Position: Key Claims in LLM Research Have a Long Tail of Footnotes
Authors: Anna Rogers, Sasha Luccioni
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This position paper argues that LLM research should be more precise with its key terms and claims. |
| Researcher Affiliation | Collaboration | 1IT University of Copenhagen 2Hugging Face, Canada. Correspondence to: Anna Rogers <arog@itu.dk>, Sasha Luccioni <sasha.luccioni@hf.co>. |
| Pseudocode | No | No, the paper is a position paper and does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | No, the paper is a position paper and does not describe a new methodology for which open-source code would be provided. |
| Open Datasets | No | No, this is a position paper and does not involve training models on a dataset. It discusses concepts and critiques claims within LLM research rather than presenting new experimental data. |
| Dataset Splits | No | No, this paper is a position paper and does not describe experiments that would involve dataset splits for validation. |
| Hardware Specification | No | No, the paper is a position paper and does not conduct experiments requiring specific hardware specifications. |
| Software Dependencies | No | No, the paper is a position paper and does not conduct experiments requiring specific software dependencies. |
| Experiment Setup | No | No, the paper is a position paper and does not describe an experimental setup with hyperparameters or system-level training settings. |