Evaluating Large Language Models for Health-related Queries with
  Presuppositions
Navreet Kaur Monojit Choudhury Danish Pruthi
As corporations rush to integrate large language models (LLMs) to their
search offerings, it is critical that they provide factually accurate
information that is robust to any presuppositions that a user may express. In
this work, we introduce UPHILL, a dataset consisting of health-related queries
with varying degrees of presuppositions. Using UPHILL, we evaluate the factual
accuracy and consistency of InstructGPT, ChatGPT, and BingChat models. We find
that while model responses rarely disagree with true health claims (posed as
questions), they often fail to challenge false claims: responses from
InstructGPT agree with 32% of the false claims, ChatGPT 26% and BingChat 23%.
As we increase the extent of presupposition in input queries, the responses
from InstructGPT and ChatGPT agree with the claim considerably more often,
regardless of its veracity. Responses from BingChat, which rely on retrieved
webpages, are not as susceptible. Given the moderate factual accuracy, and the
inability of models to consistently correct false assumptions, our work calls
for a careful assessment of current LLMs for use in high-stakes scenarios.