What is a hallucination?
Hallucination refers to the phenomenon where a large language model generates outputs that are factually incorrect or nonsensical, despite appearing confident and plausible.
Why do large language models hallucinate?
LLMs hallucinate due to numerous factors, including bias and limitations in training data, lack of real-world understanding, and the statistical nature of language modeling.
How can one detect and remove hallucinations?
Detecting and mitigating hallucinations is an active area of research. Some approaches include:
Fact-checking against external knowledge bases: Verifying the generated text against trusted sources.
Training LLMs to be more aware of their limitations: Teaching models to identify and flag potentially unreliable outputs.
Using human-in-the-loop systems: Combining LLM outputs with human review and verification.
Simbian utilizes TrustedLLM™, a system designed to enhance the safety and reliability of LLMs, mitigating risks associated with hallucinations in security automation.
Is my data used to train AI Agents?
Whether or not your data is used to train an AI Agent depends on your technology provider. Providers may leverage anonymized customer data to help keep the AI Agent trained and informed of the latest threats. Like sharing threat intelligence via an ISAC, sharing anonymized information across users can increase the collective defense of anyone using that AI Agent. Any usage of your data to train an AI Agent should be made clear in the platform's licensing agreement and privacy policy.
What are my options if I wanted a private AI Agent?
Depending on your provider, you may be able to request an isolated environment for your AI Agents and opt-out of any data sharing.
What do we provide?
Simbian provides a platform of AI Agents to automate cybersecurity.