Plausibility Processing in Transformer Language Models: Focusing on the
  Role of Attention Heads in GPT
Soo Hyun Ryu
The goal of this paper is to explore how Transformer language models process
semantic knowledge, especially regarding the plausibility of noun-verb
relations. First, I demonstrate GPT2 exhibits a higher degree of similarity
with humans in plausibility processing compared to other Transformer language
models. Next, I delve into how knowledge of plausibility is contained within
attention heads of GPT2 and how these heads causally contribute to GPT2's
plausibility processing ability. Through several experiments, it was found
that: i) GPT2 has a number of attention heads that detect plausible noun-verb
relationships; ii) these heads collectively contribute to the Transformer's
ability to process plausibility, albeit to varying degrees; and iii) attention
heads' individual performance in detecting plausibility does not necessarily
correlate with how much they contribute to GPT2's plausibility processing
ability.