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..
The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
Authors: Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V Le, Barret Zoph, Jason Wei, Adam Roberts
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through careful ablation studies on the Flan Collection of tasks and methods, we tease apart the effect of design decisions which enable Flan-T5 to outperform prior work by 3-17%+ across evaluation settings. |
| Researcher Affiliation | Collaboration | 1Media Lab, Massachusetts Institute of Technology, Cambridge, USA 2Google, Mountain View, USA. Correspondence to: Shayne Longpre <EMAIL>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1Data generation code available at: https://github. com/google-research/FLAN/tree/main/flan/v2. Generation code allows users to vary mixtures rates, templates, prompt types and data augmentations techniques, for faster public research. |
| Open Datasets | Yes | to accelerate research on instruction tuning, we make the Flan 2022 collection of datasets, templates, and methods publicly available. |
| Dataset Splits | Yes | We evaluate on (a) a suite of 8 Held-In tasks represented within the 1800+ training task collection (4 question answering and 4 natural language inference validation sets), (b) Chain-of-Thought (Co T) tasks (5 validation sets), and (c) the MMLU (Hendrycks et al., 2020) and BBH (Suzgun et al., 2022) benchmarks as our set of Held-Out tasks |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software components with their version numbers (e.g., Python, PyTorch, CUDA, or specialized solvers with versions) that would be needed to replicate the experiment environment. |
| Experiment Setup | Yes | For single-task finetuning, described in Section 4, our models are finetuned for 100,000 steps for all tasks. We use a constant learning rate of 0.001, a dropout probability of 0.1, and a batch size of 128 length-512 sequences. We save a checkpoint every 20 steps and report test performance on the model checkpoint corresponding to the highest validation performance. |