If thought corrupts language, language can also corrupt thought.
- George Orwell
Our LLM Alignment Framework
Leverage the Alignment framework to de risk and secure the outputs of generative AI.
What's a reward model?
Large Language Model guardrails keep LLMs within strict parameters the business can control and monitor.
Risk appropriate control over your LLM.
Adjust controls depending on the use-case and context of your model deployment. Risk appetites will differ between customer facing tools and the internal tools.
Enable key internal stakeholders to grasp how AI language models interpret knowledge and form responses.
Well managed risk will promote trust, stakeholder confidence and user adoption.
Reliability and Compliance
Meet stringent compliance requirements.
Advai Guardrails come complete with end-to-end documentation that highlights the rigorous robustness assurance methods employed.
This acts as a safety net against regulatory challenges and assures that your organisation's AI operations are both safe and compliant.
With Advai's robust alignment and testing, enjoy peace of mind knowing that your LLM will function as intended in various scenarios.
Keep your LLM guardrails up-to-date.
As a cutting edge field, novel methods to control LLMs are discovered every week.
Our team of researchers and ML engineers keep your LLM guardrails updated.
Adversarial attack methods are released almost weekly. Keeping on top of novel attack vectors will reduce the chance of your business saying something it will regret.
Deploy faster with confidence
Meet the competitive pressure to deploy without undue risk.
Assurance needs to come first, not last. The faster you can assure your system, the faster you can deploy.
Ensure the reliability of your Large Language Models (LLMs) with our comprehensive robustness assessments.
Win the confidence of key stakeholders using empirical methods to demonstrate that your model is fit for purpose.
1Language Model Agnostic
Our adversarial attacks have been optimised across multiple models with different LLM architectures, therefore having relevance to a broader landscape of verification methods. We have demonstrated that this enables us to successfully conduct “one shot” attacks on multiple unrelated systems.
2Risk-Appropriate Control Over LLMs
We enable businesses to fine-tune and control Large Language Models (LLMs) to align with organisational risk appetites and operational requirements.
3Reward Model Driven
Our approach places a heavy emphasis on ensuring the quality of reward models used in LLM fine-tuning. We counter this by using algorithmically optimised suffix attacks (see more below).
4Adversarial Attack Vectors Reveal Vulnerabilities
We carry out advanced self-optimising suffix attacks to discover out-of-sample attack vectors (unfamiliar strings of text input) that reveal novel methods of bypassing guardrails and manipulating LLMs to perform undesirably. This reveals vulnerabilities to address.
‘Low-hanging fruit’ integration: Address high impact & ‘best-for-LLM’ tasks, then expand and advance in complexity.
Recognise weaknesses of AI: targeted human in-the-loop.
- Team: PM, CTO, ML Engineer, 2 x Data Scientists
- Objective: Target high-impact implementations
- Deliverable: LLM-enabled service with a simple UI
Industry state-of-the-art approach: Plan and integrate on modular basis, expecting the tech to rapidly evolve.
Collaboration: ensure your team understands what is going on, and that analysts understand responsibilities and opportunities.
- Cost: Tailored to client needs and Phase 1 findings
- Objective: Modular and prioritised implementations
- Deliverables: Proof-of-Concept, further discovery, and continuous updates