Reverse Training to Nurse the Reversal Curse
Large language models (LLMs) have a surprising failure: when trained on "A
has a feature B", they do not generalize to "B is a feature of A", which is
termed the Reversal Curse. Even when training with trillions of tokens this
issue still appears due to Zipf's law - hence even if we train on the entire
internet. This work proposes an alternative training scheme, called reverse
training, whereby all words are used twice, doubling the amount of available
tokens. The LLM is trained in both forward and reverse directions by reversing
the training strings while preserving (i.e., not reversing) chosen substrings,
such as entities. We show that data-matched reverse-trained models provide
superior performance to standard models on standard tasks, and compute-matched
reverse-trained models provide far superior performance on reversal tasks,
helping resolve the reversal curse issue.
65fb9dc00b78c48c9e373b44
Olga Golovneva
admin_assigned
2024-03-21T11:50:17.843Z
6318b1e06e6bd039440871d6
/avatars/2d9f96eb4092a9d1734dcc09303b259a.svg
Olga Golovneva
user
Golovneva
65fb9dc00b78c48c9e373b45
Zeyuan Allen-Zhu
admin_assigned
2024-03-21T11:50:23.906Z
6316d14c0db6c679cf77fe1f
/avatars/b33dcadc0ff8941da6aa850284be3207.svg
Zeyuan Allen-Zhu
user
zhuzeyuan
65fb9dc00b78c48c9e373b46
Jason Weston
admin_assigned
2024-03-21T11:50:31.277Z
62f023a36a027498eaa2f9cc
/avatars/8ac1c5c74d0957e3c6cc94b3a7795c37.svg
Jason Weston
user
spermwhale
65fb9dc00b78c48c9e373b47
Sainbayar Sukhbaatar
claimed_verified
2024-07-30T16:01:21.239Z
66a8611eb51510d82ed54231
/avatars/ad559e774fee4914091b82c9831ae2a2.svg
Sainbayar Sukhbaatar
user
sainbar
Reverse Training to Nurse the Reversal Curse
Large language models (LLMs) have a surprising failure: when trained on "A
has a feature B", they do not generalize to "B is a feature of A", which is
termed the Reversal Curse. Even when training with trillions of tokens this
issue still appears due to Zipf's law - hence even if we train on the entire
internet. This work proposes an alternative training scheme, called reverse
training, whereby all words are used twice, doubling the amount of available
tokens. The LLM is trained in both forward and reverse directions by reversing
the training strings while preserving (i.e., not reversing) chosen substrings,
such as entities. We show that data-matched reverse-trained models provide
superior performance to standard models on standard tasks, and compute-matched
reverse-trained models provide far superior performance on reversal tasks,
helping resolve the reversal curse issue.
Reverse training enhances LLM performance on reversal tasks by doubling the training data through forward and reverse direction training while preserving entities.
Large language models Reversal Curse Zipf's law reverse training training strings entities