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..
How to Protect Copyright Data in Optimization of Large Language Models?
Authors: Timothy Chu, Zhao Song, Chiwun Yang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 7.1, we provided the details of our experiment. In Section 7.2, we provided experimental results and analyzed the effectiveness of Copyright Regression. |
| Researcher Affiliation | Collaboration | Timothy Chu1, Zhao Song2, Chiwun Yang3 1 Google, Mountain View, CA 2Adobe Research, San Jose, CA 3 Sun Yat-sen University, China |
| Pseudocode | No | The paper describes mathematical definitions and theoretical properties but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code of experiments in this paper is opensourced at https://github.com/Christian Yang37/chiwun/tree/ main/src/Copyright-Regression. |
| Open Datasets | Yes | We employed an open-source dataset Wikitext2 (Merity et al. 2016) to fine-tune our model, and evaluate the performance of our model on its test set. |
| Dataset Splits | No | The paper mentions using a 'test set' but does not explicitly provide details about a validation set split or specific percentages for training, validation, and test splits required for reproduction. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | To assess the influence of copyright data with different proportions during training, we varied the value of n1/n to be n1/n ∈ {0.1, 0.2, 0.4, 0.6, 0.8}. Additionally, to evaluate the impact of different values of γc on copyright protection, we consider γc values of {0.1, 0.2, 0.3, 0.4, 0.5}. In addition, we fixed random seeds and conducted multiple experiments to record the maximum, minimum, and average values to ensure stable results were obtained. |