😮😮 GPT-4, despite being one of the leading LLMs, struggles with long-context bilingual reasoning tasks. Even with text lengths shorter than 2K tokens!!

An interesting benchmark paper evaluated the long-context capabilities of standard LLMs in an English-Chinese setting.

Link 👉 https://arxiv.org/pdf/2407.11963

💡 NeedleBench is a framework designed to evaluate the long-context capabilities of LLMs, particularly in bilingual settings (English-Chinese). It includes a series of tasks that progressively increase in complexity, spanning multiple length intervals (4k, 8k, 32k, 128k, 200k, 1000k, and beyond) and different depth ranges.

Some features

• ⛳ NeedleBench assesses how well leading open-source models can identify key information and apply it to reasoning within long bilingual texts. The framework - allows strategic insertion of critical data points in different text depth zones to rigorously test models’ retrieval and reasoning capabilities.
• ⛳ They propose the ATC (Ancestral Trace Challenge) to simulate complex long-context tasks in real-world scenarios, providing a simple method for evaluating LLMs in complicated long-context situations.
• ⛳ The paper evaluates mainstream models like GPT-4 Turbo, Claude 3, GLM-4, and others in identifying key question-relevant information and reasoning. Despite recent advancements, these models show significant room for improvement in practical long-context applications.
• ⛳ Experimental results highlight that existing LLMs face challenges in handling complex logical relationships in long-context texts.