@HelloSurgeAI
Making LLMs reliable is a tough task. But this is where a lot of the LLM research and development work is focused. Let's take a look at how LLMs are made reliable: At Surge AI, we work with the top AI companies to improve LLM reliability. This effort is essential to enable wider applicability in even higher-stakes domains. Reliability not only focuses on getting models to output what users want in terms of specifics and quality but also ensuring that no unwanted output (e.g., toxic content) is produced by the model. A lot of the current efforts to increase reliability focus on ad-hoc approaches and prompt engineering. More recently, there have been more efforts to develop a more systematic framework to improve reliability while training models. This has led to a lot of interest in red teaming. Red teaming deals with identifying risks in LLMs through adversarial prompting. It has been applied not only to general-purpose LLMs like Claude and ChatGPT but also to more recent code LLMs like Llama Code. The challenge with red teaming is that, if not done right, it can lead to LLMs over-refusing and potentially leading to a bad user experience. In addition, the reality is that red teaming requires deep expertise in working with LLMs. We deeply believe that in order to make LLMs safer, useful, and more reliable, comprehensive red teaming is critical. But you don't need to hear this from us. Many large LLM companies have also publicly expressed huge interest in red teaming. If you are looking for deep expertise in training LLMs and red teaming, reach out to learn how our world-class team can help: https://t.co/iFBrffKYKT
