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Great work! In your medium post, in the Post-processing section (step 1) you wrote: "If so, we re-run the evaluation prompt and force content_contains_answer to
by curl-up 4y ago
Great work! In your medium post, in the Post-processing section (step 1) you wrote: "If so, we re-run the evaluation prompt and force content_contains_answer to be false." I assume you do this forcing by adding 'false' to the prompt (as if GPT completed it), just like to seed the JSON structure itself. Could you explain what you do with the justification in that case? Use it in the same way as if the result was 'false' in the first place?
Do you have some stats on how often this even happens? In my experience, I have rarely seen these logprob thresholds actually get activated, because for binary token choices the probability of one of them is almost always >95%, but maybe this is because my prompts were not set correctly.
- jasonwcfan 4y agoIf we force content_contains_answer to false, we always re-run the evaluation step first to get a new justification for why the content doesn't have enough information. Then we run that justification through another prompt to convert it to a user-facing response. It happens pretty rarely, off the top of my head probably 5% of the time. However we do see a pretty wide range in binary token probabilities anywhere from low 50% to something that asymptotically approaches 100%.
- deleted 4y ago[deleted]