Summary of Delia: Diversity-enhanced Learning For Instruction Adaptation in Large Language Models, by Yuanhao Zeng et al.
DELIA: Diversity-Enhanced Learning for Instruction Adaptation in Large Language Models
by Yuanhao Zeng, Fei Ren, Xinpeng Zhou, Yihang Wang, Yingxia Shao
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| 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 paper explores the limitations of instruction tuning in Large Language Models (LLMs) and proposes a novel approach to overcome these limitations. Instruction tuning is widely used to adjust the behavior of LLMs, but empirical evidence suggests that it primarily adjusts the model’s behavior to fit specific task formats rather than acquiring new knowledge or capabilities. The authors argue that this limitation stems from biased features learned during instruction tuning, which differ from ideal task-specific features. They propose a novel data synthesis method called DELIA (Diversity-Enhanced Learning for Instruction Adaptation) that leverages the buffering effect of diverse data to transform biased features into approximations of ideal features. The authors demonstrate the effectiveness of DELIA through experiments on Icelandic-English translation and formatted text generation, achieving improved performance compared to common instruction tuning and other baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how Large Language Models (LLMs) learn new skills. Currently, people use something called “instruction tuning” to help LLMs learn new things. But the problem is that this method only makes the model better at doing specific tasks, rather than learning new knowledge or abilities. The authors of this paper think that this limitation comes from the way the model learns features, which are not ideal for understanding language. They propose a new approach called DELIA to help LLMs learn in a more effective way. This method uses a lot of diverse data to help the model learn better features. The authors tested their approach and found that it performed better than other methods. |
Keywords
» Artificial intelligence » Instruction tuning » Text generation » Translation
