Summary of Chasing Random: Instruction Selection Strategies Fail to Generalize, by Harshita Diddee et al.
Chasing Random: Instruction Selection Strategies Fail to Generalize
by Harshita Diddee, Daphne Ippolito
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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 A novel study investigates the generalizability of instruction-tuning methods for language models across diverse datasets and evaluation benchmarks. Prior research has shown that fine-tuning language models with high-quality instructions accelerates development, but the performance of these methods is not well-established due to varying experimental setups. This work analyzes popular selection strategies and their cost-performance trade-offs, revealing that they often fail to consistently outperform random baselines and can exceed the cost of fine-tuning on the full dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how language models are trained using instructions from different sources and evaluates their performance across various datasets. Researchers found that some methods work better than others, but many don’t do much better than just picking random data or training on all the data. This means that it might not be worth spending extra time and resources to select good data when you can just use all of it. |
Keywords
» Artificial intelligence » Fine tuning » Instruction tuning
