Are Long-LLMs A Necessity For Long-Context Tasks? (2405.15318v1)

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.

• The paper introduces LC-Boost, showing that short LLMs can address long-context tasks by adaptively accessing and utilizing relevant segments.
• Empirical results demonstrate that LC-Boost matches or surpasses brute-force long-LLM methods while significantly reducing computational resources.
• The framework challenges the conventional need for larger context models, offering a scalable and efficient approach to long-context task processing.

LC-Boost: A Framework for Efficiently Solving Long-Context Tasks with Short LLMs

The development and practical deployment of long-context LLMs remain a challenging issue in the field of natural language processing. Despite notable strides in enhancing these models, challenges related to the extensive computational and resource demands of long-LLMs persist. This paper presents a compelling argument that numerous long-context tasks can be effectively addressed using short-context LLMs with an innovative method called LC-Boost (Long-Context Bootstrapper). This framework allows short-LLMs to tackle long-context tasks by adaptively accessing and utilizing necessary portions of the context, demonstrating substantial improvements in performance and resource efficiency.

Introduction

The introduction highlights the widespread adoption of LLMs across diverse real-world applications, many of which involve processing long-sequence inputs—such as document summarization and question answering (QA). Traditional approaches favor extending the context sizes of LLMs (e.g., Llama-1 with 2K, Llama-2 with 4K, Llama-3 with 8K) to handle long contexts. However, these methods incur considerable costs in terms of learning, deployment, and resource consumption. Additionally, extensive fine-tuning required for longer contexts may undermine the general capabilities of LLMs on short-context tasks. Despite ongoing efforts, it remains an open problem to find efficient solutions for long-context processing.

Argument and Proposal

The authors argue that most long-context tasks can be solved by strategically utilizing short contexts within the long-context inputs. This perspective aligns with the way humans and modern computers decompose and solve long problems based on limited memory capacities. To operationalize this argument, the paper introduces LC-Boost, which employs short-LLMs in a bootstrapping manner to navigate and solve long-context tasks. The core of LC-Boost consists of two reasoning steps:

1. Access: Determining how to access the relevant part of the context.
2. Utilize: Deciding how to effectively use the accessed context.

This method dynamically adapts to the specifics of each task, enabling LC-Boost to efficiently handle a diverse range of long-context problems.

Empirical Validation

The paper provides empirical validation through a comprehensive evaluation on various tasks from long-context benchmarks, including single-doc QA, multi-doc QA, summarization, few-shot learning, synthetic tasks, and code completion. The results demonstrate that LC-Boost achieves performance on par with, or even exceeding, that of brute-force long-LLM approaches such as GPT-4-128K, with a marked reduction in resource consumption. In particular, LC-Boost surpasses short-LLM surrogates that utilize predefined access and usage strategies, underscoring the importance of reasoning and adaptability.

Contributions

The contributions of this paper are threefold:

1. Problem Identification: It identifies the problem of solving long-context tasks with short-LLMs, presenting a novel perspective on long-context task solvability.
2. Framework Proposal: It proposes LC-Boost, a general framework capable of adaptively handling a broad spectrum of long-context tasks by reasoning about context access and utility.
3. Empirical Verification: It provides empirical evidence of LC-Boost's effectiveness through superior performance results achieved with lower resource consumption.

Future Implications

The findings and proposed framework have both practical and theoretical implications. Practically, LC-Boost offers a more cost-effective and sustainable approach for deploying LLMs in real-world applications involving long-context inputs. Theoretically, it challenges the prevailing notion that extending context sizes is the optimal route for improving long-context task performance, advocating instead for intelligent context management through shorter LLMs.

Conclusion

The paper successfully challenges the necessity of long-LLMs for long-context tasks by introducing the LC-Boost framework. This method showcases that strategic reasoning in accessing and utilizing context can lead to efficient and effective solutions, reducing the resource burdens typically associated with long-context LLMs. Future research could explore further optimizations of LC-Boost’s decision-making processes and expand its application to even broader domains, ensuring sustainable and scalable growth in AI capabilities.

In summary, the LC-Boost framework represents a significant advancement in the efficient processing of long-context tasks, highlighting a promising direction for the future development of LLMs while addressing critical concerns related to computational resource consumption.