在人工智能领域的持续探索与创新中，Meta公司近期宣布成功开发了一项名为“System 2蒸馏技术”的突破性研究。这项技术的应用使得其构建的对话模型Llama 2的准确性达到了接近100%的水平，这一成果不仅标志着AI领域的一大进步，也为未来AI系统的持续学习能力带来了新的可能。
System 2蒸馏技术的核心理念在于提升AI系统在推理任务上的表现。通过这一技术，AI系统能够在处理表现不佳的推理任务时，显著提升其性能。这项技术的实现，不仅强化了AI系统在复杂决策和深度思考方面的处理能力，还为其成为未来持续学习AI系统的重要特征铺平了道路。
大语言模型（LLM）是AI领域中的一大热点，通常分为即时的System 1（快速反应）和System 2（慢速思考）两种策略。System 1注重快速反应，而System 2则侧重于深思熟虑的思维，通过生成中间思维，允许模型进行推理和规划，以完成任务或响应指令。在System 2的思维过程中，心理活动需要付出更多的努力，尤其是在System 1可能出错的情况下。System 1通常指的是Transformer的直接应用，能够根据输入直接生成响应，无需生成中间token。相比之下，System 2则涉及生成中间token的方法，如执行搜索、多次提示后再生成最终响应。
针对System 2推理，业界已经发展出一系列相关技术，包括思维链、思维树、思维图、分支解决合并、System 2 Attention、Rephrase and Respond (RaR)等。这些技术充分利用了明确的推理过程，往往能够带来更准确的结果，但同时也伴随着更高的推理成本和响应时间。
Meta公司的这项突破性研究不仅展示了AI领域在提升系统性能和优化推理策略方面的潜力，也为未来AI系统的持续学习和适应性提供了新的路径。随着技术的不断进步，AI系统将能够更好地理解复杂情境，更准确地进行决策，为人类社会带来更多的便利和创新。
英语如下：
News Title: “Meta’s System 2 Distillation Technique Elevates AI Inference Task Accuracy to Near-Perfect Levels”
Keywords: Meta, System 2, Distillation Technique
News Content: In the ongoing exploration and innovation in the field of artificial intelligence (AI), Meta recently announced the successful development of a groundbreaking research named “System 2 Distillation Technique.” This application has significantly enhanced the accuracy of its constructed dialogue model, Llama 2, to nearly 100%, marking a significant advancement in the AI domain and opening new possibilities for the future learning capabilities of AI systems.
The core concept of System 2 Distillation Technique lies in improving AI systems’ performance in inference tasks. Through this technique, AI systems can notably boost their performance when handling poorly performing inference tasks. The achievement of this technique not only strengthens AI systems’ capabilities in complex decision-making and deep thinking but also paves the way for future AI systems to become more adept at continuous learning.
Large language models (LLMs) are a major focus in the AI field, typically categorized into immediate (System 1) and slow (System 2) strategies. System 1 emphasizes quick responses, whereas System 2 focuses on thoughtful consideration, allowing models to engage in reasoning and planning to complete tasks or respond to instructions. During System 2’s thinking process, mental activities require more effort, especially when System 1 might fail. System 1 usually refers to the direct application of Transformers, which can generate responses based on input without the need for intermediate tokens. In contrast, System 2 involves methods of generating intermediate tokens, such as executing searches, generating final responses after multiple prompts.
Several related techniques for System 2 reasoning have been developed in the industry, including the chain of thought, tree of thought, graph of thought, branch resolution, System 2 Attention, and Rephrase and Respond (RaR). These techniques leverage clear reasoning processes, often resulting in more accurate outcomes but also come with higher reasoning costs and response times.
Meta’s pioneering research not only showcases the potential for enhancing system performance and optimizing reasoning strategies in AI but also provides new avenues for future AI systems to learn and adapt continuously. As technology advances, AI systems will be better equipped to understand complex scenarios, make more accurate decisions, and bring more convenience and innovation to human society.
【来源】https://www.jiqizhixin.com/articles/2024-07-15-8
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