电子商务网站属性价值提取（AVE）的主要挑战是如何处理多种产品的大量属性。尽管该挑战是通过一个问题回答（QA）方法来解决的，该方法在给定查询（属性）的产品数据中找到值，但对于稀有和模棱两可的查询，它不能有效地工作。因此，我们根据基于质量质量质量检查的AVE的查询（属性）的可能答案（属性）提出了简单的知识驱动查询扩展。我们从培训数据中检索查询（属性）的值以扩展查询。我们用两个技巧来训练一个模型，即知识辍学和知识令牌混合，这模仿了测试中价值知识的不完善。我们清洁版的Aliexpress数据集的实验结果表明，我们的方法改善了AVE的性能（+6.08宏F1），尤其是对于稀有和模棱两可的属性（分别为+7.82和+6.86宏F1）。
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Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.
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没有一致响应的对话系统并不令人着迷。在这项研究中，我们建立了一个对话系统，可以根据给定的角色设置（角色）响应以带来一致性。考虑到语言模型迅速增加的趋势，我们提出了一种使用迅速调整的方法，该方法在预训练的大规模语言模型上使用了低学习成本。英语和日语中自动和手动评估的结果表明，可以使用比微调更少的计算资源来构建具有更自然和个性化响应的对话系统。
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In this paper, we present a cross-modal recipe retrieval framework, Transformer-based Network for Large Batch Training (TNLBT), which is inspired by ACME~(Adversarial Cross-Modal Embedding) and H-T~(Hierarchical Transformer). TNLBT aims to accomplish retrieval tasks while generating images from recipe embeddings. We apply the Hierarchical Transformer-based recipe text encoder, the Vision Transformer~(ViT)-based recipe image encoder, and an adversarial network architecture to enable better cross-modal embedding learning for recipe texts and images. In addition, we use self-supervised learning to exploit the rich information in the recipe texts having no corresponding images. Since contrastive learning could benefit from a larger batch size according to the recent literature on self-supervised learning, we adopt a large batch size during training and have validated its effectiveness. In the experiments, the proposed framework significantly outperformed the current state-of-the-art frameworks in both cross-modal recipe retrieval and image generation tasks on the benchmark Recipe1M. This is the first work which confirmed the effectiveness of large batch training on cross-modal recipe embeddings.
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人对象交互（HOI）检测作为对象检测任务的下游需要本地化人和对象，并从图像中提取人类和对象之间的语义关系。最近，由于其高效率，一步方法已成为这项任务的新趋势。然而，这些方法侧重于检测可能的交互点或过滤人对象对，忽略空间尺度处的不同物体的位置和大小的可变性。为了解决这个问题，我们提出了一种基于变压器的方法，Qahoi（用于人对象交互检测的查询锚点），它利用了多尺度架构来提取来自不同空间尺度的特征，并使用基于查询的锚来预测全部Hoi实例的元素。我们进一步调查了强大的骨干，显着提高了QAHOI的准确性，QAHOI与基于变压器的骨干优于最近的最近最先进的方法，通过HICO-DEC基准。源代码以$ \ href {https://github.com/cjw2021/qhoii} {\ text {this https url}} $。
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