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

arXiv:2312.14346v2 Announce Type: replace
Abstract: Large Language Models (LLMs) are adept at text manipulation -- tasks such as machine translation and text summarization. However, these models can also be prone to hallucination, which can be detrimental to the faithfulness of any answers that the model provides. Recent works in combating hallucinations in LLMs deal with identifying hallucinated sentences and categorizing the different ways in which models hallucinate. This paper takes a deep dive into LLM behavior with respect to hallucinations, …

abstract adept arxiv cs.ai cs.cl everything hallucination hallucinations however identification interpretability language language models large language large language models llms machine machine translation manipulation summarization tasks text text summarization through translation type