title: Hyper-multi-step: The Truth Behind Difficult Long-context Tasks
summary: 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.
paper_authors._id: 6706260a18b86c7e905baa92
paper_authors.name: Yijiong Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-09T07:37:08.228Z
paper_authors._id: 6374c494958cd71fa7ea0a9d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6374c494958cd71fa7ea0a9d/2YCKv6tXCZXtsIOFIIXjs.png
paper_authors.fullname: yuyijiong
paper_authors.type: user
paper_authors.user: yuyijiong
paper_title: Hyper-multi-step: The Truth Behind Difficult Long-context Tasks
paper_summary: 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.
paper_ai_summary: Detailed experiments reveal that long-context language models struggle with tasks primarily due to hyper-multi-step challenges like multi-matching and logic-based retrieval.
paper_ai_keywords: long-context language models multi-matching retrieval logic-based retrieval hyper-multi-step