title: Shifting Long-Context LLMs Research from Input to Output
summary: Recent advancements in long-context Large Language Models (LLMs) have
  primarily concentrated on processing extended input contexts, resulting in
  significant strides in long-context comprehension. However, the equally
  critical aspect of generating long-form outputs has received comparatively less
  attention. This paper advocates for a paradigm shift in NLP research toward
  addressing the challenges of long-output generation. Tasks such as novel
  writing, long-term planning, and complex reasoning require models to understand
  extensive contexts and produce coherent, contextually rich, and logically
  consistent extended text. These demands highlight a critical gap in current LLM
  capabilities. We underscore the importance of this under-explored domain and
  call for focused efforts to develop foundational LLMs tailored for generating
  high-quality, long-form outputs, which hold immense potential for real-world
  applications.
paper_authors._id: 67d39576de5ce3cc428b1909
paper_authors.name: Yuhao Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T10:48:49.288Z
paper_authors._id: 63369da91ba5d5ece24118a4
paper_authors.avatarUrl: /avatars/67889e1ecadb04100a77bc8b5284c6fd.svg
paper_authors.fullname: wuyuhao
paper_authors.type: user
paper_authors.user: mozhu
paper_authors._id: 67d39576de5ce3cc428b190a
paper_authors.name: Yushi Bai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T10:48:17.625Z
paper_authors._id: 64ed568ccf6118a9379a61b8
paper_authors.avatarUrl: /avatars/6d040cbcb4a9b624cbe64c9d01cd5c88.svg
paper_authors.fullname: Yushi Bai
paper_authors.type: user
paper_authors.user: bys0318
paper_authors._id: 67d39576de5ce3cc428b190b
paper_authors.name: Zhiqing Hu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-14T08:56:31.682Z
paper_authors._id: 637f228152229c63921119c3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/637f228152229c63921119c3/acwXorra1r9_7i3KlBFjS.jpeg
paper_authors.fullname: Zhiqiang Hu
paper_authors.type: user
paper_authors.user: Zhiqiang007
paper_authors._id: 67d39576de5ce3cc428b190c
paper_authors.name: Shangqing Tu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T10:48:09.780Z
paper_authors._id: 648c48d8c0ddeee6df5b6d22
paper_authors.avatarUrl: /avatars/8706b0b16dfc332b96c91d3ced31bd0b.svg
paper_authors.fullname: Shangqing Tu
paper_authors.type: user
paper_authors.user: tsq2000
paper_authors._id: 67d39576de5ce3cc428b190d
paper_authors.name: Ming Shan Hee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T10:48:03.137Z
paper_authors._id: 64ba81d60a30149d57cfadb8
paper_authors.avatarUrl: /avatars/da8f9813975606ea6b1dec0c9767831d.svg
paper_authors.fullname: Hee Ming Shan
paper_authors.type: user
paper_authors.user: mingshan
paper_authors._id: 67d39576de5ce3cc428b190e
paper_authors.name: Juanzi Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T10:47:53.216Z
paper_authors._id: 65df8cbc2705d9672f55d1aa
paper_authors.avatarUrl: /avatars/63e46f15bb76bd9d4508fd0f54f39829.svg
paper_authors.fullname: Juanzi Li
paper_authors.type: user
paper_authors.user: juanli
paper_authors._id: 67d39576de5ce3cc428b190f
paper_authors.name: Roy Ka-Wei Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T10:47:46.961Z
paper_authors._id: 64b94cc0e3d41dbd6974ae45
paper_authors.avatarUrl: /avatars/5edb9d9465addfceccef04c4465a34e6.svg
paper_authors.fullname: Roy Ka-Wei lee
paper_authors.type: user
paper_authors.user: sroylee
paper_title: Shifting Long-Context LLMs Research from Input to Output
paper_summary: Recent advancements in long-context Large Language Models (LLMs) have
  primarily concentrated on processing extended input contexts, resulting in
  significant strides in long-context comprehension. However, the equally
  critical aspect of generating long-form outputs has received comparatively less
  attention. This paper advocates for a paradigm shift in NLP research toward
  addressing the challenges of long-output generation. Tasks such as novel
  writing, long-term planning, and complex reasoning require models to understand
  extensive contexts and produce coherent, contextually rich, and logically
  consistent extended text. These demands highlight a critical gap in current LLM
  capabilities. We underscore the importance of this under-explored domain and
  call for focused efforts to develop foundational LLMs tailored for generating
  high-quality, long-form outputs, which hold immense potential for real-world
  applications.
paper_ai_summary: Recent advancements in LLMs have focused on processing long input contexts but have overlooked generating long, coherent, and logically consistent outputs, necessitating a shift in research focus.
paper_ai_keywords: Large Language Models long-context comprehension long-output generation novel writing long-term planning complex reasoning coherent outputs contextually rich logically consistent high-quality long-form outputs