Generative AI (GenAI) represents a shift from AI's ability to “recognize” to its ability to
“generate” solutions for a wide range of tasks. As generated solutions and applications grow …

In the rapidly evolving domain of Natural Language Generation (NLG) evaluation,
introducing Large Language Models (LLMs) has opened new avenues for assessing …

We analyze the behaviors of open large language models (LLMs) on the task of data-to-text
(D2T) generation, ie, generating coherent and relevant text from structured data. To avoid …

Evaluating the degree of reproduction of copyright-protected content by language models
(LMs) is of significant interest to the AI and legal communities. Although both literal and non …

Recently, the integration of external tools with Large Language Models (LLMs) has emerged
as a promising approach to overcome the inherent constraints of their pre-training data …

This study introduces EHRNoteQA, a novel patient-specific question answering benchmark
tailored for evaluating Large Language Models (LLMs) in clinical environments. Based on …

Some prior work has shown that LLMs perform well in NLG evaluation for different tasks.
However, we discover that LLMs seem to confuse different evaluation criteria, which reduces …

Recently, Large Language Models (LLMs) have been widely studied by researchers for their
roles in various downstream NLP tasks. As a fundamental task in the NLP field, Chinese …

Abstract Large Language Models (LLMs) exhibit remarkable text classification capabilities,
excelling in zero-and few-shot learning (ZSL and FSL) scenarios. However, since they are …

Building high-quality datasets for specialized tasks is a time-consuming and resource-
intensive process that often requires specialized domain knowledge. We propose Corpus …