Summary of Do Llms “know” Internally When They Follow Instructions?, by Juyeon Heo et al.

Do LLMs “know” internally when they follow instructions?

by Juyeon Heo, Christina Heinze-Deml, Oussama Elachqar, Shirley Ren, Udhay Nallasamy, Andy Miller, Kwan Ho Ryan Chan, Jaya Narain

First submitted to arxiv on: 18 Oct 2024

GrooveSquid.com Paper Summaries

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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 The paper investigates how large language models (LLMs) can be improved to follow user-provided instructions accurately and efficiently. The authors analyze the internal states of LLMs and discover a specific dimension in the input embedding space that is linked to successful instruction-following. They demonstrate that modifying representations along this dimension leads to higher success rates without compromising response quality. Furthermore, they find that this dimension is more closely related to prompt phrasing rather than task difficulty or instructions, which explains why LLMs sometimes fail to follow clear instructions and why prompt engineering can be effective. The research provides valuable insights into the internal workings of LLMs’ instruction-following capabilities, paving the way for reliable AI agents.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Large language models are super smart computers that can understand and generate human-like text. But they often struggle to follow simple rules and instructions. Scientists want to improve this “instruction-following” ability so AI agents can work better with humans. By studying how these models think, researchers found a special way to make them more accurate without sacrificing their language skills. They also discovered that the way you phrase your request matters more than the difficulty of the task or the instructions themselves. This new understanding can help us build more reliable and helpful AI assistants.