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..
Privacy-Preserving Instructions for Aligning Large Language Models
Authors: Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In both supervised fine-tuning and reinforcement learning from human feedback, our extensive experiments demonstrate the high utility of the final set of synthetic instructions by showing comparable results to real instructions. |
| Researcher Affiliation | Collaboration | Da Yu Sun Yat-sen University, the work was done when Da Yu was an intern at Google Research. EMAIL. Google Research, alphabetical order. EMAIL. |
| Pseudocode | Yes | Algorithm 1 Train DP Generator to Synthesize Instructions. Algorithm 2 Resample Synthetic Data with DP Histogram. |
| Open Source Code | Yes | The code to reproduce the main findings in our paper is available at: https://github.com/google-research/google-research/tree/master/dp_instructions. |
| Open Datasets | Yes | We use the LMSYS-Chat-1M dataset as the private dataset (Zheng et al., 2023a)...For this, we take instructions from the FLAN dataset (Chung et al., 2022; Longpre et al., 2023). |
| Dataset Splits | Yes | The Chatbot Arena instructions are then divided into three subsets: 180,000 for the training set, 5,000 for the validation set, and the rest for the test set. |
| Hardware Specification | Yes | Our experiments use 32 Nvidia A100 40G GPUs. |
| Software Dependencies | No | The paper mentions several software components and libraries (e.g., LLaMA models, LoRA, DP-Adam, Adam optimizer, Sentence-T5-base, Faiss package, GPT-3.5-Turbo, GPT-4, phi-1.5, TRL library) but does not provide specific version numbers for them. |
| Experiment Setup | Yes | Our hyperparameter sweeps for DP fine-tuning and non-private fine-tuning are outlined in Table 9 and Table 10, respectively...The KL penalty coefficient is set as 0.2 and the number of optimisation steps per batch is 4. We use a batchsize of 128 and a learning rate of 1.41 × 10−5. |