Long-context LLMs Struggle with Long In-context Learning
Tianle Li, Ge Zhang, Quy Duc Do, Xiang Yue, Wenhu Chen
Large Language Models (LLMs) have made significant strides in handling long sequences exceeding 32K tokens. However, their performance evaluation has largely been confined to metrics like perplexity and synthetic tasks, which may not fully capture their abilities in more nuanced, real-world scenarios. This study introduces a specialized benchmark (LongICLBench) focusing on long in-context learning within the realm of extreme-label classification. We meticulously selected six datasets with a label range spanning 28 to 174 classes covering different input (few-shot demonstration) lengths from 2K to 50K tokens. Our benchmark requires LLMs to comprehend the entire input to recognize the massive label spaces to make correct predictions. We evaluate 13 long-context LLMs on our benchmarks. We find that the long-context LLMs perform relatively well on less challenging tasks with shorter demonstration lengths by effectively utilizing the long context window. However, on the most challenging task Discovery with 174 labels, all the LLMs struggle to understand the task definition, thus reaching a performance close to zero. This suggests a notable gap in current LLM capabilities for processing and understanding long, context-rich sequences. Further analysis revealed a tendency among models to favor predictions for labels presented toward the end of the sequence. Their ability to reason over multiple pieces in the long sequence is yet to be improved. Our study reveals that long context understanding and reasoning is still a challenging task for the existing LLMs. We believe LongICLBench could serve as a more realistic evaluation for the future long-context LLMs.