This article briefly explains what language models are and how small players in this exciting space build sustainable products that can survive the competition.
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There’s been a lot of chatter around Chat GPT. In this article, I’m briefly explaining what language models are, how Chat GPT differs from other language models, and how small players coming into this exciting space build sustainable products that can survive the competition.
Language modeling is at the core of Natural language processing, trying to predict the probability of the following sequence of words given the past sequence of words in a text. E.g., The model tries to learn that the word „blue“ is more likely to follow the sequence of words „The water is clear and the sky is <>“ than the words red/green/ocean, etc. Suppose the model can learn the next sequence of words/sentences/characters. In that case, this model can be used in various tasks like Autocomplete, speech recognition, machine translation, text generation, etc., along with other classification models.
Here’s a good primer on What language models are. In this article, I want to focus on what the recent advancements in Large language model research mean for Startups and engineers building out their companies in this space.
Open AI launched the ChatGPT demo in December. Deep learning-based language models are not entirely new. There are at least 80 different language models released over the past six months, differing in how they are trained, what they are trained on, and the task they are optimized for. What makes ChatGPT unique is that it uses Reinforcement learning with human feedback in training the language model. This article does a great job of explaining how RLHF was used to train ChatGPT. Let me touch up on that briefly.
Now that we have seen how ChatGPT was trained, let me talk about the main challenges.
LLM models are incredibly resource intensive to train and run inference on. From the above process, you can imagine that this model has billions of parameters and also requires tons of human raters to keep bettering the model.
