https://python.langchain.com/docs/use_cases/question_answering/ 假设我们想基于自己部署的Llama2模型,构建一个用于回答针对特定文档内容提问的聊天机器人。文档的内容可能属于特定领域或者特定组织内的文档,因此不属于任何Llama2进行预训练和微调的数据集。一个直觉的做法是in-context-learning:将文档作为Prompt提供给...
The dataset used in this study is a subset ofnq_open, an open source question answering dataset...
3.2 Closed-book Question Answering We compare LLaMA to existing large language models on two closed-book question answering benchmarks: Natural Questions (Kwiatkowski et al., 2019) and TriviaQA (Joshi et al., 2017). For both benchmarks, we report exact match perfor-mance in a closed book s...
databases, and APIs without the need to fine-tune it. When usinggenerative AIfor question answering, RAG enables LLMs to answer questions with the most relevant, up-to-date information and optionally cite their data sources
Text summarization and in-context question answering System prompts can also be straightforward and enforce context to answer questions. In this example, we ask Llama 2 Chat to assume the persona of a chatbot and have it answer questions only from the iconic 1997 Amazon...
与Llama 2 相同的设置,1-shot,看f1值 "QuAC"(Question Answering in Context)是一个问答数据集,专注于对话式问答的场景。 该数据集由教师和学生之间的对话组成,其中学生询问问题,而教师提供基于背景材料的答案。 QuAC 的目的是促进对话式问答系统的研究和开发,使其能够更好地理解和生成自然对话中的问答序列。BoolQ...
This is a multimodal model design for the Vision Question Answering (VQA) task. It integrates the Llama2 13B, OWL-ViT, and YOLOv8 models. - ycchen218/VisionQA-Llama2-OWLViT
I downloaded the model here and ran it on the fork, but the model is writing a lot of random text instead of answering the question "What is the process number?" https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf/tree/main PS C:\my...
Grade-school math question-answering (GSM8k) We specifically show how on some tasks (e.g. SQL Gen or Functional Representation) we can fine-tune small Llama-2 models to become even better than GPT-4. At the same time, there are tasks like math reasoning and understanding tha...
3.2 Closed-book Question Answering 我们在两个闭书问答基准上将LLaMA与现有的大语言模型进行了比较:自然问题(Kwiatkowski et al., 2019)和TriviaQA (Joshi et al., 2017)。对于这两个基准,我们在闭书环境中报告了完全匹配的性能,即模型无法访问包含回答问题的证据的文档。在表4中,我们报告了NaturalQuestions的性能...