Turn Your LLM into a Calibrated Classifier for $2 (fireworks.ai)

A recent breakthrough in tuning large language models (LLMs) reveals a cost-effective method for transforming them into calibrated classifiers, necessitating only about $2 in compute costs. Traditionally focused on free-form text generation, LLMs can be repurposed for classification tasks by mapping classes to tokens and utilizing the model’s original probability outputs. This approach maintains the model architecture and avoids extensive retraining while allowing natural conversion of token probabilities into class probabilities, essential for applications like event prediction and ranking. The method leverages fine-tuning on existing classification tasks to ensure that the probabilities are well-calibrated, aligning closely with real-world likelihoods. By directly mapping class labels to tokens, and through effective training methods, the next-token probability distribution can be adjusted to reflect accurate class probabilities without complex renormalization processes. The empirical validation using the AG News dataset demonstrated that calibration converges naturally under a standard fine-tuning setup, presenting a simple and efficient pathway for deploying classification capabilities based on LLMs, especially on platforms like Fireworks AI that recognize and expose token-based probabilities.