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Thang Luong 28. sij
Introducing , a 2.6B-param open-domain chatbot with near-human quality. Remarkably, we show strong correlation between perplexity & humanlikeness! Paper: Sample conversations:
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Thang Luong 28. sij
Odgovor korisniku/ci @quocleix @xpearhead
is based on the Evolved Transformer (ET, an improved Transformer) & trained to minimize perplexity, the uncertainty of predicting the next word in a conversation. We built a novel "shallow-deep" seq2seq architecture: 1 ET block for encoder & 13 ET blocks for decoder.
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Thang Luong
We design a new human evaluation metric, Sensibleness & Specificity Average (SSA), which captures key elements of natural conversations. SSA is also shown to correlate with humanlikeness while being easier to measure. Human scores 86% SSA, 79%, other best chatbots 56%.
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Thang Luong 28. sij
Odgovor korisniku/ci @quocleix @xpearhead
Implications from the project: 1. Perplexity might be "the" automatic metric that the field's been looking for. 2. Bots trained on large-scale social conversations & pushed hard for low perplexity will be good. 3. Safety layer is needed for respectful conversations!
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