Key Points
1. The paper argues that long-context tasks, despite being associated with long-sequence inputs, can be addressed by merely working with short-contexts in a strategic way.
2. It proposes a framework called LC-Boost (Long-Context Bootstrapper) that enables a short-LLM to address long-context tasks in a bootstrapping manner.
3. LC-Boost prompts itself to reason for two critical decisions: 1) how to access the appropriate part of context within the input, and 2) how to make effective use of the accessed context.
4. 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.
5. The authors comprehensively evaluate different types of tasks from popular long-context benchmarks, where LC-Boost is able to achieve substantially improved performance with much smaller resource consumption.
6. The paper identifies the research problem of solving long-context problems with short-LLMs as an important yet understudied area.
7. The authors propose LC-Boost as a novel framework that can adaptively handle general long-context tasks based on reasoning about how to access and utilize the long context.
8. Experimental results show that LC-Boost can achieve equivalent or better performance compared to strong long-LLMs, while consuming significantly less resources.
9. The paper discusses the broader impact of this work in terms of reducing the energy consumption and environmental impact of large language models as they become ubiquitous in the future.
Summary
This research paper proposes a framework called LC-Boost (Long-Context Bootstrapper) to enable short-LLMs (large language models with limited context length) to address long-context tasks effectively and efficiently. The paper argues that long-LLMs are not a necessity for solving long-context tasks, as many common long-context tasks can be solved by working with oracle short-contexts within the long-context tasks' inputs.
Key Idea: Adaptive Context Processing
The key idea behind LC-Boost is to prompt the short-LLM to reason about two critical decisions: 1) how to access the appropriate part of the context within the input, and 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 diverse long-context processing problems.
The paper presents comprehensive experiments on 12 datasets covering various long-context tasks such as question-answering, summarization, and code completion. The results show that LC-Boost is able to achieve substantially improved performance compared to using long-LLMs, while consuming much smaller computational resources. This indicates that long-LLMs are not necessary for solving long-context tasks, and short-LLMs can be effectively leveraged to address these challenges through strategic context processing as proposed by LC-Boost.
Theoretical Analysis: Long-Context Tasks
The paper also provides theoretical analysis to support the claim that most long-context tasks are short-context solvable. It demonstrates that by decomposing the long context into shorter contexts and processing them individually, the mutual information required to solve the task can often be preserved, making the long-context tasks amenable to short-context processing.
Overall, this work presents a novel and efficient approach to handling long-context tasks, challenging the common assumption that long-LLMs are essential for such scenarios. The findings have important implications for developing more sustainable and resource-efficient language modeling solutions for real-world applications.
Reference: https://arxiv.org/abs/2405.153...