When can transformers reason with abstract symbols?
We investigate the capabilities of transformer large language models (LLMs)
on relational reasoning tasks involving abstract symbols. Such tasks have long
been studied in the neuroscience literature as fundamental building blocks for
more complex abilities in programming, mathematics, and verbal reasoning. For
(i) regression tasks, we prove that transformers generalize when trained, but
require astonishingly large quantities of training data. For (ii)
next-token-prediction tasks with symbolic labels, we show an "inverse scaling
law": transformers fail to generalize as their embedding dimension increases.
For both settings (i) and (ii), we propose subtle transformer modifications
which can reduce the amount of data needed by adding two trainable parameters
per head.
652decb82d617ec278bf9c5b
Enric Boix-Adsera
extracted_confirmed
2023-10-17T20:06:23.846Z
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/avatars/da8df192767bf2a2a16a3889118b5184.svg
Enric Boix
user
eboix
652decb82d617ec278bf9c5c
Omid Saremi
652decb82d617ec278bf9c5d
Emmanuel Abbe
652decb82d617ec278bf9c5e
Samy Bengio
claimed_verified
2024-06-09T20:07:47.351Z
6488d89678570cedad6f4e03
/avatars/d3c86914cab46b953c0ee7ed17e3d6bb.svg
Samy Bengio
user
bengio
652decb82d617ec278bf9c5f
Etai Littwin
652decb82d617ec278bf9c60
Joshua Susskind
claimed_verified
2024-01-17T16:20:19.756Z
6470d2247fd7ecdbd0ec3cc9
/avatars/52c5eca12499a1aa9bd49c43d4f20685.svg
Joshua M. Susskind
user
jsusskind
When can transformers reason with abstract symbols?
We investigate the capabilities of transformer large language models (LLMs)
on relational reasoning tasks involving abstract symbols. Such tasks have long
been studied in the neuroscience literature as fundamental building blocks for
more complex abilities in programming, mathematics, and verbal reasoning. For
(i) regression tasks, we prove that transformers generalize when trained, but
require astonishingly large quantities of training data. For (ii)
next-token-prediction tasks with symbolic labels, we show an "inverse scaling
law": transformers fail to generalize as their embedding dimension increases.
For both settings (i) and (ii), we propose subtle transformer modifications
which can reduce the amount of data needed by adding two trainable parameters
per head.
Transformers require vast amounts of data for regression tasks and exhibit decreased generalization with higher embedding dimensions in symbolic next-token-prediction tasks, but simple parameter modifications can improve their performance.
transformer large language models relational reasoning next-token-prediction embedding dimension trainable parameters