Summary of Show Me How It’s Done: the Role Of Explanations in Fine-tuning Language Models, by Mohamad Ballout et al.
Show Me How It’s Done: The Role of Explanations in Fine-Tuning Language Models
by Mohamad Ballout, Ulf Krumnack, Gunther Heidemann, Kai-Uwe Kuehnberger
First submitted to arxiv on: 12 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 This study investigates the impact of fine-tuning with explanations on the performance of language models. Unlike prompting, which fixes the model’s parameters, fine-tuning enables the model to learn and update its parameters during a training phase. The researchers applied this approach to various-sized language models using data containing explanations rather than answers. They found that even smaller models with 60 million parameters benefited substantially from fine-tuning with explanations. Interestingly, the results showed that detailed explanations were more beneficial for smaller models, while larger models gained nearly equal advantages regardless of explanation length. The study also demonstrates that including explanations enables models to solve tasks they couldn’t previously, and argues that adding explanations reduces data volume requirements and promotes better generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research shows how using explanations when training language models makes them much better at understanding and doing tasks. Unlike other methods, fine-tuning lets the model learn and change its own rules during training. The scientists tested this on different-sized language models with explanations instead of just answers. They found that even small models got a big boost from using explanations. What’s cool is that small models loved detailed explanations, while bigger models didn’t mind what kind of explanation they got. This means that adding explanations helps smaller models do tasks they couldn’t before. |
Keywords
* Artificial intelligence * Fine tuning * Generalization * Prompting
