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原始链接: https://news.ycombinator.com/item?id=39443965
在分析和综合各种文章和资源后，这里是一个总结版本：
在人工智能、机器学习 (ML) 和自然语言处理 (NLP) 领域，长短期记忆 (LSTM) 神经网络在对语音识别、语言翻译和股市预测等序列数据进行建模方面变得越来越流行。 These recurrent neural networks can process sequences efficiently by considering past inputs while weighing recent inputs。 However, LSTMs require large datasets for training and can suffer from issues related to vanishing gradients and exploding gradients。 Recently, transformer architecture, specifically Transformers with Self-Attention Mechanisms, have emerged as the dominant architectural paradigms in NLP tasks。 These architectures perform self-attention calculations to capture dependencies across multiple positions in input sentences, leading to superior performance compared to RNNs。 Moreover, recent breakthrough works like GPT (Generative Pretrained Transformer) have shown remarkable achievements in NLP tasks, achieving state-of-the-art performances with unprecedented capacity and scalability through massive unsupervised pretraining on large corpus of text data。 Nevertheless, building a transformer model involves several complex aspects such as training a deep language model, masking of input features during model training, decoupling supervision signals using joint teacher and student loss functions, and dealing with various technical challenges such as overfitting and generalization problems。 Overall, the field of deep learning and NLP continues to grow and evolve rapidly, requiring continuous innovation and research efforts to push boundaries further。 As demonstrated by cutting edge techniques like GPT and BERT, the possibilities for exploring novel methods and applications in these areas appear endless, and it will undoubtedly shape the landscape of AI for years to come。
To fix it, I'd throw a few gigabytes of synthetic data in the training mix before fine tuning that included the alphabets of all the relevant languages, things like.
etc.It still amazes me that Word2Vec is as useful as it is, let alone LLMs. The structure inherent in language really does convey far more meaning that we assume. We're like fish, not being aware of water, when we use language.
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