Large Language Models (LLMs) have demonstrated promising capabilities as automatic
evaluators in assessing the quality of generated natural language. However, LLMs still …

In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities
in various tasks (eg, long-context understanding), and many benchmarks have been …

The rise of large language models (LLMs) has enabled us to seek answers to inherently
debatable questions on LLM chatbots, necessitating a reliable way to evaluate their ability …

As Large Language Models (LLMs) continue to advance in natural language processing
(NLP), their ability to stably follow instructions in long-context inputs has become crucial for …

This paper presents the Long Context and Form Output (LCFO) benchmark, a novel
evaluation framework for assessing gradual summarization and summary expansion …

Autoformalization aims to convert informal mathematical proofs into machine-verifiable
formats, bridging the gap between natural and formal languages. However, ensuring …

Retrieval-augmented generation (RAG) is a promising approach to address the limitations of
fixed knowledge in large language models (LLMs). However, current benchmarks for …

Large Language Models (LLMs) have demonstrated exceptional comprehension
capabilities and a vast knowledge base, suggesting that LLMs can serve as efficient tools for …

Like humans, Large Language Models (LLMs) struggle to generate high-quality long-form
text that adheres to strict requirements in a single pass. This challenge is unsurprising, as …

Evaluating LLM-generated text has become a key challenge, especially in domain-specific
contexts like the medical field. This work introduces a novel evaluation methodology for LLM …