Comment by JeremyHerrman
1 year ago
Why can't we resolve this with synthetic data? Just take the original statements and ask another LLM to flip around the relation, then train on both. I tried this with GPT-4 and it seems to understand the task [0]:
Me:
For each of the following relational statements, flip the relation around and generate a new statement or question answer pair.
Input: Bluey's father is Bandit
Output: Bandit's daughter is Bluey
Input: Neil Armstrong was the first person to step on the Moon.
Output: Who was the first person to step on the Moon? Neil Armstrong.
Now for the real statements:
- Valentina Tereshkova was the first woman to travel to space
- Who is Mary Lee Pfeiffer's son? Tom Cruise
ChatGPT:
Here are the flipped relational statements:
Valentina Tereshkova was the first woman to travel to space
Output: Who was the first woman to travel to space? Valentina Tereshkova.
Who is Mary Lee Pfeiffer's son? Tom Cruise
Output: Tom Cruise's mother is Mary Lee Pfeiffer.
[0]: https://chat.openai.com/share/33eb3ee4-2094-4748-b01e-0967af...
The difficulty would likely be that the “X is Y” relations don’t just occur in that simple, separated-out form, but in a whole spectrum of more complicated contexts, and the latter probably dominate. You would have to identify those and synthesize a text corpus that contains the reverse notions across a similar spectrum.
I don't think the reversal curse actually replicates though. When I looked into that it seemed like there was some flaws in the study and when I fixed those I couldn't get the same results as they did anymore. I keep meaning to write this up properly but never get around to it.
If you think about it, they're making an extraordinary claim given how fluently LLMs engage in language and reasoning.