Haize Labs, a research lab specialiced in AI safety, has launched the Haizing Suite, a comprehensive platform designed to rigorously test large language models (LLMs). The tool uses a method called “haizing” to identify and fix potential faults before they can affect real-world applications.
The company has already several partnerships with industry leaders such as Anthropic, AI21, Hugging Face, UK AISI, and MLCommons.
Evaluating LLMs
Despite the rapid advancements in AI technology, systems still struggle with issues like generating harmful content or disseminating false information. Haize Labs has been investigating these problems for the past five years, long before the recent surge in interest in generative AI. Evaluating LLMs poses significant challenges due to their randomness and lack of true reasoning abilities, with traditional testing methods often falling short. Haize Labs addresses this gap by adapting proven techniques from software and hardware verification to create more thorough testing protocols.
Haizing combines aspects of fuzz testing and red-teaming to scrutinize AI systems extensively. It utilizes a variety of test scenarios to uncover potential failure points, guided by an “anti-constitution” that defines undesirable behaviors. The Haizing Suite incorporates multiple algorithms refined through rigorous research, using strategies such as gradient-guided searches, evolutionary programming, and reinforcement learning to detect problematic inputs. While Haize Labs offers several open-source tools, its commercial versions provide enhanced capabilities.
Haizing as a Flexible Approach
The adaptability of haizing makes it suitable for any application, industry, or failure type. The anti-constitution can be expressed in natural language, allowing for easy customization, which broadens the Haizing Suite’s applicability in ensuring AI system reliability across various sectors.
According to Haize Labs, the company focuses on algorithmically testing and red-teaming LLMs to ensure their reliability and robustness, thereby mitigating risks in high-stakes environments. The Haizing Suite’s automated, scalable, and customizable tools proactively identify all potential LLM failures. Key features include automated testing without the need for human intervention, adaptive capabilities to expose undesirable behaviors, cost-effective synthesis of each input, and comprehensive coverage across input scenarios.
Early Partners
Haize Labs says to have seen substantial demand from early users, including leading industry labs and AI developers. The suite is considered crucial for defining robustness standards for LLMs and ensuring their reliability in deployment.
The founding team comprises AI researchers and engineers with specialized backgrounds, including authorship of numerous machine learning papers, development of ML-guided services, and work at the Allen Institute of AI. Advisors like Graham Neubig provide expertise in LLM evaluation to Haize Labs.
$30k pilot with Anthropic
Primary clients of Haize Labs include LLM providers, industry labs, research organizations, and governments, who use the firm’s red-teaming tools to test safety classifiers and set safety benchmarks. Notable collaborations include a $30k pilot with Anthropic, mid five-figure agreements with AI21, and partnerships with Hugging Face, UK AISI, and MLCommons. Additionally, Haize Labs has a $500k letter of intent with Scale AI and targets domain-specific stress-testing of LLMs in healthcare and finance sectors, re-evaluating models with each update to maintain robustness.
Haize Labs has secured approximately $1.05 million in funding from prominent angel investors, including founders and senior executives from Okta, Hugging Face, Weights and Biases, and Netflix, as well as early investors in companies like Cruise and Ramp.
Last Updated on November 18, 2024 11:33 am CET
