  The widespread adoption of Large Language Models (LLMs) has facilitated
numerous benefits. However, hallucination is a significant concern. In
response, Retrieval Augmented Generation (RAG) has emerged as a highly
promising paradigm to improve LLM outputs by grounding them in factual
information. RAG relies on textual entailment (TE) or similar methods to check
if the text produced by LLMs is supported or contradicted, compared to
retrieved documents. This paper argues that conventional TE methods are
inadequate for spotting hallucinations in content generated by LLMs. For
instance, consider a prompt about the 'USA's stance on the Ukraine war''. The
AI-generated text states, ...U.S. President Barack Obama says the U.S. will not
put troops in Ukraine...'' However, during the war the U.S. president is Joe
Biden which contradicts factual reality. Moreover, current TE systems are
unable to accurately annotate the given text and identify the exact portion
that is contradicted. To address this, we introduces a new type of TE called
``Factual Entailment (FE).'', aims to detect factual inaccuracies in content
generated by LLMs while also highlighting the specific text segment that
contradicts reality. We present FACTOID (FACTual enTAILment for hallucInation
Detection), a benchmark dataset for FE. We propose a multi-task learning (MTL)
framework for FE, incorporating state-of-the-art (SoTA) long text embeddings
such as e5-mistral-7b-instruct, along with GPT-3, SpanBERT, and RoFormer. The
proposed MTL architecture for FE achieves an avg. 40\% improvement in accuracy
on the FACTOID benchmark compared to SoTA TE methods. As FE automatically
detects hallucinations, we assessed 15 modern LLMs and ranked them using our
proposed Auto Hallucination Vulnerability Index (HVI_auto). This index
quantifies and offers a comparative scale to evaluate and rank LLMs according
to their hallucinations.
