This post is divided into three parts; they are: • Understanding Word Embeddings • Using Pretrained Word Embeddings • Training Word2Vec with Gensim • Training Word2Vec with PyTorch • Embeddings in...

Build and fine-tune XGBoost models entirely online — no installations, just data, tuning, and results inside your browser.

Machine learning models have become increasingly sophisticated, but this complexity often comes at the cost of interpretability.

Quantization is a frequently used strategy applied to production machine learning models, particularly large and complex ones, to make them lightweight by reducing the numerical precision of the...

This post is divided into five parts; they are: • Naive Tokenization • Stemming and Lemmatization • Byte-Pair Encoding (BPE) • WordPiece • SentencePiece and Unigram The simplest form of...

This article is divided into three parts; they are: • Full Transformer Models: Encoder-Decoder Architecture • Encoder-Only Models • Decoder-Only Models The original transformer architecture,...

本研究提出了StepSearch框架，旨在解决大型语言模型在复杂多跳问答中的知识获取问题。通过逐步近端策略优化，该框架显著优于传统方法，验证了细粒度监督的有效性。

本研究提出了一种结合大语言模型与图像生成模型的语义进化框架，旨在解决模型对特定语义概念的敏感性问题，识别影响模型表现的敏感语义，并验证了该方法的有效性。

本研究提出了一种“首先定位然后微调”（LFTF）算法，旨在减轻大型语言模型中的性别偏见。该算法通过BMI评分对相关部分进行排序，优先微调偏见最严重的部分，实验结果表明该方法有效。

本研究探讨了预训练模型在人工智能创新中的应用，分析了HCI研究者的案例，提出了创新机会、能力分类和新兴交互设计模式，为有效应用提供指导，强调理解这些模型对推动AI创新的重要性。