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

In the last few years, a large number of automatic evaluation metrics have been proposed for
evaluating Natural Language Generation (NLG) systems. The rapid development and …

The rise in malicious usage of large language models, such as fake content creation and
academic plagiarism, has motivated the development of approaches that identify AI …

Recent advances in Transformer-based large language models (LLMs) have led to
significant performance improvements across many tasks. These gains come with a drastic …

In this paper, we present a conceptually simple while empirically powerful framework for
abstractive summarization, SimCLS, which can bridge the gap between the learning …

We propose BERTScore, an automatic evaluation metric for text generation. Analogously to
common metrics, BERTScore computes a similarity score for each token in the candidate …

Modern NLP defines the task of style transfer as modifying the style of a given sentence
without appreciably changing its semantics, which implies that the outputs of style transfer …

The spread of toxic content online is an important problem that has adverse effects on user
experience online and in our society at large. Motivated by the importance and impact of the …

Source code summarization involves creating brief descriptions of source code in natural
language. These descriptions are a key component of software documentation such as …

While human evaluation remains best practice for accurately judging the faithfulness of
automatically-generated summaries, few solutions exist to address the increased difficulty …