Equity for Early Employees in Early Stage Startups: Aligning Language Models to Follow Instructions

Equity for Early Employees in Early Stage Startups: Aligning Language Models to Follow Instructions

When it comes to early stage startups, one of the most crucial aspects is attracting and retaining the right talent. For your first key hires, whether it's three, five, or even ten employees, there is no one-size-fits-all formula to entice them. Convincing someone to join your dream before it has fully materialized requires a delicate blend of art and persuasion. However, there is one common thread that runs through successful startups - equity.

Equity, in terms of ownership, emotional attachment, responsibility, and understanding of the startup process, is what makes early employees feel like founders. It is this sense of ownership and shared vision that drives them to go above and beyond their job description. The more aligned they are with the company's goals, the greater the chances of success for the startup.

But how can startups ensure that their employees truly feel like founders? This is where aligning language models to follow instructions comes into play. Language models, such as GPT-3, are incredibly powerful tools that can generate human-like text. However, they often lack the ability to understand and follow instructions accurately.

Enter InstructGPT, a language model that has been specifically fine-tuned to follow instructions. InstructGPT models have shown significant improvements in their ability to adhere to prompts compared to GPT-3. They are less likely to make up facts and exhibit lower levels of toxic output generation. This is achieved through reinforcement learning from human feedback (RLHF), a technique that helps align the model with user intentions.

Interestingly, despite having over 100 times fewer parameters, InstructGPT models outperform GPT-3 models according to human evaluators. This suggests that size does not necessarily equate to better performance. By using curated information, such as that available on platforms like Glasp, the quality of outputs can be further enhanced.

However, it's important to note that even with these advancements, InstructGPT models are far from perfect. They can still generate biased or toxic outputs, make up facts, and produce explicit content without explicit prompting. The challenge lies in refining these models to refuse certain instructions, ensuring they are safe and reliable.

Another aspect that requires attention is the cultural bias inherent in language models. Currently, InstructGPT is primarily trained to follow instructions in English, making it biased towards the cultural values of English-speaking populations. To address this issue, research is being conducted to understand the preferences of different populations and condition the models accordingly.