Authors: Yi Dong, Ronghui Mu, Gaojie Jin, Yi Qi, Jinwei Hu, Xingyu Zhao, Jie Meng, Wenjie Ruan, Xiaowei Huang

Published on: February 02, 2024

Impact Score: 8.2

Arxiv code: Arxiv:2402.01822

Summary

• What is new: This paper introduces a systematic approach to constructing guardrails for LLMs, advocating for socio-technical methods and advanced neural-symbolic implementations.
• Why this is important: The integration of Large Language Models (LLMs) into daily life poses risks that need mitigation to prevent profound impacts on users and societies.
• What the research proposes: A systematic construction of guardrails through multi-disciplinary collaboration, focusing on socio-technical methods and neural-symbolic implementations.
• Results: Proposed methods aim to provide a comprehensive framework for safer LLM applications, although specific outcome metrics are not provided.

Technical Details

Technological frameworks used: Socio-technical frameworks, neural-symbolic implementations

Models used: Llama Guard, Nvidia NeMo, Guardrails AI

Data used: Previous research evidence

Potential Impact

This research could disrupt markets related to content moderation, social media platforms, customer service automation, and companies producing or deploying LLMs.

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