NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails
원문 초록
NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems. Guardrails (or rails for short) are a specific way of controlling the output of an LLM, such as not talking about topics considered harmful, following a predefined dialogue path, using a particular language style, and more. There are several mechanisms that allow LLM providers and developers to add guardrails that are embedded into a specific model at training, e.g. using model alignment. Differently, using a runtime inspired from dialogue management, NeMo Guardrails allows developers to add programmable rails to LLM applications - these are user-defined, independent of the underlying LLM, and interpretable. Our initial results show that the proposed approach can be used with several LLM providers to develop controllable and safe LLM applications using programmable rails.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Developing a Chatbot system using Deep Learning based for Universities consultancy
skfolio: Portfolio Optimization in Python
100-Day Analysis of USD/IDR Exchange Rate Dynamics Around the 2025 U.S. Presidential Inauguration
Automated Risk Management Mechanisms in DeFi Lending Protocols: A Crosschain Comparative Analysis of Aave and Compound
Semantic Scheduling for LLM Inference
SDialog: A Python Toolkit for Synthetic Dialogue Generation and Analysis
토론
이 논문과 연결된 포럼 글이 아직 없습니다.