Detecting Mode Collapse in Language Models via Narration
Sil Hamilton
No two authors write alike. Personal flourishes invoked in written
narratives, from lexicon to rhetorical devices, imply a particular author--what
literary theorists label the implied or virtual author; distinct from the real
author or narrator of a text. Early large language models trained on unfiltered
training sets drawn from a variety of discordant sources yielded incoherent
personalities, problematic for conversational tasks but proving useful for
sampling literature from multiple perspectives. Successes in alignment research
in recent years have allowed researchers to impose subjectively consistent
personae on language models via instruction tuning and reinforcement learning
from human feedback (RLHF), but whether aligned models retain the ability to
model an arbitrary virtual author has received little scrutiny. By studying
4,374 stories sampled from three OpenAI language models, we show successive
versions of GPT-3 suffer from increasing degrees of "mode collapse" whereby
overfitting the model during alignment constrains it from generalizing over
authorship: models suffering from mode collapse become unable to assume a
multiplicity of perspectives. Our method and results are significant for
researchers seeking to employ language models in sociological simulations.