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Knowledge Cutoff
In machine learning, a knowledge cutoff (or data cutoff) is the date that marks the end of the data used for a model's training, especially for a large language model (LLM). Any information about events after this date is absent from the model's internal knowledge base. A model's knowledge is static after this date. It cannot access information about later events without a system for real-time data access, such as Retrieval-augmented generation, RAG. This concept started with the release of GPT-3 in 2020. Major labs like Google, OpenAI and Anthropic began publicly disclosing cutoff dates for transparency. While useful for training and tuning LLMs, knowledge cutoffs introduce new limitations like hallucinations, information gaps and temporal bias. To mitigate these issues, methods like RAG and continual learning are used to supplement static knowledge with dynamic or updated information. Overview Training large language models on static datasets is standard practice. This is necessa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Cutoffs Of Popular LLMs
Knowledge is an Declarative knowledge, awareness of facts, a Knowledge by acquaintance, familiarity with individuals and situations, or a Procedural knowledge, practical skill. Knowledge of facts, also called propositional knowledge, is often characterized as Truth, true belief that is distinct from opinion or guesswork by virtue of Justification (epistemology), justification. While there is wide agreement among philosophers that propositional knowledge is a form of true belief, many controversies focus on justification. This includes questions like how to understand justification, whether it is needed at all, and whether something else besides it is needed. These controversies intensified in the latter half of the 20th century due to a series of thought experiments called ''Gettier cases'' that provoked alternative definitions. Knowledge can be produced in many ways. The main source of empirical knowledge is perception, which involves the usage of the senses to learn about ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Reproducibility
Reproducibility, closely related to replicability and repeatability, is a major principle underpinning the scientific method. For the findings of a study to be reproducible means that results obtained by an experiment or an observational study or in a statistical analysis of a data set should be achieved again with a high degree of reliability when the study is replicated. There are different kinds of replication but typically replication studies involve different researchers using the same methodology. Only after one or several such successful replications should a result be recognized as scientific knowledge. History The first to stress the importance of reproducibility in science was the Anglo-Irish chemist Robert Boyle, in England in the 17th century. Boyle's air pump was designed to generate and study vacuum, which at the time was a very controversial concept. Indeed, distinguished philosophers such as René Descartes and Thomas Hobbes denied the very possibility of vacuum ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Language Model
A language model is a model of the human brain's ability to produce natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation,Andreas, Jacob, Andreas Vlachos, and Stephen Clark (2013)"Semantic parsing as machine translation". Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval. Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word ''n''-gram language model. History Noam Chomsky did pioneering work on lan ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Continual Learning
In computer science, incremental learning is a method of machine learning in which input data is continuously used to extend the existing model's knowledge i.e. to further train the model. It represents a dynamic technique of supervised learning and unsupervised learning that can be applied when training data becomes available gradually over time or its size is out of system memory limits. Algorithms that can facilitate incremental learning are known as incremental machine learning algorithms. Many traditional machine learning algorithms inherently support incremental learning. Other algorithms can be adapted to facilitate incremental learning. Examples of incremental algorithms include decision trees (IDE4, ID5R angaenari, decision rules, artificial neural networks ( RBF networks, Learn++, Fuzzy ARTMAP, TopoART,Marko Tscherepanow, Marco Kortkamp, and Marc KammerA Hierarchical ART Network for the Stable Incremental Learning of Topological Structures and Associations from Noisy D ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fine-tuning (machine Learning)
In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). A model may also be augmented with "adapters" that consist of far fewer parameters than the original model, and fine-tuned in a parameter-efficient way by tuning the weights of the adapters and leaving the rest of the model's weights frozen. For some architectures, such as convolutional neural networks, it is common to keep the earlier layers (those closest to the input layer) frozen, as they capture lower-level features, while later layers often discern high-level features that can be more related to the task that the model is trained on. Models that are pre-trained on large, general corpora are usually fine-tuned by reusing their p ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Adapter (machine Learning)
