Large language models (LLMs) have demonstrated remarkable capabilities across a broad
spectrum of tasks. They have attracted significant attention and been deployed in numerous …

Many text generation applications require the generated text to be factually consistent with
input information. Automatic evaluation of factual consistency is challenging. Previous work …

NLP research on logical reasoning regains momentum with the recent releases of a handful
of datasets, notably LogiQA and Reclor. Logical reasoning is exploited in many probing …

Factual consistency evaluation is often conducted using Natural Language Inference (NLI)
models, yet these models exhibit limited success in evaluating summaries. Previous work …

Recently proposed BERT-based evaluation metrics for text generation perform well on
standard benchmarks but are vulnerable to adversarial attacks, eg, relating to information …

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

Natural Language Inference (NLI) has been extensively studied by the NLP community as a
framework for estimating the semantic relation between sentence pairs. While early work …

Automated evaluation is crucial for streamlining text summarization benchmarking and
model development, given the costly and time-consuming nature of human evaluation …

Pre-trained abstractive summarization models can generate fluent summaries and achieve
high ROUGE scores. Previous research has found that these models often generate …

Despite tremendous improvements in natural language generation, summarization models
still suffer from the unfaithfulness issue. Previous work evaluates faithfulness either using …