Something I haven't seen explored, but I think could perhaps help is to somehow introduce feedback regarding the generation into the context, based on things th

Something I haven't seen explored, but I think could perhaps help is to somehow introduce feedback regarding the generation into the context, based on things that are easily computed w/ other tools (like perplexity). In "thinking" models we see a lot of emerging behaviour like "perhaps I should, but wait, this seems wrong", etc. Perhaps adding some signals at regular? intervals could help in surfacing the correct patterns when they are needed. There's a podcast I listened to ~1.5 years ago, where a team used GPT2, further trained on a bunch of related papers, and used snippets + perplexity to highlight potential errors. I remember them having some good accuracy when analysed by humans. Perhaps this could work at a larger scale? (a sort of "surprise" factor)