EfficientRAG 迭代生成新查询,而无需在每次迭代时调用 LLM,并过滤掉不相关的信息。 论文地址:arXiv reCAPTCHA 2、 核心框架 论文核心框架 EfficientRAG 由两个轻量级组件组成:Labeler和Tagger以及Filter。这些组件共享相同的模型结构,Labeler和Tagger从同一模型内的不同头部产生输出,而Filter的输出来自另一个模型。Labeler...
论文解读:Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings 知识库问答(KBQA/KGQA)是指给定一个自然语言问句和对应的知识库,试图从知识库中返回对应正确的答案。现如今一些方法是通过对问句中的候选实体在知识库中对齐,并获得一定跳数范围内的子图,通过排序算法或Top...
回答一个问题往往需要把问题切割成多个子问题,所以本质上回答这类问题就是一个multi-hop question-answering task. 上面图中只是首先chunking文档然后embedding数据库中的文档之后plain vector similarity search用于multi-hop questions存在如下问题: (1)repeated information in top N documents. (2)missing reference info...
We evaluate our model on an open-domain complex Chinese question answering task CCKS2019 and achieve F1-score of 62.55% on the test dataset. In addition, in order to test the few-shot learning capability of our model, we randomly select 10% of the primary data to train our ...
ACL 2020: Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings - malllabiisc/EmbedKGQA
2 code implementations in PyTorch. Existing question answering datasets focus on dealing with homogeneous information, based either only on text or KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms, using hom
目前解决复杂KBQA(Knowledge Base Question Answering)的难点主要在于:问题带限制以及问题里包含有多个关系。这篇文章提出了一个query graph生成方法解决这个问题。 1. 背景介绍 在了解本文具体做什么之前,需要再明确下什么是带限制的问题和multi-hop of relations。带限制的问题,其实就是带限定词,例如Who was the firs...
Key强调,模型通过点积运算为每个记忆单元分配一个归一化的相关权重,作为问题(question)与记忆单元中每个Key表示之间的相关概率; Value读取,模型通过取所有value和相关权重的加权和来读取所有寻址存储器的值,并使用输出表示中间推理结果,然后用于更新问题(question)表示。
We introduce VIMQA, a new Vietnamese dataset with over 10,000 Wikipedia-based multi-hop question-answer pairs. The dataset is human-generated and has four main features: (1) The questions require advanced reasoning over multiple paragraphs. (2) Sentence-level supporting facts are provided, ...
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