Summary of Stronger Models Are Not Stronger Teachers For Instruction Tuning, by Zhangchen Xu et al.
Stronger Models are NOT Stronger Teachers for Instruction Tuning
by Zhangchen Xu, Fengqing Jiang, Luyao Niu, Bill Yuchen Lin, Radha Poovendran
First submitted to arxiv on: 11 Nov 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 research paper presents a challenge to the common assumption in instruction tuning for large language models (LLMs). The authors argue that larger and stronger models are not necessarily better teachers, but instead propose a novel approach based on a new metric called Compatibility-Adjusted Reward (CAR) to measure the effectiveness of response generators. They demonstrate that CAR outperforms traditional metrics across five base models. This research has implications for improving the instruction-following capabilities of LLMs and highlights the importance of considering the compatibility between teachers and base models being fine-tuned. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper questions a widely-held assumption in teaching large language models. It shows that bigger models aren’t always better at helping smaller models learn to follow instructions. Instead, the authors propose a new way to measure how well response generators are doing their job. They test this approach and find it works better than usual methods. This research is important because it could help make large language models even more useful for tasks like answering questions or generating text. |
Keywords
» Artificial intelligence » Instruction tuning
