Toward Reliable Biomedical Hypothesis Generation: Evaluating
  Truthfulness and Hallucination in Large Language Models
Authors: name: Guangzhi Xiong, name: Eric Xie, name: Corey Williams, name: Myles Kim, name: Amir Hassan Shariatmadari, name: Sikun Guo, name: Stefan Bekiranov, name: Aidong Zhang

Large language models (LLMs) have shown significant potential in scientific
disciplines such as biomedicine, particularly in hypothesis generation, where
they can analyze vast literature, identify patterns, and suggest research
directions. However, a key challenge lies in evaluating the truthfulness of
generated hypotheses, as verifying their accuracy often requires substantial
time and resources. Additionally, the hallucination problem in LLMs can lead to
the generation of hypotheses that appear plausible but are ultimately
incorrect, undermining their reliability. To facilitate the systematic study of
these challenges, we introduce TruthHypo, a benchmark for assessing the
capabilities of LLMs in generating truthful biomedical hypotheses, and KnowHD,
a knowledge-based hallucination detector to evaluate how well hypotheses are
grounded in existing knowledge. Our results show that LLMs struggle to generate
truthful hypotheses. By analyzing hallucinations in reasoning steps, we
demonstrate that the groundedness scores provided by KnowHD serve as an
effective metric for filtering truthful hypotheses from the diverse outputs of
LLMs. Human evaluations further validate the utility of KnowHD in identifying
truthful hypotheses and accelerating scientific discovery. Our data and source
code are available at https://github.com/Teddy-XiongGZ/TruthHypo.