Shifting Long-Context LLMs Research from Input to Output
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.
67d39576de5ce3cc428b1909
Yuhao Wu
admin_assigned
2025-03-14T10:48:49.288Z
63369da91ba5d5ece24118a4
/avatars/67889e1ecadb04100a77bc8b5284c6fd.svg
wuyuhao
user
mozhu
67d39576de5ce3cc428b190a
Yushi Bai
admin_assigned
2025-03-14T10:48:17.625Z
64ed568ccf6118a9379a61b8
/avatars/6d040cbcb4a9b624cbe64c9d01cd5c88.svg
Yushi Bai
user
bys0318
67d39576de5ce3cc428b190b
Zhiqing Hu
claimed_verified
2025-03-14T08:56:31.682Z
637f228152229c63921119c3
https://cdn-avatars.huggingface.co/v1/production/uploads/637f228152229c63921119c3/acwXorra1r9_7i3KlBFjS.jpeg
Zhiqiang Hu
user
Zhiqiang007
67d39576de5ce3cc428b190c
Shangqing Tu
admin_assigned
2025-03-14T10:48:09.780Z
648c48d8c0ddeee6df5b6d22
/avatars/8706b0b16dfc332b96c91d3ced31bd0b.svg
Shangqing Tu
user
tsq2000
67d39576de5ce3cc428b190d
Ming Shan Hee
admin_assigned
2025-03-14T10:48:03.137Z
64ba81d60a30149d57cfadb8
/avatars/da8f9813975606ea6b1dec0c9767831d.svg
Hee Ming Shan
user
mingshan
67d39576de5ce3cc428b190e
Juanzi Li
admin_assigned
2025-03-14T10:47:53.216Z
65df8cbc2705d9672f55d1aa
/avatars/63e46f15bb76bd9d4508fd0f54f39829.svg
Juanzi Li
user
juanli
67d39576de5ce3cc428b190f
Roy Ka-Wei Lee
admin_assigned
2025-03-14T10:47:46.961Z
64b94cc0e3d41dbd6974ae45
/avatars/5edb9d9465addfceccef04c4465a34e6.svg
Roy Ka-Wei lee
user
sroylee
Shifting Long-Context LLMs Research from Input to Output
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.
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.
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