提出一种新方法Large Language Model Programs(LLM程序),通过将预训练语言模型嵌入到算法或程序中,来进一步扩展语言模型的能力,解决更复杂的任务。该方法将主要问题递归地分解为子问题,然后利用模型进行求解,同时提高输入和输出的粒度,以便在不需要微调的情况下对模型的能力进行开发和测试。 @爱可可-爱生活 [LG]《...
为了启用 LMP,论文实现了 LMQL(Language Model Query Language),它利用来自 LMP prompt的约束和控制流,以生成有效的推理过程,最大限度地减少对底层语言模型的昂贵调用的数量。 论文通过实验证明 LMQL 可以以直观的方式追上各种最先进的提示方法,特别是促进使用现有高级 API 难以实现的交互流程。 论文实验的评估表明我...
在接收到这些输入后,LLMs进行推理并产生输出,包括生成的语言模型程序(Language Model Programs, LMPs)P和推理思想R。生成的LMP被发送到执行器在环境中执行,而推理思想帮助LLMs生成更合理的驾驶策略。不过要注意的是,这是一个通用概念,具体实现可能因不同应用而异。 人类指令和评估 人类的指令I和评估F直接以自然...
然而,要获得state-of-the-art的性能或针对特定任务调整语言模型,需要实施复杂的任务和模型特定程序(complex task- and model- specific programs),这可能仍需要临时交互(ad-hoc interaction)。 基于此,论文提出了一种新的概念,即语言模型编程(Language Model Programming, LMP)。LMP将语言模型提示从纯文本提示推广到文...
1. What is a large language model? In a nutshell, a large language model (LLM) is a natural language processing computer program. LLMs are primarily known for driving popular AI tools such as Open AI’s ChatGPT and Google’s Gemini. Trained using artificial neural networks—which aim to...
PaLM 2 (Bison-001) is a large language model from Google AI. It is focused on commonsense reasoning and advanced coding. PaLM 2 has also been shown to outperform GPT-4 in reasoning evaluations, and it can also generate code in multiple languages. ...
we present an approach to augment these large language models with post-processing steps based on program analysis and synthesis techniques, that understand the syntax and semantics of programs. Further, we show that such techniques can make use of user feedback and improve with usage. We pr...
Difference Between Large Language Models and Generative AI General Architecture What is a Large Language Model (LLM)? LLMs are the types of artificial intelligence (AI) systems that can produce written answers to questions that resemble those of a human. They are known as large language models ...
A large language model needs to be trained using a large dataset, which can include structured or unstructured data. Once initial pre-training is complete, the LLM can be fine-tuned, which may involve labeling data points to encourage more precise recognition of different concepts and meanings. ...
The probability of a word sequence within a conventional language model can be approximated through techniques such as n-grams or Hidden Markov Models. The chain rule is one method that can be utilized when calculating the probability: \(P\left( w_1, w_2, \ldots , w_n\right) =P\left...