What development of LLM best practices means for the enterprise

Large language models (LLMs) and multimodal AI are the cutting edge of AI innovation, with applications trickling down to the enterprise from the ‘Googles’ and ‘OpenAIs’ of the world. We are currently seeing a barrage of LLM and multimodal AI model announcements, as well as commercial applications created around them.

LLMs power applications that range from code creation to customer feedback. At the same time, they are driving multimodal AI and fueling the debate around the limits and use of AI. In 2019, GPT-2 was deemed “too dangerous to release” by OpenAI. Today, models far more powerful than GPT-2 are being released. Either way, the evaluation feels arbitrary. However, yesterday, a first step toward industry-wide best practices for AI language model deployment may have been taken.

Cohere, OpenAI and AI21 Labs have collaborated on a preliminary set of best practices applicable to any organization developing or deploying LLMs. The trio is recommending key principles to help providers of LLMs mitigate the risks of this technology in order to achieve its full promise to augment human capabilities.

The move has garnered support from Anthropic, the Center for Security and Emerging Technology, Google Cloud Platform and the Stanford Center for Research on Foundation Models. AI21 Labs, Anthropic, Cohere, Google and OpenAI are actively developing LLMs commercially, so the endorsement of these best practices may indicate the emergence of some sort of consensus around their deployment.

The joint recommendation for language model deployment is centered around the principles of prohibiting misuse, mitigating unintentional harm and thoughtfully collaborating with stakeholders.

Read the full article on VentureBeat

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