An adapter or adaptor is a device that converts attributes of one electrical device or system to those of an otherwise incompatible device or system. Some modify power or signal attributes, while others merely adapt the physical form of one connector to another. Travel adapters Many countries with ties to Europe use 230-volt, 50 Hz AC mains electricity, using a variety of power plugs and sockets. Difficulty arises when moving an electrical device between countries that use different sockets. A passive electric power adapter, sometimes called a ''travel plug'' or ''travel adapter'', allows using a plug from one region with a foreign socket. As other countries supply 120-volt, 60 Hz AC, using a travel adapter in a country with a different supply poses a safety hazard if the connected device does not support both input voltages. AC-to-DC adapters An AC-to-DC power supply adapts electricity from household mains voltage (either 120 or 230 volts AC) to low-voltage DC suitab ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Hallucination (artificial Intelligence)
In the field of artificial intelligence (AI), a hallucination or artificial hallucination (also called bullshitting, confabulation, or delusion) is a response generated by AI that contains false or misleading information presented as fact. This term draws a loose analogy with human psychology, where hallucination typically involves false ''percept#Process and terminology, percepts''. However, there is a key difference: AI hallucination is associated with erroneously constructed responses (confabulation), rather than perceptual experiences. For example, a chatbot powered by large language models (LLMs), like ChatGPT, may embed plausible-sounding random falsehoods within its generated content. Researchers have recognized this issue, and by 2023, analysts estimated that chatbots hallucinate as much as 27% of the time, with factual errors present in 46% of generated texts. Detecting and mitigating these hallucinations pose significant challenges for practical deployment and reliabi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
T5 (language Model)
T5 (Text-to-Text Transfer Transformer) is a series of Large language model, large language models developed by Google AI introduced in 2019. Like the Attention Is All You Need, original Transformer model, T5 models are Transformer (deep learning architecture), encoder-decoder Transformers, where the encoder processes the input text, and the decoder generates the output text. T5 models are usually pretrained on a massive Text corpus, dataset of text and code, after which they can perform the text-based tasks that are similar to their pretrained tasks. They can also be finetuned to perform other tasks. T5 models have been employed in various applications, including chatbots, machine translation systems, text summarization tools, code generation, and robotics. Training The original T5 models are pre-trained on the Colossal Clean Crawled Corpus (C4), containing text and code Web crawler, scraped from the internet. This pre-training process enables the models to learn general langu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
BERT (language Model)
Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. It learns to represent text as a sequence of vectors using self-supervised learning. It uses the Transformer (machine learning model), encoder-only transformer architecture. BERT dramatically improved the State of the art, state-of-the-art for large language model, large language models. , BERT is a ubiquitous baseline in natural language processing (NLP) experiments. BERT is trained by masked token prediction and next sentence prediction. As a result of this training process, BERT learns contextual, Latent space, latent representations of tokens in their context, similar to ELMo and GPT-2. It found applications for many natural language processing tasks, such as coreference resolution and polysemy resolution. It is an evolutionary step over ELMo, and spawned the study of "BERTology", which attempts to interpret what is learned by BERT. BERT wa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gemini (language Model)
Gemini is a family of multimodal large language models (LLMs) developed by Google DeepMind, and the successor to LaMDA and PaLM 2. Comprising Gemini Ultra, Gemini Pro, Gemini Flash, and Gemini Nano, it was announced on December 6, 2023, positioned as a competitor to OpenAI's GPT-4. It powers the chatbot of the same name. In March 2025, Gemini 2.5 Pro Experimental was rated as highly competitive. History Development Google announced Gemini, a large language model (LLM) developed by subsidiary Google DeepMind, during the Google I/O keynote on May 10, 2023. It was positioned as a more powerful successor to PaLM 2, which was also unveiled at the event, with Google CEO Sundar Pichai stating that Gemini was still in its early developmental stages. Unlike other LLMs, Gemini was said to be unique in that it was not trained on a text corpus alone and was designed to be multimodal, meaning it could process multiple types of data simultaneously, including text, images, audio, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Claude (language Model)
Claude is a family of large language models developed by Anthropic. The first model was released in March 2023. The Claude 3 family, released in March 2024, consists of three models: Haiku, optimized for speed; Sonnet, which balances capability and performance; and Opus, designed for complex reasoning tasks. These models can process both text and images, with Claude 3 Opus demonstrating enhanced capabilities in areas like mathematics, programming, and logical reasoning Logical reasoning is a mind, mental Action (philosophy), activity that aims to arrive at a Logical consequence, conclusion in a Rigour, rigorous way. It happens in the form of inferences or arguments by starting from a set of premises and reason ... compared to previous versions. Claude 4, which includes Opus and Sonnet, was released in May 2025. Training Claude models are generative pre-trained transformers. They have been pre-trained to predict the next word in large amounts of text. Then, they have ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
