This article aims to introduce the Transformer model. Originally developed for machine translation, this model has since been widely applied in various fields such as computer recognition and multimodal tasks. The Transformer model introduces self-attention mechanisms and positional encoding, and its architecture mainly consists of an input part, an output part, and encoders and decoders. … Read more
Introduction In recent years, deep learning has made tremendous progress in the field of Natural Language Processing (NLP), and the Transformer model is undoubtedly one of the best. Since the Google research team proposed the Transformer model in their paper “Attention is All You Need” in 2017, it has become the cornerstone for many NLP … Read more
Big Data Digest authorized reprint from Xi Xiaoyao Technology Author | Xie Nian Nian In interpersonal communication, especially when using a language as profound as Chinese, people often do not answer questions directly but instead adopt implicit, obscure, or indirect expressions. Humans can make accurate judgments about some implied meanings based on past experiences or … Read more
Paper Title “AUTOPROMPT: Eliciting Knowledge from Language Models with Automatically Generated Prompts”, authored by Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. The paper was published at the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). The paper aims to enhance the performance of language models on downstream … Read more
I obtained an invitation code for Tongyi Qianwen from Alibaba Damo Academy during its public testing. Below are the responses from Tongyi Qianwen, Wenxin Yiyan, ChatGPT (GPT 3.5), and ChatGPT (GPT 4) to the same question. This comparison focuses on a single question and does not involve context. It allows for a direct comparison of … Read more
Machine Heart Reports Editor: Rome Large language models have astonishing capabilities, but they often incur huge costs during deployment due to their size. Researchers from the University of Washington, in collaboration with the Google Cloud AI Research Institute and Google Research, have proposed a solution to this problem by introducing the Distilling Step-by-Step paradigm to … Read more
Report by Machine Heart Machine Heart Editorial Team This article conducts a controlled variable experiment on the reasoning step length of the thinking chain, finding that the reasoning step length is linearly correlated with the accuracy of the answers, and this influence mechanism even transcends the differences generated by the problem itself. Today, the emergence … Read more
In today’s rapidly developing technological era, AI technology has become an important force driving industry transformation. Many enterprises and developers are beginning to seek AI platforms that can help them improve efficiency and foster innovation. Aggregated AI platforms are one of the powerful tools that meet this demand. So, what are the aggregated AI platforms? … Read more
Source: AI Study Society I was interviewed recently and, since I still don’t fully understand how word embeddings work, I’ve been looking for a lot of related materials to grasp this concept better. My understanding is still limited, so I won’t overestimate myself by writing my own article (even if I did, it would just … Read more
Deep learning has achieved great success in natural language processing (NLP) tasks, among which distributed representation of words is a crucial technology. To deeply understand distributed representation, one must delve into word2vec. Today, let’s explore how the Huffman Tree is generated in the word2vec code. This is a very important data structure in word2vec, used … Read more