Abstract Evaluation practices in natural language generation (NLG) have many known flaws,
but improved evaluation approaches are rarely widely adopted. This issue has become …

Large language models (LLMs), exemplified by ChatGPT, have gained considerable
attention for their excellent natural language processing capabilities. Nonetheless, these …

Large language models (LLMs) have gained considerable attention for their excellent
natural language processing capabilities. Nonetheless, these LLMs present many …

Faithfully correcting factual errors is critical for maintaining the integrity of textual knowledge
bases and preventing hallucinations in sequence-to-sequence models. Drawing on humans' …

Evaluating the factual consistency of automatically generated summaries is essential for the
progress and adoption of reliable summarization systems. Despite recent advances, existing …

Long-form clinical summarization of hospital admissions has real-world significance
because of its potential to help both clinicians and patients. The factual consistency of …

Despite significant progress in understanding and improving faithfulness in abstractive
summarization, the question of how decoding strategies affect faithfulness is less studied …

Abstractive summarization has made tremendous progress in recent years. In this work, we
perform fine-grained human annotations to evaluate long document abstractive …

Existing factual consistency evaluation approaches for text summarization provide binary
predictions and limited insights into the weakness of summarization systems. Therefore, we …

In work on AMR (Abstract Meaning Representation), similarity metrics are crucial as they are
used to evaluate AMR systems such as AMR parsers. Current AMR metrics are all based on …