"Some scientists are building LLMs that can act as guardrails. Yes, adding one LLM to fix the problems of another one seems like doubling the potential for trouble, but there's an underlying logic to it. These new models are specially trained to recognize when an LLM is potentially going off the rails. If they don't like how an interaction is going, they have the power to stop it."
"For every project that needs guardrails, there's another one where the guardrails just get in the way. Some projects demand an LLM that returns the complete, unvarnished truth. For these situations, developers are creating unfettered LLMs that can interact without reservation. Some of these solutions are based on entirely new models while others remove or reduce the guardrails built into popular open source LLMs."
"IBM built the Granite Guardian model and framework combination as a protective filter for common errors in AI pipelines. First, the model scans for prompts that might contain or lead to answers that include undesirable content (hate, violence, profanity, etc.). Second, it watches for attempts to evade barriers by hoodwinking the LLM. Third, it watches for poor or irrelevant d"
The landscape of large language models has diversified to accommodate different safety requirements. Some developers build LLMs specifically designed to act as guardrails, monitoring other models for potentially problematic outputs and stopping interactions when necessary. Conversely, other projects require unrestricted LLMs that provide unfiltered information without safety constraints. This has led to two distinct categories of models: heavily guarded versions emphasizing AI safety across multiple dimensions, including detection of harmful content, evasion attempts, and poor outputs; and unfettered models that operate without reservation. The choice between these approaches depends on specific project requirements and use cases.
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