Summary of Reverse Training to Nurse the Reversal Curse, by Olga Golovneva et al.
Reverse Training to Nurse the Reversal Curse
by Olga Golovneva, Zeyuan Allen-Zhu, Jason Weston, Sainbayar Sukhbaatar
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) have a surprising failure: they struggle with generalizing from “A has a feature B” to “B is a feature of A”, known as the Reversal Curse. Despite being trained on trillions of tokens, this issue persists due to Zipf’s law, making it even more challenging to resolve. This work proposes an alternative training scheme, called reverse training, where all words are used twice, effectively doubling the amount of available tokens. The LLM is trained in both forward and reverse directions by reversing the training strings while preserving chosen substrings, such as entities. We demonstrate that data-matched reverse-trained models outperform standard models on standard tasks, and compute-matched reverse-trained models achieve far superior performance on reversal tasks, helping to address the Reversal Curse issue. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have a big problem: they can’t understand sentences like “B is a feature of A” when they’re trained on “A has a feature B”. This is called the Reversal Curse. Even if they’re trained on all the data in the world, this problem still happens. The solution proposed in this paper is to train the model in two ways: forward and backward. This helps the model understand sentences like “B is a feature of A” better. |
