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New paper & surprising result: We show GPT3 can learn to express its own uncertainty in natural language (eg “high confidence”) without using model logits. GPT3 is reasonably *calibrated* even w/ distribution shift for a range of basic math tasks.
We finetune GPT3 to express its own uncertainty in words and show it remains (moderately) calibrated under distribution shift. On basic math tasks, the performance of this “verbalized uncertainty” is comparable to logit-based uncertainty (and sometimes better).
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Why express uncertainty in natural language (vs. using logits)? 1. It’s how humans express uncertainty - so helps model understand & communicate w/ people. 2. Language is more expressive, e.g. continuous distributions. 3. Not all models have logits, e.g. info-retrieval models.
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We test calibration under distribution shift, which is harder than in-distribution. The standard is other baselines: e.g. using the model logprobs (logits) or always guessing the baserate. Our approach (verbalized probability) does well against the baselines…
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