Counting-Stars: A Multi-evidence, Position-aware, and Scalable Benchmark for Evaluating Long-Context Large Language Models

• URL: http://arxiv.org/abs/2403.11802v3
• Date: Fri, 17 May 2024 16:58:23 GMT
• Title: Counting-Stars: A Multi-evidence, Position-aware, and Scalable Benchmark for Evaluating Long-Context Large Language Models
• Authors: Mingyang Song, Mao Zheng, Xuan Luo,
• Abstract summary: We propose a benchmark for evaluating long-context Large Language Models (LLMs) named Counting-Stars. We conduct experiments to evaluate long-context LLMs (i.e., GPT-4 Turbo, Gemini 1.5 Pro, Claude3 Opus, GLM-4, and Moonshot-v1) Results show that Gemini 1.5 Pro achieves the best overall results, while GPT-4 Turbo is the most stable across various tasks.
• Score: 14.906150451947443
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While recent research endeavors have focused on developing Large Language Models (LLMs) with robust long-context capabilities, due to the lack of long-context benchmarks, relatively little is known about how well the performance of long-context LLMs. To address this gap, we propose a multi-evidence, position-aware, and scalable benchmark for evaluating long-context LLMs, named Counting-Stars, which evaluates long-context LLMs by using two tasks: multi-evidence acquisition and multi-evidence reasoning. Based on the Counting-Stars test, we conduct experiments to evaluate long-context LLMs (i.e., GPT-4 Turbo, Gemini 1.5 Pro, Claude3 Opus, GLM-4, and Moonshot-v1). Experimental results demonstrate that Gemini 1.5 Pro achieves the best overall results, while the performance of GPT-4 Turbo is the most stable across various tasks. Furthermore, our analysis of these LLMs, which are extended to handle long-context scenarios, indicates that there is potential for improvement as the length of the input context and the intricacy of the tasks are increasing.

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