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Large Language Models

Are Long-LLMs A Necessity For Long-Context Tasks?

Hongjin Qian, Zheng Liu, Peitian Zhang, Kelong Mao, Yujia Zhou, Xu Chen, Zhicheng Dou
May 24, 2024arXiv.org29 citations
arXivPDFDOI

29

Citations

1

Influential Citations

arXiv.org

Venue

2024

Year

Abstract

The learning and deployment of long-LLMs remains a challenging problem despite recent progresses. In this work, we argue that the long-LLMs are not a necessity to solve long-context tasks, as common long-context tasks are short-context solvable, i.e. they can be solved by purely working with oracle short-contexts within the long-context tasks' inputs. On top of this argument, we propose a framework called LC-Boost (Long-Context Bootstrapper), which enables a short-LLM to address the long-context tasks in a bootstrapping manner. In our framework, the short-LLM prompts itself to reason for two critical decisions: 1) how to access to the appropriate part of context within the input, 2) how to make effective use of the accessed context. By adaptively accessing and utilizing the context based on the presented tasks, LC-Boost can serve as a general framework to handle diversified long-context processing problems. We comprehensively evaluate different types of tasks from popular long-context benchmarks, where LC-Boost is able to achieve a substantially improved performance with a much smaller consumption of resource.

Analysis

Why This Paper Matters

This paper challenges the prevailing assumption that long-context tasks inherently require long-context LLMs. By arguing that many such tasks are actually short-context solvable, it opens the door to more efficient approaches. The proposed LC-Boost framework demonstrates that a short-LLM can match or exceed long-LLM performance on standard benchmarks, which is significant given the high computational and memory costs of training and deploying long-context models. This work could reduce the resource barrier for practitioners dealing with long documents, codebases, or conversations.

Technical Contributions

The key innovation is the LC-Boost framework, which enables a short-LLM to bootstrap its way through long-context tasks. The framework has two critical components: 1) adaptive context access, where the model decides which part of the input to focus on, and 2) effective context utilization, where it reasons about how to use that context for the task. This self-prompting mechanism allows the short-LLM to iteratively refine its understanding without needing to process the entire input at once. The approach is general and can be applied to various long-context benchmarks.

Results

The paper evaluates LC-Boost on popular long-context benchmarks, showing substantial performance improvements over baseline short-LLMs and competitive results with long-LLMs. Importantly, these gains come with much lower resource consumption—both in terms of memory and computation—making the method practical for real-world deployment. The results validate the core claim that many long-context tasks do not require long-LLMs.

Significance

This work has broad implications for the AI field. It suggests that the race toward ever-longer context windows may be partially misguided, and that smarter context selection and reasoning can achieve similar results. This could lead to more efficient model architectures and inference strategies, democratizing access to advanced NLP capabilities. It also opens new research directions in understanding what makes a task truly long-context and how to design models that can handle such cases without brute-force scaling.

long-contextshort-llmbootstrappingcontext-accessefficiencynlplarge-language-models
PromptingBenchmarks

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