Reliability Check: An Analysis of GPT-3's Response to Sensitive Topics
  and Prompt Wording
Aisha Khatun Daniel G. Brown
Large language models (LLMs) have become mainstream technology with their
versatile use cases and impressive performance. Despite the countless
out-of-the-box applications, LLMs are still not reliable. A lot of work is
being done to improve the factual accuracy, consistency, and ethical standards
of these models through fine-tuning, prompting, and Reinforcement Learning with
Human Feedback (RLHF), but no systematic analysis of the responses of these
models to different categories of statements, or on their potential
vulnerabilities to simple prompting changes is available. In this work, we
analyze what confuses GPT-3: how the model responds to certain sensitive topics
and what effects the prompt wording has on the model response. We find that
GPT-3 correctly disagrees with obvious Conspiracies and Stereotypes but makes
mistakes with common Misconceptions and Controversies. The model responses are
inconsistent across prompts and settings, highlighting GPT-3's unreliability.
Dataset and code of our analysis is available in
https://github.com/tanny411/GPT3-Reliability-Check.