Measuring text summarization factuality using atomic facts entailment metrics in the context of retrieval augmented generation
<|reference_start|>Measuring text summarization factuality using atomic facts entailment metrics in the context of retrieval augmented generation: The use of large language models (LLMs) has significantly increased since the introduction of ChatGPT in 2022, demonstrating their value across various applications. However, a major challenge for enterprise and commercial adoption of LLMs is their tendency to generate inaccurate information, a phenomenon known as "hallucination." This project proposes a method for estimating the factuality of a summary generated by LLMs when compared to a source text. Our approach utilizes Naive Bayes classification to assess the accuracy of the content produced.<|reference_end|>
@article{kriman2024measuring,
    title={Measuring text summarization factuality using atomic facts entailment
  metrics in the context of retrieval augmented generation},
    author={N. E. Kriman},
    journal={arXiv preprint arXiv:2408.15171},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15171},
    primaryClass={cs.CL}
}