And prompting tricks like asking for novelty and denial prompting trade-off originality and quality without meaningfully shifting the frontier of novelty …. so there’s lot more work to be done 😀

Sure, but can we elicit more novelty at inference time? Turns out it’s tricky. Increasing sampling temperatures (from 0.5 to 2) boosts originality but can hurt quality, creating a U-shaped effect.

But improving the underlying model can help yield more novel output! This can either be by (a) increasing model scale (1B -> 7B), and (b) instruction tuning (7B -> 7B-Instruct)

We find that base LLMs often generate less novel output than human-written references from the datasets

What does it mean for #LLM output to be novel?
In work w/ johnchen6.bsky.social, Jane Pan, Valerie Chen and He He, we argue it needs to be both original and high quality. While prompting tricks trade one for the other, better models (scaling/post-training) can shift the novelty frontier 🧵