LLM Augmented LLMs: Expanding Capabilities through Composition
Foundational models with billions of parameters which have been trained on
large corpora of data have demonstrated non-trivial skills in a variety of
domains. However, due to their monolithic structure, it is challenging and
expensive to augment them or impart new skills. On the other hand, due to their
adaptation abilities, several new instances of these models are being trained
towards new domains and tasks. In this work, we study the problem of efficient
and practical composition of existing foundation models with more specific
models to enable newer capabilities. To this end, we propose CALM --
Composition to Augment Language Models -- which introduces cross-attention
between models to compose their representations and enable new capabilities.
Salient features of CALM are: (i) Scales up LLMs on new tasks by 're-using'
existing LLMs along with a few additional parameters and data, (ii) Existing
model weights are kept intact, and hence preserves existing capabilities, and
(iii) Applies to diverse domains and settings. We illustrate that augmenting
PaLM2-S with a smaller model trained on low-resource languages results in an
absolute improvement of up to 13\% on tasks like translation into English and
arithmetic reasoning for low-resource languages. Similarly, when PaLM2-S is
augmented with a code-specific model, we see a relative improvement of 40\%
over the base model for code generation and explanation tasks -- on-par with
fully fine-tuned counterparts.
659775ac40cffd3b17ad97e7
Rachit Bansal
659775ac40cffd3b17ad97e8
Bidisha Samanta
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Siddharth Dalmia
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Nitish Gupta
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Shikhar Vashishth
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Sriram Ganapathy
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Abhishek Bapna
659775ac40cffd3b17ad97ee
Prateek Jain
659775ac40cffd3b17ad97ef
Partha Talukdar
LLM Augmented LLMs: Expanding Capabilities through Composition
Foundational models with billions of parameters which have been trained on
large corpora of data have demonstrated non-trivial skills in a variety of
domains. However, due to their monolithic structure, it is challenging and
expensive to augment them or impart new skills. On the other hand, due to their
adaptation abilities, several new instances of these models are being trained
towards new domains and tasks. In this work, we study the problem of efficient
and practical composition of existing foundation models with more specific
models to enable newer capabilities. To this end, we propose CALM --
Composition to Augment Language Models -- which introduces cross-attention
between models to compose their representations and enable new capabilities.
Salient features of CALM are: (i) Scales up LLMs on new tasks by 're-using'
existing LLMs along with a few additional parameters and data, (ii) Existing
model weights are kept intact, and hence preserves existing capabilities, and
(iii) Applies to diverse domains and settings. We illustrate that augmenting
PaLM2-S with a smaller model trained on low-resource languages results in an
absolute improvement of up to 13\% on tasks like translation into English and
arithmetic reasoning for low-resource languages. Similarly, when PaLM2-S is
augmented with a code-specific model, we see a relative improvement of 40\%
over the base model for code generation and explanation tasks -- on-par with
fully fine-tuned counterparts.
CALM, a composition method for large language models, enhances capabilities by integrating cross-attention with specialized models, demonstrating improvements on translation and coding tasks.
foundation models parameters large corpora adaptation abilities CALM Composition to Augment Language Models cross-attention LLMs PaLM2-S low-resource languages translation arithmetic reasoning code generation explanation tasks