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Large Language Models (LLMs) are adept at text manipulation -- tasks such asmachine translation and text summarization. However, these models can also beprone to hallucination, which can be detrimental to the faithfulness of anyanswers that the model provides. Recent works in combating hallucinations inLLMs deal with identifying hallucinated sentences and categorizing thedifferent ways in which models hallucinate. This paper takes a deep dive intoLLM behavior with respect to hallucinations, defines a token-level approach toidentifying different kinds of hallucinations, and further utilizes thistoken-level tagging to improve the interpretability and faithfulness of LLMs indialogue summarization tasks. Through this, the paper presents a new, enhanceddataset and a new training paradigm.

Don't Believe Everything You Read: Enhancing Summarization  Interpretability through Automatic Identification of Hallucinations in Large  Language Models