What is SFT data and what role does it play in state-of-the-art LLMs? Supervised finetuning (SFT) in the context of RLHF deals with further tuning an initial language model using demonstration data. At Surge AI, we provide SFT data for top LLM teams to finetune their LLMs. Here is what we have observed: SFT data typically involves collecting demonstration data including prompts and in-depth responses written by human annotators demonstrating how the model should respond to the prompt. Specifically, You take a set of commands and obtain human-written responses for each. The SFT training dataset consists of <prompt, ideal generation> pairs used to finetune the pre-trained LLM to output human-like responses. So let’s say you are aligning an LLM-powered dialogue system then you need to collect dialogue-style instructions/responses data that cater to that use case. Similarly, as shown in the figure, if you want high-quality code generation capabilities you can also provide instruction + written responses as part of the SFT data. This leads to the first important component, also referred to as supervised policy, for training an RLHF LLM. But why go through all this process when training RLHF LLMs? The core idea of SFT is to provide a high-quality initialization for the RLHF process. It’s widely applied by some of the most advanced closed and open-sourced LLMs. To make this work, you need to collect lots of demonstration data but the challenge is collecting high-quality and diverse demonstration data at scale. SFT data can be written by different annotators and can incorporate a lot of noise as response quality and style can vary from annotator to annotator. Controlling for this is key. According to reported insights, you will need to collect thousands of examples to ensure you are tuning a high-quality LLM. SFT data helps to improve target areas that allow steering the LLM better to your needs. We can help with your SFT data needs! If you need help with collecting high-quality preference or SFT data, reach out to our team here: https://t.co/OSm4aHIOP6

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

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