If you are interested in launching a ChatGPT-based chatbot for your business, please send us a note using the form below. We can deploy your bot in less than a week, and host it for you on the Witlingo platform. All we would need is access to the content that you wish the chatbot to answer question about.
Adversarial Training: A technique for improving the robustness of language models by exposing them to examples that are specifically designed to challenge the model and force it to learn more robust representations.
AI (Artificial Intelligence): A field of computer science focused on building intelligent machines that can perform tasks that typically require human intelligence, such as speech recognition, problem solving, and language translation.
Attention Mechanism: a technique used in Transformer-based models to selectively focus on specific tokens in the input sequence when generating output.
Autoregression: A property of language models, where the prediction of the next token depends on the previous tokens generated by the model itself, rather than just on the input.
Beam Search: A search algorithm used in NLP to find the most likely sequence of tokens generated by a language model, by maintaining a beam of the k most likely sequences at each time step.
Contextual Embeddings: Word embeddings that are generated in a way that considers the context in which the words appear, such as the surrounding words or the sentence structure.
Coreference Resolution: The process of identifying when different expressions in a text refer to the same entity and replacing them with a single, consistent representation.
Dependency Parsing: A task in NLP that involves analyzing the grammatical structure of a sentence to identify the relationships between its constituents, such as subject, object, or modifier.
Deployment: The process of making a trained language model available for use, either by integrating it into a larger system or by providing an API for others to access it.
Entities: Refers to real-world objects, such as people, organizations, locations, or products, that can be identified and extracted from text.
Evaluation Metrics: Measures used to assess the performance of a language model, such as perplexity, accuracy, F1 score, or BLEU score.
Fine-tuning: The process of adapting a pre-trained language model for a specific task by training it on a smaller, task-specific dataset.
Fine-Grained Control: The ability of a language model to generate text with specific attributes, such as style, tone, or content, by adjusting its internal parameters.
Generation: the process of using a language model to generate new text, either by sampling from the model's predicted distribution over tokens, or by using the model as a guide for human text generation
Generative Adversarial Networks (GANs): A type of neural network architecture used in NLP, consisting of two models: a generator that generates text, and a discriminator that evaluates the quality of the generated text and provides feedback to the generator.
GPT-3 (Generative Pre-trained Transformer 3) : An AI language model developed by OpenAI, trained on a large corpus of text from the internet to generate human-like text.
Greedy Search: A search algorithm used in NLP to find the most likely sequence of tokens generated by a language model, by selecting the most likely token at each time step.
Inference: The process of using a trained language model to make predictions on new, unseen data.
Knowledge Base: A structured repository of information, such as a database or an ontology, that can be used to provide context and background information for language models.
Language Model: An AI model that has been trained to generate text based on patterns it has learned from a large corpus of text data.
Masked Language Modeling: A pre-training task where some tokens in the input sequence are masked, and the model is trained to predict these tokens, given the context of the surrounding tokens.
Multitask Learning: The process of training a model on multiple tasks simultaneously, in order to improve overall performance and learn shared representations across tasks.
Named Entity Recognition (NER): A task in NLP that involves identifying and classifying entities mentioned in a piece of text into predefined categories, such as person, location, or organization.
NLP (Natural Language Processing): A subfield of AI focused on the interaction between computers and humans using natural language.
Overfitting: A common issue in machine learning where a model becomes too specialized to the training data and performs poorly on unseen data. To avoid overfitting, models are usually regularized, for example by using dropout or early stopping.
Part-of-Speech Tagging (POS): A task in NLP that involves labeling each word in a sentence with its grammatical role, such as noun, verb, or adjective.
Pre-training: The process of training a language model on a large corpus of text data before fine-tuning it for specific tasks, such as answering questions or generating text.
Prompt: Refers to the input text provided by a user to initiate a conversation or to ask a question. It is the starting point for ChatGPT to generate a response based on its training. The prompt serves as a context or cue for ChatGPT to generate a relevant and coherent reply.
QA (Question Answering): A task in NLP where a model is given a question and must generate a relevant and coherent answer, based on its understanding of the language and the knowledge it has learned.
Regularization: A technique for reducing overfitting by adding a penalty term to the loss function that discourages the model from becoming too complex.
Self-Attention: A mechanism used in Transformer-based models to compute relationships between tokens within a single sequence, enabling the model to attend to different parts of the input when generating its output.
Semantic Similarity: A measure of the similarity between the meanings of two pieces of text, usually based on their representations in a vector space.
Sentiment Analysis: A task in NLP that involves determining the sentiment expressed in a piece of text, such as positive, negative, or neutral.
Sequence Generation: The process of using a language model to generate a sequence of tokens, such as a sentence or a paragraph, based on the patterns it has learned from training data.
Sequence-to-Sequence (Seq2Seq) Models: A type of neural network architecture used in NLP, designed to map an input sequence to an output sequence, such as in machine translation or text summarization.
Token: A basic unit of meaning in NLP, representing a single word, punctuation mark, or other piece of text.
Topic Modeling: A technique for discovering the underlying topics in a collection of text documents, based on the patterns of word co-occurrence.
Transfer Learning: The process of using knowledge learned from one task to improve performance on a related task, by leveraging pre-trained models.
Transformers: A type of neural network architecture used in NLP, designed to process sequential data, such as text.
Word Embeddings: Dense, continuous representations of words in a vector space, usually obtained by training a language model on a large corpus of text. Word embeddings capture the semantic and syntactic similarities between words.
Zero-shot Learning: The ability of a language model to perform a task it has not seen before, based on its understanding of the language and the knowledge it has learned from pre-training.