Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains
Junhong Shen Neil Tenenholtz James Brian Hall David Alvarez-Melis Nicolo Fusi
Large Language Models (LLMs) have demonstrated remarkable proficiency in
understanding and generating natural language. However, their capabilities wane
in highly specialized domains underrepresented in the pretraining corpus, such
as physical and biomedical sciences. This work explores how to repurpose
general LLMs into effective task solvers for specialized domains. We introduce
a novel, model-agnostic framework for learning custom input tags, which are
parameterized as continuous vectors appended to the LLM's embedding layer, to
condition the LLM. We design two types of input tags: domain tags are used to
delimit specialized representations (e.g., chemical formulas) and provide
domain-relevant context; function tags are used to represent specific functions
(e.g., predicting molecular properties) and compress function-solving
instructions. We develop a three-stage protocol to learn these tags using
auxiliary data and domain knowledge. By explicitly disentangling task domains
from task functions, our method enables zero-shot generalization to unseen
problems through diverse combinations of the input tags. It also boosts LLM's
performance in various specialized domains, such as predicting protein or
chemical properties and modeling drug-target interactions, outperforming expert
models tailored to these tasks.