Counting-Stars: A Simple, Efficient, and Reasonable Strategy for Evaluating Long-Context Large Language Models

While recent research endeavors have concentrated on developing Large Language Models (LLMs) with robust long-context capabilities, due to the lack of appropriate evaluation strategies, relatively little is known about how well the long-context capability and performance of leading LLMs (e.g., GPT-4 Turbo and Kimi Chat). To address this gap, we propose a simple, efficient, and reasonable strategy for evaluating long-context LLMs as a new benchmark, named Counting-Stars. The Counting-Stars is designed to require LLMs to fully understand and capture long dependencies in long contexts, further being able to collect inter-dependency across multiple pieces of evidence spanning the entire context to finish the task. Based on the Counting-Stars, we conduct experiments to evaluate the two leading long-context LLMs, i.e., GPT-4 Turbo and Kimi Chat. The experimental results indicate that GPT-4 Turbo and Kimi Chat achieve significant performance in the long context from 4K to 128K. We further present several intriguing analyses regarding the behavior of LLMs processing long context.