Summary of Trust-oriented Adaptive Guardrails For Large Language Models, by Jinwei Hu et al.
Trust-Oriented Adaptive Guardrails for Large Language Models
by Jinwei Hu, Yi Dong, Xiaowei Huang
First submitted to arxiv on: 16 Aug 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 The paper introduces an adaptive guardrail mechanism to ensure large language models align with human values by moderating harmful or toxic responses. The proposed approach combines trust modeling and online in-context learning via retrieval-augmented generation to dynamically moderate access to sensitive content based on user trust metrics. The system uses a novel combination of direct interaction trust and authority-verified trust to tailor the strictness of content moderation to the user’s credibility and context. Empirical evaluation shows the adaptive guardrail outperforms existing approaches while securing sensitive information and managing hazardous content through a context-aware knowledge base. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make sure big language models don’t say bad things by using a system that learns from people. The system uses two types of trust: how well someone knows you, and if they’re an expert. It then uses this information to decide what kind of content is safe for each person to see. This helps keep sensitive information private while also making sure the language model doesn’t say anything bad. |
Keywords
» Artificial intelligence » Knowledge base » Language model » Retrieval augmented generation
