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29 April

Why Gpt Is Not The Answer For Every Nlp Problem

Deep Learning has a bright future ahead of it, with discoveries being made all the time. It's feasible that, in the not-too-distant future, machine learning algorithms capable of creating human language may be used to produce a vast volume of material, and that you, as a reader, will never know whether the information was written by a person or an AI-generated bot. Natural language processing models have the incredible capability.

Artificial intelligence can create a website based on a basic user description. For an eighth-grader, AI can summarize an explanation of quantum computing. Natural language processing (NLP) and machine learning enthusiast circles have been abuzz with news of new potential capabilities with language-based AI since the announcement of the GPT-3 language model this year.

Recent advances in NLP have been years in the making, beginning in 2018 with the release of two massive deep learning models for language understanding: GPT (Generative Pre-Training) by Open AI and BERT (Bidirectional Encoder Representations from Transformers), including BERT-Base and BERT-Large by Google. Unlike earlier NLP models, BERT is an open-source, highly bidirectional, and unsupervised language representation that is only pre-trained using a plain text corpus. Since then, several deep learning huge language models have emerged, including GPT RoBERT, ESIM+GloVe.

Innovative model architectures such as GPT-3 and BERT have emerged as a result of enormous advances in natural language processing. Pre-trained models have democratized machine learning, allowing even non-technical users to get their hands dirty designing ML applications without having to train a model from start. Most modern NLP models are often trained on a large variety of data, in the billions, to solve diverse issues including creating accurate predictions, transfer learning, and feature extraction.

Unless you want to spend a lot of time and effort constructing a model from scratch, these pre-trained models contradict the point of training one from scratch. Language models like BERT, on the other hand, can be readily be fine-tuned and used for the appropriate tasks. However, the introduction of more powerful versions such as GPT-3 has made the job much simpler for users, as they can now just define the task and develop their desired application with a single click. Such advances emphasize their cutting-edge capabilities.

The GPT-3 model, which was launched last year, caused quite a stir in the artificial intelligence community. It took a lot of effort to create a language model of this complexity. Many more versions have been launched in the last year, each larger and more powerful than the one before it. The core premise is deep monolithic structures that are utilized to comprehend how languages are employed in content gathered from large web crawls. Model training entails storing the parameters required to comprehend linguistic tasks and constructing highly abstract knowledge representations of facts, things, and events that the model may need to complete these tasks.

NLP has simplified activities such as question answering, report summary and translation, and sentiment analysis. However, the black-box nature of NLP is a significant impediment to fulfilling the essential objectives. This is a typical difficulty with the regularly used generative-based NLP. Retrieval-based NLP models are a viable option. Models directly seek information in a text corpus to demonstrate expertise in the latter. These models make use of language models' representational power while also addressing other issues. REALM, RAG, Baleen, and ColBERT-QA are some excellent examples of these models.

The Problem with NLP's Black Box

Despite their success, massive language models have the following difficulties:

  • The models have already surpassed the trillion parameter threshold. This not only offers a big environmental concern, but many smaller businesses are unable to teach or implement these models due to the high expense of training.
  • These big models are not only challenging to train and deploy, but they are also exceedingly static. In reality, every such model adaptation requires costly retraining and fine-tuning on a fresh corpus.
  • By synthesizing what they remember from training instances, the models encode information into model weight. This makes tracing the sources that a model could employ to create a certain prediction difficult, resulting in opacity in these models, which are consequently prone to making fluent but false assertions.
  • Because NLP models, in particular, are opaque in their knowledge representation and supporting assertions with provenance, training, and deployment of models like T5 and GPT-3 is particularly challenging.

Rescue Retrieval Model

Retrieval-based NLP models, as the name implies, extract information from a plugged-in text corpus to solve a problem. It enables NLP models to make use of the representational power of language models without the need for a huge architecture, resulting in clear provenance for claims and simple updation and adaption.

  • Retrieval-based NLP models treat problems as open book examinations. Knowledge is explicitly encoded in the form of a text corpus in these models. The model then learns to look at such passages and utilize the information it finds to create intelligent answers. This kind of model separates a model's ability to interpret a text from how it stores knowledge. These models have three major benefits:
  • Transparency is a benefit of retrieval-based models. When the model generates an answer, for example, the user may read the sources it has found and assess their relevance and believability.
  • Models that are based on retrieval are often smaller than those that are based on generation. The parameters no longer need to hold an ever-growing collection of information, unlike black-box language models. Instead, these variables may be utilized to analyze language and solve real-world problems.
  • Learning generic skills for discovering and linking information from various sources is emphasized heavily in retrieval-based models. This allows for efficient retrieval knowledge storage and growth simply by changing the text corpus without affecting the model's ability to identify and use information.


NLP models based on retrieval are substantially smaller. Unlike generative models, this model can trace the source of information utilized to answer any specific query, resulting in more dependable replies. What do you think of this? Let us know in the comments below.