Recipes for building an open-domain chatbot
Figure 22: Counts of most common 3-grams from the BST Generative 2.7B model (likelihood) from the conversation logs when talking to crowdworkers, compared to those of the same model trained with unlikelihood, and to human logs (for the same number of utterances).
['[ITALIC] n-gram', 'MLE', 'Unlikelihood', 'Human']
It has been observed that generative models employing beam search decoding (or other methods that approximately choose the most likely utterance) tend to generate common words too frequently, and rare words too infrequently, as compared to the human distribution Holtzman et al. In dialogue, humans can interpret this as technically correct, but unengaging, in the extreme this is the so-called “I don’t know” problem, where models tend to output such non-committal utterances. Using sampling to select lower likelihood generations can help, but at the risk of saying something which makes less sense. It appears that even our best models using beam search are still exhibiting such behavior. We have found that encouraging the length of the generations to be longer helps, in that the model is forced to generate something more detailed, but the problem still remains. Given that there are only 100 conversations, the expressions “do you like”, “lot of fun”, “have any hobbies” etc. are clearly over-expressed compared to human-human conversations. We note that the current evaluation does not seem to expose this as boring because the conversations are short and are evaluated separately. Unfortunately, this made a very small or negative impact in our ACUTE-Evals of engagingness, see For engagingness, as explained, we believe this is because the current evaluation technique employing short conversations cannot measure this phenomenon well.