大型语言模型（LLM）在上下文学习（ICL）方面表现出色，ICL是一种仅使用输入提示中包含的几个示例来训练模型完成任务的技术，无需进一步训练。ICL的主要特征之一是这些模型可以在单个推理会话中同时处理多个计算上不同的ICL任务，这种现象被称为叠加。任务叠加意味着，当LLM在同一输入提示中为每个任务提供相关示例时，它可以同时处理和生成多个任务的响应。 威斯康星大学麦迪逊分校、密歇根大学和微软研究院最近的一项研究实证支持了不同类型和规模的LLM中任务叠加的发生。即使是通过ICL训练一次学习一项任务的模型也表现出这种同时处理多个任务的能力。这意味着同时处理能力是一种内在属性，它是在推理过程中产生的，而不是与训练类型直接相关。 从理论上讲，任务叠加的概念与构成大多数现代LLM基础的Transformer架构的能力相一致。Transformer以其处理数据中复杂模式和依赖关系的能力而闻名，它们利用自注意力等技术，使它们能够根据需要专注于不同的输入段。这种多功能性使它们能够在单个提示中表示和解释特定于任务的信息，使它们能够生成同时解决多个任务的响应。 该研究还探索了LLM内部处理此任务叠加的方式。它研究了它们如何整合和处理不同的任务向量，即特定于每个任务的内部表示。本质上，该模型通过在推理过程中修改其内部状态来平衡这些特定于任务的表示。这使得模型能够为输入中呈现的每种任务类型生成准确的输出。 该研究的主要结论之一是，更大的LLM通常能够同时处理更多任务。随着模型规模的增加，模型可以同时处理更多任务并提高准确性，因为它校准了其输出概率。这表明，更大的模型更能为它们正在执行的所有任务生成更准确、更可靠的答案，并且更擅长多任务处理。

🤔 **任务叠加：LLM的一种新能力** 大型语言模型（LLM）在单个推理会话中同时处理多个计算上不同的ICL任务的能力被称为任务叠加。这意味着LLM可以根据同一输入提示中提供的多个示例，同时完成不同的任务。

🧐 **叠加的实证研究** 威斯康星大学麦迪逊分校、密歇根大学和微软研究院的研究人员发现，即使是通过ICL训练一次学习一项任务的模型也表现出这种同时处理多个任务的能力。这意味着这种能力不是与训练类型直接相关，而是模型本身的内在属性。

🧠 **Transformer架构的贡献** 研究人员认为，Transformer架构的结构使得LLM能够同时处理多个任务。Transformer使用自注意力机制来处理数据中的复杂模式和依赖关系，这使得它们能够在单个提示中表示和解释特定于任务的信息，从而实现任务叠加。

📊 **模型规模与叠加能力的关系** 研究发现，更大的LLM通常能够同时处理更多任务。随着模型规模的增加，模型可以同时处理更多任务并提高准确性，因为它校准了其输出概率。

💡 **LLM作为叠加模拟器** 研究结果支持LLM作为叠加模拟器的观点。这意味着LLM可以在自身内部模拟各种可能的特定于任务的模型，使其能够根据输入的上下文灵活地做出反应。

❓ **未来研究方向** 这项研究为LLM在处理复杂、多方面任务方面的局限性和潜在用途提供了见解。未来的研究将集中在深入理解LLM如何同时完成多个任务的机制上，例如，这是否源于它们的训练和优化，还是源于模型更深层的结构属性。

Large Language Models (LLMs) have demonstrated remarkable proficiency in In-Context Learning (ICL), which is a technique that teaches them to complete tasks using just a few examples included in the input prompt and no further training. One of the primary features of ICL is that these models can manage several computationally different ICL tasks simultaneously in a single inference session; the phenomenon is called superposition. Task superposition means that when an LLM is provided relevant examples for each task within the same input prompt, it can process and produce responses for several tasks at once.

In a recent study from the University of Wisconsin-Madison, the University of Michigan, and Microsoft Research, the occurrence of task superposition across different LLM kinds and scales has been empirically supported. Even models taught to learn one task at a time using ICL exhibit this capacity to manage several tasks simultaneously. This implies that the capacity for simultaneous processing is an intrinsic trait that arises throughout the inference process rather than being directly related to the type of training.

Theoretically, the idea of task superposition fits in with the capabilities of transformer architectures, which constitute the basis of the majority of contemporary LLMs. By using techniques like self-attention, which enables them to concentrate on various input segments as required, transformers are renowned for their capacity to handle intricate patterns and dependencies in data. This versatility enables them to represent and interpret task-specific information within a single prompt, making it viable for them to generate responses that simultaneously address numerous tasks.

The study has also explored the internal handling of this task superposition by LLMs. It looks at how they integrate and handle various task vectors, i.e., the internal representations that are specific to each task. In essence, the model balances these task-specific representations by modifying its internal state during inference. This enables the model to generate accurate outputs for every task type that is presented in the input.

One of the study’s main conclusions is that larger LLMs are typically better able to manage several activities at once. The model can handle more jobs concurrently and improves accuracy when calibrating its output probabilities as its size grows. This indicates that larger models are more capable of producing more precise and dependable answers for all of the jobs they are doing and are better at multitasking.

These revelations have clarified the fundamental powers of LLMs and provide credence to the idea that these models are a superposition of simulators. According to this viewpoint, LLMs can simulate a variety of possible task-specific models inside of themselves, enabling them to react flexibly depending on the input’s context. These results also raise interesting concerns about how LLMs actually accomplish several tasks at once, including whether this is a result of their training and optimization or if it stems from a deeper structural property of the model. Gaining a deeper understanding of these mechanisms may help identify the limitations and possible uses of LLMs in managing intricate, multifaceted jobs.

The team has shared their primary contributions as follows.

Through comprehensive experimental and theoretical analysis, the team has shown that task superposition is a common phenomenon across different pretrained LLM families, including GPT-3.5, Llama-3, and Qwen.

The team has empirically shown that task superposition can arise even when the model is taught with instances of only one task at a time, suggesting that this ability is not primarily related to multi-task training.

A theoretical framework has been offered that shows transformer models’ innate ability to perform numerous tasks at once by utilizing their structure for parallel task processing.

The study has explored how LLMs internally manage and mix task vectors and finds that convex combinations of these vectors can replicate the impact of superposition.

It has been found that larger models are able to handle more tasks at once and capture the distribution of in-context instances more accurately, which results in more accurate results.

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