Was sind die gängigsten NLP-Modelle und -Architekturen? (2024)

Since rule-based models can incorporate some logic that our domain experts are aware of, they can occasionally be very powerful, which is why I like them. In domain-centric AI projects, this becomes more and more crucial. Occasionally, running embeddings and models would be preferable to a straightforward rule.

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Rule-based NLP models are built upon predefined linguistic rules, which are manually defined by domain experts to empower the model with the ability to comprehend and process natural language. These models function based on explicit patterns and instructions making it interpretable. Rule-based NLP models excel in scenarios where language patterns are well-defined and consistent, such as in simple chatbots or straightforward text preprocessing tasks. However, their flexibility is constrained as they may struggle with handling nuances, ambiguity and language variations that extend beyond the scope of the predefined rules. Continuously maintaining and updating these rules can be a challenge as specific domains evolve over time

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While rule-based NLP models are not necessarily the most sophisticated ones, they are powerful because of their simplicity.For instance, it is easy to communicate with subject matter experts which rule-based models are implemented, such as regular expressions or keyword lookups.In combination with other NLP approaches, rule-based models can add value to powerful NLP pipelines.

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While statistical models excel in part-of-speech tagging and named entity recognition, it's crucial to note that their performance hinges on data quality and quantity. They operate by predicting the next word based on context but may struggle with out-of-vocabulary (OOV) words due to training data limitations. Therefore, data quality is paramount, and the models may have limitations in capturing semantic nuances and context.

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Statistical models are probabilistic models, frequently used for predicting the next word in the sequence, similar way the Gmail, MS Teams suggest or your chat software suggests your next words in the given sequential words based on the preceding words.However, large data set, understanding the context and inability to handle out-of-vocabulary (OOV) words (based on training limitation) limits the use cases.

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As of late we've seen more and more transformers being used across the field of NLP. These models leverage position encoding and self-attention to achieve exceptional results. This architecture has paved the way for models such as the GPT series. There's also the Bert family of models. Lately, we're even witnessing extremely capable open source models such as LLAMA.

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The neural models offer unparalleled flexibility and adaptability, enabling them to tackle a wide range of NLP applications but they come with demanding computational resource requirements, which can pose challenges for many organizations. Also, neural models may encounter hurdles like overfitting and underfitting. Addressing such challenges is paramount as we continue to leverage the potential of neural models in the evolving landscape of NLP. Furthermore, the interpretability of the models remains an active area of research, with strides being made to make their decision-making processes more transparent and accountable. As it matures, striking the right balance between the power and complexity of neural models will be a pivotal focus.

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Encoder-decoder architectures have played a crucial role in the development of models like GPT (Generative Pre-trained Transformer). While GPT itself primarily employs a decoder architecture, the use of encoder-decoder architectures in models has contributed a lot to the advancement of natural language understanding and generation.

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Before jumping on the latest and greatest, it's always a good idea to consider simpler models/methods for baseline performance especially when data is sparse, computational resources are limited and budgets are not sky high. BERT-based models are best utilized for tasks requiring deep language understanding, like sentiment analysis, text classification, and information extraction while GPT-based models shine in generative tasks, including content creation, language translation, and chatbot interactions, leveraging their ability to produce coherent and contextually relevant text.

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Transformer models excel in NLP tasks due to their ability to capture long-term dependencies and context in sequences. Some of the key features that I believe drive their advantages versus traditional encoder-decoder architectures include:1. Self-Attention: Enables the model to weigh the importance of different parts of the input when processing each token. This leads to better context understanding. 2. Positional Encoding: Allows the model to consider the position of each word, overcoming the architecture's inherent lack of sequence awareness. 3. Parallel Processing: Unlike RNNs that process tokens sequentially, transformers process all tokens simultaneously, speeding up training.

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In my experience, pre-trained models have unblocked enterprise innovation. By leveraging pre-trained models, enterprise-internal or -external upstarts can get to a value proposition within a short timeline. Pre-trained models have raised the needle in the art-of-the possible in siloed industries too, where large amounts of data was hard to come by but sufficient data was available to fine-tune a pre-trained model.

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Pre-trained models represent the epitome of transfer learning in NLP. By leveraging knowledge from vast general corpora, they offer a jumpstart in domain-specific tasks, eliminating the need to start from scratch. While models like BERT and GPT-3 have taken center stage, it's crucial to remember the importance of fine-tuning. Harnessing the power of these models while tailoring them to specific tasks ensures we reap the best of both worlds.

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In my opinion, development of NLP (or any AI) solutions requires a delicate balance among Data, Process, Technology, and People. It is essential to consider the affordances and desired outcomes of the Data, construct a solution that integrates into existing Process by leveraging appropriate Technology, while maintaining an understanding of the skills and needs of the People.

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Too many dive into the deep end with enthusiam + lead weights!Take time to understand your use and whether you should be a taker, shaper or maker of models. Not every use case requires you to train your own! Start simple by consuming via API, fine tune and then only start training your own when you feel constrained.

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