Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains
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.
65c59478ed68e7aafdc698af
Junhong Shen
admin_assigned
2024-02-09T10:31:25.565Z
6532e347b66f4bf689cf269a
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Junhong Shen
user
sjunhongs
65c59478ed68e7aafdc698b0
Neil Tenenholtz
admin_assigned
2024-02-09T10:31:32.779Z
64edebfb562a2701cc286897
/avatars/537bd273bbb5225779b2329acf82b104.svg
Neil Tenenholtz
user
neilatmsft
65c59478ed68e7aafdc698b1
James Brian Hall
65c59478ed68e7aafdc698b2
David Alvarez-Melis
admin_assigned
2024-02-09T10:32:06.466Z
652ec11d0af36fdc872f7ba8
/avatars/9e7c5e592cd76ab8ea9475f395e326d0.svg
David Alvarez Melis
user
daalvare
65c59478ed68e7aafdc698b3
Nicolo Fusi
admin_assigned
2024-02-09T10:32:12.409Z
64f29de4f40f35cfa3e8618d
/avatars/0aa2cf7d94716edaf585fc91a7c55dd2.svg
Nicolo Fusi
user
nfusi
Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains
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.
A model-agnostic framework for learning custom input tags enables general LLMs to solve specialized domain tasks with zero-shot generalization and improved performance.
Large Language Models LLMs natural language domain tags function tags embedding layer zero-shot generalization protein properties chemical properties drug-target interactions