这里说few-shot其实是a few examples,而zero-shot其实是instructions describing the task 发展脉络: LLM with task-specific few-shot or zero-shot prompting对于那些需要多步推理的任务是有困难的\rightarrowchain of thought:可以把复杂的推理分解成多步简单的步骤,并且生成一个推理路径\rightarrowzero-shot reason...
模型规模大小对zero-shot推理能力有影响, 推理链的使用需要在大规模预训练语言模型上才有效果,且不同的预训练语言模型的的参数规模对CoT的影响有差异,但都是越大越明显【比较符合之前的CoT能力的研究】 Does model size matter for zero-shot reasoning 错误分析:常识推理任务, 预测答案虽然不对, 但zero-shot-CoT...
In contrast, a long tradition of research in cognitive science has focused on elucidating the computational principles underlying human analogical reasoning; however, this work has generally relied on manually constructed representations. Here we present visiPAM (visual Probabilistic Analogical Mapping), a...
Research talks: Few-shot and zero-shot visual learning and reasoning October 20, 2021 Speakers: Han Hu, Zhe Gan, Kyoung Mu Lee Panel: Computer vision in the next decade: Deeper or broader October 20, 2021 Speakers: Kyoung Mu Lee,
and have revolutionized the field of natural language processing (NLP) with their excellent few-shot and zero-shot learning capabilities. However, although state-of-the-art LLMs make short work of system-1 tasks, they still struggle on system-2 tasks that require slow and multi-task...
(2022) recently proposed a simple zero-shot-CoT approach that improves LLM performance on several reasoning tasks. By simply adding "Let's think step by step" before answers, a pre-trained large-scale language model (LLM) is found to produce decent zero-shot reasoning performance. The ...
Zero-shot image recognition (ZSIR) aims at empowering models to recognize and reason in unseen domains via learning generalized knowledge from limited data in the seen domain. The gist for ZSIR is to execute element-wise representation and reasoning from the input visual space to the target semant...
This is the code for our paper "Better Zero-Shot Reasoning with Role-Play Prompting". Data is from the repo of Zero-Shot-CoTThe repository is the latest version. After the paper is officially published, we will update its arxiv version.Environmentopenai...
Example-based learning doesn't work well for complex reasoning tasks. However, adding instructions can help address this. Few-shot learning requires creating lengthy prompts. Prompts with large number of tokens can increase computation and latency. This typically means increased costs. There's also ...
Zero-shot prompting is a technique in which an AI model is given a task or question without any prior examples or specific training on that task, relying solely on its pre-existing knowledge to generate a response. Jul 21, 2024 · 10 min read ...