Text summarization research has undergone several significant transformations with the
advent of deep neural networks, pre-trained language models (PLMs), and recent large …

Recognizing if LLM output can be grounded in evidence is central to many tasks in NLP:
retrieval-augmented generation, summarization, document-grounded dialogue, and more …

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

Factual inconsistency with source documents in automatically generated summaries can
lead to misinformation or pose risks. Existing factual consistency (FC) metrics are …

Single document news summarization has seen substantial progress on faithfulness in
recent years, driven by research on the evaluation of factual consistency, or hallucinations …

In this paper, we present a strategy to provide users with explainable cross-domain
recommendations (CDR) that exploits large language models (LLMs). Generally speaking …

We introduce CheckEval, a novel evaluation framework using Large Language Models,
addressing the challenges of ambiguity and inconsistency in current evaluation methods …

Code generation aims to automatically generate code snippets of specific programming
language according to natural language descriptions. The continuous advancements in …

LLMs can generate factually incorrect statements even when provided access to reference
documents. Such errors can be dangerous in high-stakes applications (eg, document …

Despite the remarkable performance of generative large language models (LLMs) on
abstractive summarization, they face two significant challenges: their considerable size and …