正文：
在自然语言处理领域，大规模语言模型（LLM）已经展现出了强大的能力，但在具体任务上的表现仍存在不足。为了解决这一问题，清华大学和卡内基梅隆大学的研究团队提出了一种新的方法——SELF-GUIDE，让LLM能够自主合成数据进行学习，从而在特定任务上取得了显著的提升。
SELF-GUIDE方法的核心在于一个高效的多阶段生成机制。首先，研究者根据任务类型制定不同的提示模板，然后利用语言模型生成输入数据。对于生成型任务，使用简单的提示模板；而对于分类型任务，则采用更为复杂的策略，从标签空间中随机选择标签作为伪标签，引导模型生成与标签相对应的输入内容。
在生成和过滤阶段，SELF-GUIDE方法逐步扩展LLM生成的输入集，减少重复，并通过基于规则的过滤器去除低质量的数据。在输出数据生成阶段，研究者向模型提供任务指令和原始示例，使模型对输入生成阶段产生的每一个输入进行标注，再进行一轮基于规则的过滤，以选择最终的合成数据集。
这一方法不仅减少了对于高质量人工标注数据的依赖，还提高了数据生成过程的效率和灵活性。更重要的是，它为LLM在特定任务上的性能提升提供了一种新的解决方案。
SELF-GUIDE方法的成功实施，不仅为学术界和工业界提供了新的研究思路，也为人工智能在各种自然语言处理任务中的应用提供了新的可能。随着这项研究的深入和应用范围的扩大，我们有理由相信，未来的LLM将更加智能，能够更好地服务于人类社会的各个方面。
英语如下：
Title: “Tsinghua-CMU Breakthrough: LLM Autonomously Synthesizes Data to Significantly Improve Task Performance”
Keywords: Synthetic Data, LLM Enhancement, Specific Task
News Content:
Title: Tsinghua University and Carnegie Mellon University Collaborate on a New Study to Allow LLM to Autonomously Synthesize Data for Significant Improvement in Specific Task Performance
Article:
In the field of natural language processing, large language models (LLMs) have demonstrated remarkable capabilities, yet their performance on specific tasks remains inadequate. To address this issue, a research team from Tsinghua University and Carnegie Mellon University has proposed a novel approach called SELF-GUIDE, which enables LLMs to autonomously synthesize data for learning, resulting in a significant boost in performance on specific tasks.
The core of the SELF-GUIDE method lies in an efficient multi-stage generation mechanism. First, researchers develop different prompt templates based on the task type and then use language models to generate input data. For generative tasks, simple prompt templates are used; for classification tasks, more complex strategies are employed, including randomly selecting labels from the label space as pseudo-labels to guide the model in generating input content corresponding to the labels.
During the generation and filtering stage, the SELF-GUIDE method progressively expands the input set generated by the LLM, reduces redundancy, and eliminates low-quality data through rule-based filters. In the output data generation stage, researchers provide task instructions and original examples to the model, enabling it to label each input generated in the input generation stage, followed by another round of rule-based filtering to select the final synthetic dataset.
This method not only reduces the dependency on high-quality manually annotated data but also enhances the efficiency and flexibility of the data generation process. More importantly, it offers a new solution for enhancing the performance of LLMs on specific tasks.
The successful implementation of the SELF-GUIDE method not only provides a new research direction for academia and industry but also opens up new possibilities for the application of artificial intelligence in various natural language processing tasks. As this research progresses and its application scope expands, we have reason to believe that future LLMs will be more intelligent and better serve various aspects of human society.
【来源】https://www.jiqizhixin.com/articles/2024-08-01-2
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