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

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

ABSTRACT Large Language Models (LLMs) are rapidly surpassing human knowledge in
many domains. While improving these models traditionally relies on costly human data …

As large language models (LLMs) advance, it becomes more challenging to reliably
evaluate their output due to the high costs of human evaluation. To make progress towards …

We introduce AudioBench, a universal benchmark designed to evaluate Audio Large
Language Models (AudioLLMs). It encompasses 8 distinct tasks and 26 datasets, among …

Generative models are a class of AI models capable of creating new instances of data by
learning and sampling from their statistical distributions. In recent years, these models have …

Automatic LLM benchmarks, such as AlpacaEval 2.0, Arena-Hard-Auto, and MT-Bench,
have become popular for evaluating language models due to their cost-effectiveness and …

Reward modeling is crucial for aligning large language models (LLMs) with human
preferences, especially in reinforcement learning from human feedback (RLHF). However …

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 …

Recent work has explored the capability of large language models (LLMs) to identify and
correct errors in LLM-generated responses. These refinement approaches frequently …