Hyper-multi-step: The Truth Behind Difficult Long-context Tasks
Authors: name: Yijiong Yu

Long-context language models (LCLM), characterized by their extensive context
window, is becoming increasingly popular. Meanwhile, many long-context
benchmarks present challenging tasks that even the most advanced LCLMs struggle
to complete. However, the underlying sources of various challenging
long-context tasks have seldom been studied. To bridge this gap, we conduct
experiments to indicate their difficulty stems primarily from two basic issues:
"multi-matching retrieval," which requires the simultaneous retrieval of
multiple items, and "logic-based retrieval," which necessitates logical
judgment within retrieval criteria. These two problems, while seemingly
straightforward, actually exceed the capabilities of LCLMs because they are
proven to be hyper-multi-step (demanding numerous steps to solve) in nature.
This finding could explain why LLMs struggle with more advanced long-context
tasks, providing a more accurate perspective for rethinking solutions for them.