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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Neighboring Perturbations of Knowledge Editing on Large Language Models
Authors: Jun-Yu Ma, Zhen-Hua Ling, Ningyu Zhang, Jia-Chen Gu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate the effectiveness of APP coupling with four editing methods on four LLMs. |
| Researcher Affiliation | Academia | 1NERC-SLIP, University of Science and Technology of China 2Zhejiang University 3University of California, Los Angeles. |
| Pseudocode | No | The paper describes the proposed method and its objectives using mathematical equations and textual explanations, but it does not include a formal pseudocode block or algorithm. |
| Open Source Code | Yes | The code and data are available at https://github.com/mjy1111/PEAK. |
| Open Datasets | Yes | To evaluate the additivity of edited models, a benchmark dubbed as Perturbation Evaluation of Appending Knowledge (PEAK) is constructed... The code and data are available at https://github.com/mjy1111/PEAK. |
| Dataset Splits | Yes | To set the hyperparameters, we additionally created a small validation set. |
| Hardware Specification | No | The paper mentions the large language models used (e.g., GPT-2 XL, LLaMA-2) and alludes to 'limited computing resources', but it does not specify any particular hardware like GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions the use of specific LLMs (e.g., GPT-2 XL, GPT-J, LLaMA-2) and frameworks for editing (e.g., Easy Edit), but it does not provide specific version numbers for underlying software libraries like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | Appendix B.2, 'Hyperparameters for APP', states: 'Table 8 shows the details of hyperparameters set for different LLMs. Besides, the margin M is set to 2.' Table 8 then lists specific alpha, beta, and gamma values for each method and LLM. |