Evaluating the factuality of long-form text generated by large language models (LMs) is non-
trivial because (1) generations often contain a mixture of supported and unsupported pieces …

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

Large Language Models (LLMs) trained on extensive textual corpora have emerged as
leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite …

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

Evaluating natural language generation (NLG) is a vital but challenging problem in artificial
intelligence. Traditional evaluation metrics mainly capturing content (eg n-gram) overlap …

Alignment tuning has become the de facto standard practice for enabling base large
language models (LLMs) to serve as open-domain AI assistants. The alignment tuning …

Assessment and evaluation have long been critical challenges in artificial intelligence (AI)
and natural language processing (NLP). However, traditional methods, whether matching …

Recently, GPT-4 has become the de facto evaluator for long-form text generated by large
language models (LLMs). However, for practitioners and researchers with large and custom …

Recent years have witnessed remarkable progress made in large language models (LLMs).
Such advancements, while garnering significant attention, have concurrently elicited various …

Model-based evaluation is at the heart of successful model development--as a reward
model for training, and as a replacement for human evaluation. To train such evaluators, the …