GPT-2 was critiqued for its inability to deal with simple arithmetic questions, as well as contradicting itself over long periods [1].
GPT-3 has similar deficiencies in terms of sensible real-world knowledge and displaying coherence both with its own previous answers and with a more general real-world knowledge:
'Within the domain of discrete language tasks, we have noticed informally that GPT-3 seems to have special difficulty with “common sense physics”, despite doing well on some datasets (such as PIQA [BZB+19]) that test this domain. Specifically GPT-3 has difficulty with questions of the type “If I put cheese into the fridge, will it melt?”.' [2]) of the type that would obviously torpedo attempts at open domain dialogue over long periods.
Take a typical two paper pointing out the failings of brute force methods for dialogue. They trot out a classic example:
'Without getting into the specifics of how these systems are constructed, it is apparent that such an approach [brute force] provides a method for imitating a dialogue but not for participating in one. We can see the limitations of the approach from examples like these: Human: what is your job ? Machine: i ’m a lawyer . Human: what do you do ? Machine: i ’m a doctor (Vinyals & Le, 2015).' [3] Huge models can provide plausible, but not consistently coherent, dialogue inputs. In the dialogue domain, the 'winter' will come (/has come) when it becomes clear that Meena, BlenderBot etc need a little help when it comes to coherence over an arbitrary number of turns, displaying 'common sense physics', and so on.
[1] https://thegradient.pub/gpt2-and-the-nature-of-intelligence/ [2] https://arxiv.org/pdf/2005.14165.pdf [3] https://arxiv.org/pdf/1812.01144.pdf