We see quite a potential in employing large language models (LLMs) to simplify creating and refining the DEECo architectures. Since this constitutes a large research scope, we focus in this paper on initial experiments demonstrating how well generic LLMs understand the advanced concepts of ensemble...
LLM 的工作原理 相关内容 生成式 AI 是一种人工智能,能够创建原始内容,例如自然语言、图像、音频和代码。 生成式 AI 的输出基于用户提供的输入。 用户与生成式 AI 交互的一种常见方法是通过使用自然语言作为输入的聊天应用程序。 OpenAI 开发的 ChatGPT 就是一个常见的示例。 使用自然语言作为输入的生成 AI 应用...
Prompt and response funnel. We compute metrics at each stage to understand how the user interacts with the model. Some stages (e.g., editing the response) are not applicable to all scenarios (e.g., chat). Prompt and Response Funnel:As the user interacts with ...
We show that large language models (LLMs), such as ChatGPT, can guide the robot design process, on both the conceptual and technical level, and we propose new human–AI co-design strategies and their societal implications. This is a preview of subscription content, access via your ...
Part 1:Is my Chatbot Ready for Production?– A 10,000 foot overview to LLMOps After Generative AI burst onto the scene, businesses rushed to learn and leverage the technology. The first wave of adoption has most often materialized as retrieval augmented generation (RAG) chatbot products. Wh...
Researchers, educators and companies are experimenting with ways to turn flawed but famous large language models into trustworthy, accurate ‘thought partners’ for learning.
这表明,即使LLMs能够处理长上下文,它们不一定会在提示过长的情况下表现更好。 3.3.2 Impact of Database Prompt 由于纳入示范数据库可能会导致Codex的性能下降,因此我们将数据库提示实验集中在使用一个示范数据库,结合不同数量的示范示例。表2呈现了使用不同数据库提示的Codex的执行准确性。问题3:不同的数据库提示...
Generative AI is a powerful tool for streamlining the workflow of creatives, engineers, researchers, scientists, and more. The use cases and possibilities span all industries and individuals. Generative AI models can take inputs such as text, image, audio, video, and code and generate new conten...
Recently, there has been a growing interest in studying how to construct better code instruction tuning data. However, we observe Code models trained with these datasets exhibit high performance on HumanEval but perform worse on other benchmarks such as LiveCodeBench. Upon further investigation, we...
Second, priors over individual choices do not fully explain choices-only accuracy, hinting that LLMs use the group dynamics of choices. Third, LLMs have some ability to infer a relevant question from choices, and surprisingly can sometimes even match the original question. Inferring the original ...