open Datasets | No | This paper is a purely theoretical paper, and it doesn t include any experiments. |
| Dataset Splits | No | This paper is a purely theoretical paper, and it doesn t include any experiments. |
| Hardware Specification | No | This paper is a purely theoretical paper, and it doesn t include any experiments. |
| Software Dependencies | No | This paper is a purely theoretical paper, and it doesn t include any experiments. |
| Experiment Setup | No | This paper is a purely theoretical paper, and it doesn t include any experiments. |