Symbol tuning improves in-context learning in language models
We present symbol tuning - finetuning language models on in-context
input-label pairs where natural language labels (e.g., "positive/negative
sentiment") are replaced with arbitrary symbols (e.g., "foo/bar"). Symbol
tuning leverages the intuition that when a model cannot use instructions or
natural language labels to figure out a task, it must instead do so by learning
the input-label mappings.
  We experiment with symbol tuning across Flan-PaLM models up to 540B
parameters and observe benefits across various settings. First, symbol tuning
boosts performance on unseen in-context learning tasks and is much more robust
to underspecified prompts, such as those without instructions or without
natural language labels. Second, symbol-tuned models are much stronger at
algorithmic reasoning tasks, with up to 18.2% better performance on the List
Functions benchmark and up to 15.3% better performance on the Simple Turing
Concepts benchmark. Finally, symbol-tuned models show large improvements in
following flipped-labels presented in-context, meaning that they are more
capable of using in-context information to override prior semantic knowledge.
6462eeed8bd7025ebe857712
Jerry Wei
claimed_verified
2023-08-10T01:14:08.719Z
60db3b451f1eec955a46d065
/avatars/10bd161ba6b56717feba9fbf539db4ea.svg
Jerry Wei
user
jerrywei
6462eeed8bd7025ebe857713
Le Hou
claimed_verified
2023-05-17T08:04:40.025Z
626b2402c804c432c1b5004c
/avatars/a119644422335e260af0ca4ef6399870.svg
Le Hou
user
solooobear
6462eeed8bd7025ebe857714
Andrew Lampinen
6462eeed8bd7025ebe857715
Xiangning Chen
claimed_verified
2023-05-17T08:04:34.385Z
631bc3584aa47d6afbe440bd
/avatars/cd9a605fbdf5454db9b4f714b28ff3fb.svg
Xiangning Chen
user
xiangningc
6462eeed8bd7025ebe857716
Da Huang
6462eeed8bd7025ebe857717
Yi Tay
6462eeed8bd7025ebe857718
Xinyun Chen
6462eeed8bd7025ebe857719
Yifeng Lu
6462eeed8bd7025ebe85771a
Denny Zhou
claimed_verified
2024-02-20T18:28:42.759Z
65d4d62d493aeaf0bb295f1b
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/vXMxaoWwFziul8TqIhQw8.jpeg
Denny Zhou
user
dennyzhou
6462eeed8bd7025ebe85771b
Tengyu Ma
6462eeed8bd7025ebe85771c
Quoc V. Le
Symbol tuning improves in-context learning in language models
We present symbol tuning - finetuning language models on in-context
input-label pairs where natural language labels (e.g., "positive/negative
sentiment") are replaced with arbitrary symbols (e.g., "foo/bar"). Symbol
tuning leverages the intuition that when a model cannot use instructions or
natural language labels to figure out a task, it must instead do so by learning
the input-label mappings.
  We experiment with symbol tuning across Flan-PaLM models up to 540B
parameters and observe benefits across various settings. First, symbol tuning
boosts performance on unseen in-context learning tasks and is much more robust
to underspecified prompts, such as those without instructions or without
natural language labels. Second, symbol-tuned models are much stronger at
algorithmic reasoning tasks, with up to 18.2% better performance on the List
Functions benchmark and up to 15.3% better performance on the Simple Turing
Concepts benchmark. Finally, symbol-tuned models show large improvements in
following flipped-labels presented in-context, meaning that they are more
capable of using in-context information to override prior semantic knowledge.
Symbol tuning enhances language models by using arbitrary symbols instead of natural language labels, improving in-context learning, algorithmic reasoning, and handling flipped labels.
symbol tuning in-context input-label pairs Flan-PaLM in-context learning tasks underspecified prompts algorithmic reasoning tasks List Functions benchmark Simple Turing Concepts benchmark flipped-labels in-context information semantic knowledge