Detecting Hallucination and Coverage Errors in Retrieval Augmented
  Generation for Controversial Topics
Tyler A. Chang Katrin Tomanek Jessica Hoffmann Nithum Thain Erin van Liemt Kathleen Meier-Hellstern Lucas Dixon
We explore a strategy to handle controversial topics in LLM-based chatbots
based on Wikipedia's Neutral Point of View (NPOV) principle: acknowledge the
absence of a single true answer and surface multiple perspectives. We frame
this as retrieval augmented generation, where perspectives are retrieved from a
knowledge base and the LLM is tasked with generating a fluent and faithful
response from the given perspectives. As a starting point, we use a
deterministic retrieval system and then focus on common LLM failure modes that
arise during this approach to text generation, namely hallucination and
coverage errors. We propose and evaluate three methods to detect such errors
based on (1) word-overlap, (2) salience, and (3) LLM-based classifiers. Our
results demonstrate that LLM-based classifiers, even when trained only on
synthetic errors, achieve high error detection performance, with ROC AUC scores
of 95.3% for hallucination and 90.5% for coverage error detection on
unambiguous error cases. We show that when no training data is available, our
other methods still yield good results on hallucination (84.0%) and coverage
error (85.2%) detection.