Summary of Divide-verify-refine: Can Llms Self-align with Complex Instructions?, by Xianren Zhang et al.
Divide-Verify-Refine: Can LLMs Self-Align with Complex Instructions?
by Xianren Zhang, Xianfeng Tang, Hui Liu, Zongyu Wu, Qi He, Dongwon Lee, Suhang Wang
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 proposes an alternative approach to fine-tuning large language models (LLMs) for better constraint adherence in complex instructions. Existing methods rely heavily on fine-tuning data quality, which can be labor-intensive and expensive. The Divide-Verify-Refine (DVR) framework addresses this challenge by dividing complex instructions into single constraints, verifying responses using rigorous tools, and refining them through dynamic few-shot prompting. This approach doubles the constraint adherence of Llama3.1-8B and triples Mistral-7B’s performance on a new dataset of complex instructions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about making language models better at following rules in long instructions. Right now, these models can struggle to understand what they should do if the instruction has multiple rules that need to be followed. The researchers propose a way to help these models by breaking down the instruction into smaller parts and checking each part against a set of rules. This makes it easier for the model to correct its mistakes and follow the rules correctly. The new approach leads to better results than previous methods. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Prompting
