In particular, in the five tasks (MMLU, MATH, BIG-Bench, Natural2Code, WMT23) where they report going to the GPT-4 API, they report an average of ~1 point improvement. This experiment setting seems comparable, and not evidence they are underperforming GPT-4.
However, all these settings are different from how ChatGPT-like systems are mostly being used (where mostly zero-shot). So difficult to judge the success of their instruction-tuning for use in this setting.
(apologies if this point posted twice. Lesswrong was showing errors when tried to post.)
dgros
Karma: 7
+1 here for the idea around how the models must commit to a URL once it starts, and that it can’t naturally cut off after starting. Presumably though the aspiration is that these reasoning/CoT-trained models could reflect back on the just completed URL and guess whether that is likely to be a real URL or not. If it’s not doing this check step, this might be a gap in the learned skills, more than intentional deception.