3 ms·
In fact, it should be expected. The underlying data is embedded with artifacts of behaviors/affects that reflect the underlying state of the person who wrote t
by AndrewKemendo 3y ago
In fact, it should be expected.
The underlying data is embedded with artifacts of behaviors/affects that reflect the underlying state of the person who wrote them.
Stanford famously studied this for reddit (notably a huge part of the commoncrawl dataset) and reported that it had non-trivial percentages of anti-social sentiments embedded in the text [1]
You can't filter out the biases embedded in the data because it is a functional artifact of what the people creating the data were communicating. Best you can do is put guardrails and censor it, but that's a neverending game you can't win.
Junk in Junk out still applies to LLMs
[1]https://hci.stanford.edu/publications/2022/Park_ContentModAudit_CSCW22.pdf https://hci.stanford.edu/publications/2022/Park_ContentModAu...
- madsbuch 3y agoNot all content comes associated with a date. I reckon enough does, that a signal is embedded in the model.
- AndrewKemendo 3y agoI’ll bet it’s really friendly around Christmas I wonder how we would test it
- vineyardmike 3y agoWell the original post uses a for loop with a prompt that manually sets the date to various dates, then measures the length. So it seems very easy to set a holiday date. Testing “friendliness” or “holiday cheer” could be done via some sortof proxy… you could prompt it to reply to generic pleasantries while role playing “as someone busy who is late” and see if the word choice changes. Or maybe ask it to be a judge for a crime of desperation (stealing toys for orphans?) and determine sentencing durations as a proxy for sympathy? I suspect that is too far from training data though.