Summary of Instruction Diversity Drives Generalization to Unseen Tasks, by Dylan Zhang et al.
Instruction Diversity Drives Generalization To Unseen Tasks
by Dylan Zhang, Justin Wang, Francois Charton
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers investigate how fine-tuning large language models (LLMs) on pairs of instructions and desired outcomes can enable them to perform real-world tasks and follow human instructions. They experiment with string rewrites, a symbolic task that allows for control over “inputs” and “instructions”, to understand the factors that determine model generalization to unseen tasks. The results show that the diversity of the instruction set determines generalization, emerging once a diverse enough set is provided even with few examples per task. This approach also ensures robustness against non-uniform distributions of instructions in the training set. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be fine-tuned to perform real-world tasks and follow human instructions by learning pairs of instructions and desired outcomes. The researchers wanted to understand what makes this work, so they used a special kind of task called string rewrites. They found that if they provide many different sets of instructions for the model to learn from, it will be able to do new tasks even with very few examples. This is important because it means we can teach machines to follow human instructions without needing a huge amount of data. |
Keywords
* Artificial intelligence * Fine tuning * Generalization
