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Shifting Long-Context LLMs Research from Input to Output

Yuhao Wu, Yushi Bai, Zhiqing Hu, Shangqing Tu, Ming Shan Hee, Juanzi Li, Roy Ka-Wei Lee

TL;DR

The paper argues for shifting NLP research from primarily improving long-input processing to advancing long-output generation in LLMs, defining long-output LLMs as models optimized for producing coherent, extended text. It surveys current progress across data, benchmarks, and models, highlighting real-world demand in domains like healthcare and law, and identifies key challenges in data alignment, benchmark coverage, and training/inference efficiency. The authors discuss real-world applications such as creative writing and long-chain-of-thought tasks, and outline opportunities including real-world data collection, hybrid data strategies, improved evaluation, and architectural innovations to reduce latency and memory demands. They also present alternative viewpoints, acknowledging that long-output generation may not always be necessary and that improving input handling could be a precursor to long-form generation. Overall, the paper calls for targeted foundational research to develop robust, scalable long-output LLMs with practical impact across industries and tasks requiring extensive and coherent text generation.

Abstract

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.

Shifting Long-Context LLMs Research from Input to Output

TL;DR

The paper argues for shifting NLP research from primarily improving long-input processing to advancing long-output generation in LLMs, defining long-output LLMs as models optimized for producing coherent, extended text. It surveys current progress across data, benchmarks, and models, highlighting real-world demand in domains like healthcare and law, and identifies key challenges in data alignment, benchmark coverage, and training/inference efficiency. The authors discuss real-world applications such as creative writing and long-chain-of-thought tasks, and outline opportunities including real-world data collection, hybrid data strategies, improved evaluation, and architectural innovations to reduce latency and memory demands. They also present alternative viewpoints, acknowledging that long-output generation may not always be necessary and that improving input handling could be a precursor to long-form generation. Overall, the paper calls for targeted foundational research to develop robust, scalable long-output LLMs with practical impact across industries and tasks requiring extensive and coherent text generation.

Abstract

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 Structure

This paper contains 53 sections, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Advancements in Long-Context LLMs (Inputs).
  3. The Case for Prioritizing Long Output.
  4. Why Long-Output LLMs have been Overlooked?
  5. Why Care about the Long Output Domain?
  6. Paper Organization.
  7. Long-Output LLMs
  8. High Demand, Low Research Focus
  9. High Demand.
  10. Low Research Focus:
  11. Defining Long-Output LLMs
  12. Input: Context Handling Capabilities.
  13. Output: Length and Quality of Generated Text.
  14. Current State of Long-Output LLMs
  15. Data
  16. ...and 38 more sections

Figures (8)

  • Figure 1: Difference between long-input and long-output LLMs.
  • Figure 2: Proportion of real-user demand: The aforementioned 2K (words) range refers to the interval [2K, 4K), and similarly for the other ranges. Solid color fill for input demand, slash fill for output.
  • Figure 3: ML and NLP Conf Long-context Research Trends Statistics (sorted by conference date). Solid color fill for Input-paper, slash fill for Output-paper.
  • Figure 4: UMAP visualization results for different SFT datasets. WildChat is derived from the long output demands of real users, filtered and referenced in Section \ref{['sec:2.2']}.
  • Figure 5: UMAP visualization results for different benchmark. We use the instructions from the benchmark to evaluate whether the benchmark assesses a wide range of long-output demand.
  • ...and 3 more figures