DelucionQA: Detecting Hallucinations in Domain-specific Question
  Answering
Mobashir Sadat Zhengyu Zhou Lukas Lange Jun Araki Arsalan Gundroo Bingqing Wang Rakesh R Menon Md Rizwan Parvez Zhe Feng
Hallucination is a well-known phenomenon in text generated by large language
models (LLMs). The existence of hallucinatory responses is found in almost all
application scenarios e.g., summarization, question-answering (QA) etc. For
applications requiring high reliability (e.g., customer-facing assistants), the
potential existence of hallucination in LLM-generated text is a critical
problem. The amount of hallucination can be reduced by leveraging information
retrieval to provide relevant background information to the LLM. However, LLMs
can still generate hallucinatory content for various reasons (e.g.,
prioritizing its parametric knowledge over the context, failure to capture the
relevant information from the context, etc.). Detecting hallucinations through
automated methods is thus paramount. To facilitate research in this direction,
we introduce a sophisticated dataset, DelucionQA, that captures hallucinations
made by retrieval-augmented LLMs for a domain-specific QA task. Furthermore, we
propose a set of hallucination detection methods to serve as baselines for
future works from the research community. Analysis and case study are also
provided to share valuable insights on hallucination phenomena in the target
scenario.