算法语言模型与神经编译库
Algorithmic Language Models with Neurally Compiled Libraries
AI总结：
针对大语言模型缺乏真正算法能力的问题，本文提出用神经编译库增强LLaMA3，通过添加内存、寄存器和自适应循环等组件，并定义直接编译算法的可微分起始库，初步验证了在简单算法任务上微调小模型的可行性。
摘要：
推理和规划等重要任务本质上是算法性的，这意味着要稳健地解决它们，需要获取真正的推理或规划算法，而不是捷径。大型语言模型缺乏真正的算法能力，主要原因是神经网络优化算法的局限性、其优化数据和优化目标，以及架构的表达能力不足。为了解决这个问题，我们的论文提出用基本操作和复杂可微分程序的库来增强大型语言模型，这样常见算法就不必从头学习。我们在基于LLaMA3构建的Transformer架构中添加了内存、寄存器、基本操作和自适应循环。然后，我们定义了一种方法，将算法直接编译成可微分的起始库，该库被原生使用并传播梯度以进行优化。在这项初步研究中，我们探索了用可微分计算机增强LLaMA3的可行性，例如通过在具有可变计算深度的简单算法任务上微调小型Transformer。
英文摘要：
Important tasks such as reasoning and planning are fundamentally algorithmic, meaning that solving them robustly requires acquiring true reasoning or planning algorithms, rather than shortcuts. Large Language Models lack true algorithmic ability primarily because of the limitations of neural network optimization algorithms, their optimization data and optimization objective, but also due to architectural inexpressivity. To solve this, our paper proposes augmenting LLMs with a library of fundamental operations and sophisticated differentiable programs, so that common algorithms do not need to be learned from scratch. We add memory, registers, basic operations, and adaptive recurrence to a transformer architecture built on LLaMA3. Then, we define a method for directly compiling algorithms into a differentiable starting library, which is used natively and propagates gradients for optimization. In this preliminary study, we explore the feasability of augmenting LLaMA3 with a differentiable computer, for instance by fine-tuning small transformers on simple algorithmic tasks with variable computational depth.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
