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' …

The goal of the NTCIR-17 QA Lab-PoliInfo-4 task is to develop real-world complex question
answering (QA) techniques using Japanese political information such as local assembly …

The recent explosion of performance of large language models (LLMs) has changed the
field of Natural Language Processing (NLP) more abruptly and seismically than any other …

Ensuring factual consistency is crucial in various natural language processing tasks,
particularly in abstractive summarization, where preserving the integrity of information is …

Ensuring factual consistency is crucial for natural language generation tasks, particularly in
abstractive summarization, where preserving the integrity of information is paramount. Prior …

Recent pre-trained language models (PLMs) achieve promising results in existing
abstractive summarization datasets. However, existing summarization benchmarks overlap …

Factual inconsistencies pose a significant hurdle for the faithful summarization by generative
models. While a major direction to enhance inconsistency detection is to derive stronger …

Current abstractive summarization models often generate inconsistent content, ie texts that
are not directly inferable from the source document, are not consistent with respect to world …

Summarization is an important application of Large Language Models (LLMs). When
judging the quality of a summary, factual consistency holds a significant weight. Despite …

Factual consistency detection has gotten raised attention in the task of abstractive
summarization. Many existing works rely on synthetic training data, which may not …