Summary of Ensuring Safe and High-quality Outputs: a Guideline Library Approach For Language Models, by Yi Luo et al.
Ensuring Safe and High-Quality Outputs: A Guideline Library Approach for Language Models
by Yi Luo, Zhenghao Lin, Yuhao Zhang, Jiashuo Sun, Chen Lin, Chengjin Xu, Xiangdong Su, Yelong Shen, Jian Guo, Yeyun Gong
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed Guide-Align approach is a two-stage solution to ensure the safe and high-quality outputs of Large Language Models (LLMs). Initially, a safety-trained model identifies potential risks and formulates specific guidelines for various inputs. This library of guidelines is then used by a retrieval model to guide LLMs in response generation. The optional fine-tuning stage incorporates well-aligned datasets generated through the process, enhancing the comprehensiveness and fine-grainedness of the guideline library. The approach customizes guidelines to accommodate diverse inputs and incorporates safety expertise from a safety-trained LLM through a lightweight retrieval model. Evaluation on three benchmarks demonstrates significant improvements in LLM security and quality, with the fine-tuned model Labrador outperforming GPT-3.5-turbo and surpassing GPT-4 in alignment capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can generate biased content and have privacy issues. A new approach called Guide-Align helps make sure these models produce safe and good outputs. It uses two stages to do this: first, a safety-trained model looks for potential risks and makes specific rules for different inputs. Then, another model uses these rules to guide the LLM in what it says. This process can be fine-tuned with datasets that are already well-aligned. The approach helps make sure guidelines are customized for different inputs and incorporates expert advice from a safety-trained LLM. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Gpt
