Summary of Rnr: Teaching Large Language Models to Follow Roles and Rules, by Kuan Wang et al.
RNR: Teaching Large Language Models to Follow Roles and Rules
by Kuan Wang, Alexander Bukharin, Haoming Jiang, Qingyu Yin, Zhengyang Wang, Tuo Zhao, Jingbo Shang, Chao Zhang, Bing Yin, Xian Li, Jianshu Chen, Shiyang Li
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 a novel approach to improve the ability of large language models (LLMs) to follow complex system prompts, which is crucial for safe interaction with users. The existing IFT models can only follow user-provided instructions and often fail to adhere to developer-defined guidelines. To address this limitation, the authors introduce , an automated data generation pipeline that generates diverse roles and rules from existing IFT instructions along with corresponding responses. This dataset can be used to train LLMs that effectively follow complex system prompts. The proposed framework is evaluated on newly created benchmarks for role and rule following ability, as well as standard instruction-following benchmarks and general NLP tasks. The results show a significant improvement in role and rule following capability, with over 25% increase in pass-rate on rule adherence in experiments using the Alpaca and Ultrachat datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps large language models (LLMs) better follow instructions from developers. Right now, these models can only do what people tell them to do, but they often get confused when following complex rules set by developers. To fix this problem, the researchers created a new way to generate data that LLMs can use to learn how to follow these complex rules. They tested their method on special datasets and showed that it works well. The results are exciting because they could lead to safer interactions between humans and computers. |
Keywords
» Artificial intelligence » Nlp
