Supercharging LLM Classifications with Logprobs
I was just reading the classification chapter of Jay Alammar and Maarten Grootendorst's excellent book Hands-On Large Language Models. I felt inspired to extend their work and show yet another cool trick you can do with LLM-based text classification. In their work they demonstrated how an LLM can be used as a "hard classifier" to determine the sentiment of movie reviews. By "hard" I mean that it gives a concrete answer, "positive" or "negative". However, we can do one better! Using "this one simple trick"™ we can make a "soft" classifier that returns the probabilities of each class rather than a concrete single choice. This makes it possible to tune the classifier – you can set a threshold in the probabilities so that classifications are optimally aligned with a training set.
