  As increasingly sophisticated language models emerge, their trustworthiness
becomes a pivotal issue, especially in tasks such as summarization and
question-answering. Ensuring their responses are contextually grounded and
faithful is challenging due to the linguistic diversity and the myriad of
possible answers. In this paper, we introduce a novel approach to evaluate
faithfulness of machine-generated text by computing the longest noncontinuous
substring of the claim that is supported by the context, which we refer to as
the Longest Supported Subsequence (LSS). Using a new human-annotated dataset,
we finetune a model to generate LSS. We introduce a new method of evaluation
and demonstrate that these metrics correlate better with human ratings when LSS
is employed, as opposed to when it is not. Our proposed metric demonstrates an
18% enhancement over the prevailing state-of-the-art metric for faithfulness on
our dataset. Our metric consistently outperforms other metrics on a
summarization dataset across six different models. Finally, we compare several
popular Large Language Models (LLMs) for faithfulness using this metric. We
release the human-annotated dataset built for predicting LSS and our fine-tuned
model for evaluating faithfulness.
