Talking with the Latents -- how to convert your LLM into an astronomer

Understand

Recent advances in Large Language Models (LLMs) offer unique opportunities for scientific tasks, yet their ability to reason over complex numerical data remains largely unexplored.

• We propose a simple mechanism to introduce domain-specific physical knowledge into LLMs by fusing pre-trained latent physical features with a pre-trained language model.
• Our method employs a teacher-student knowledge distillation framework where a large LLM (teacher) generates synthetic question-answer supervision to transfer physical reasoning to a smaller LLM (student).
• The student is conditioned on latent physical features and trained via a lightweight adapter and Low-Rank Adaptation (LoRA).