在处理长文本内容时，大型语言模型 (LLM) 的部署面临着数据稀疏性限制、实现复杂性和训练效率等挑战。为了克服这些问题，研究人员提出了一种名为在线长文本处理 (OLP) 的新范式，该范式专门设计用于实时处理大量数据，并根据输入对各种媒体流进行组织和评估。为了进一步优化 LLM 的部署，研究人员开发了角色强化学习 (Role-RL) 框架，该框架利用实时性能数据来根据其最佳角色自动部署不同的 LLM。Role-RL 通过评估每个 LLM 的重要性能指标（如速度、准确性和成本效益）来评估其适合性，然后动态地将每个 LLM 分配到最适合其任务的特定角色，从而最大限度地提高系统的整体效率。

🤖 **在线长文本处理 (OLP) 范式**：针对处理长文本内容的挑战，OLP 是一种专门设计用于实时处理大量数据的新范式。它可以有效地组织和评估各种媒体流，例如自动新闻更新、实时电商平台和短视频平台。

🤖 **角色强化学习 (Role-RL) 框架**：为了优化 LLM 在 OLP 管道中的部署，Role-RL 使用实时性能数据来动态分配不同的 LLM，根据其最佳角色来执行任务。该框架通过评估每个 LLM 的速度、准确性和成本效益等重要性能指标来确定其适合性。

🤖 **实验结果**：在 OLP-MINI 数据集上进行的广泛研究表明，OLP 和 Role-RL 框架的结合带来了显著的优势。该系统取得了 93.2% 的平均召回率，证明了其可靠性和高效地检索相关信息的能力。此外，该框架还将 LLM 部署的成本降低了 79.4%，展示了其经济可行性。

🤖 **主要贡献**：研究人员引入了 Role-RL 框架，该框架旨在根据 LLM 在特定任务上的实时性能来战略性地将它们部署到最适合的角色中，从而确保 LLM 的高效和准确部署。他们还提出了 OLP 管道来处理长文本内容，以及用于验证和测试的 OLP-MINI 数据集。

🤖 **结论**：Role-RL 框架与 OLP 管道相结合，显著提高了 LLM 在长文本处理任务中的效率和成本效益，为高效部署大型语言模型提供了新的解决方案。

Training Large Language Models (LLMs) that can handle long-context processing is still a difficult task because of data sparsity constraints, implementation complexity, and training efficiency. Working with documents of infinite duration, which are typical in contemporary media formats like automated news updates, live-stream e-commerce platforms, and viral short-form movies, makes these problems very clear. Online Long-context Processing (OLP) is a new paradigm that is used to overcome this.

The OLP paradigm is specifically made to handle and process massive amounts of data in real-time, arranging and evaluating various media streams as they come in. OLP can assist in segmenting and categorizing streaming transcripts into relevant areas, such as product descriptions, pricing talks, or customer interactions, in live e-commerce. It can assist in organizing a constant stream of news data into groups such as facts, views, and projections in automated news reporting, which enhances the information’s accuracy and user-friendliness.

However, trying to choose the best available LLM from an ever-increasing pool of models presents another difficulty. It is challenging to identify a model that performs well in all of these areas because each one differs in terms of cost, response time, and performance. In response to this problem, a framework known as Role Reinforcement Learning (Role-RL) has been introduced in a recent research paper from South China Normal University, Toronto University and Zhejiang University. Role-RL uses real-time performance data to automate the deployment of various LLMs in the OLP pipeline according to their ideal roles.

Each LLM is assessed by Role-RL based on important performance metrics such as speed, accuracy, and cost-effectiveness. Role-RL maximizes the system’s overall efficiency by dynamically assigning each LLM to the tasks for which they are most suitable based on these evaluations. With this method, resources can be used more strategically, guaranteeing that high-performing LLMs take on the most important jobs and that more economical models are used for simpler procedures.

Extensive studies on the OLP-MINI dataset have revealed that the combined OLP and Role-RL framework yielded notable benefits. With an average recall rate of 93.2%, it achieved an OLP benchmark, demonstrating the system’s ability to reliably and frequently retrieve pertinent information. This framework was also responsible for a 79.4% cost reduction for LLM deployment, demonstrating its economic viability in addition to its efficiency.

The team has summarized their primary contributions as follows.

The Role Reinforcement Learning (Role-RL) framework, has been introduced, which is intended to strategically place different LLMs in the roles that best fit them according to how well they perform in real-time on certain tasks. This guarantees that LLMs are deployed as efficiently and accurately as possible.

To manage long-context jobs, the team has suggested Online Long-context Processing (OLP) pipeline. The pipeline processes and organises data from long documents or media streams in a successful manner. OLP-MINI dataset has also been presented for validation and testing.

The benchmark average recall rate of 93.2% has been attained using the Role-RL framework in conjunction with the OLP pipeline. The framework also reduces LLM expenses by 79.4%. In addition, the recall rate is increased by 53.6 percentage points using the OLP pipeline as opposed to non-OLP procedures.

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