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Decoding GPTs & LLMs: Training, Memory & Advanced Architectures Explained
Unlock the secrets of GPTs and Large Language Models (LLMs) in our comprehensive guide!
🤖🚀 Dive deep into the world of AI as we explore ‘GPTs and LLMs: Pre-Training, Fine-Tuning, Memory, and More!’ Understand the intricacies of how these AI models learn through pre-training and fine-tuning, their operational scope within a context window, and the intriguing aspect of their lack of long-term memory.
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🧠 In this article, we demystify:
- Pre-Training & Fine-Tuning Methods: Learn how GPTs and LLMs are trained on vast datasets to grasp language patterns and how fine-tuning tailors them for specific tasks.
- Context Window in AI: Explore the concept of the context window, which acts as a short-term memory for LLMs, influencing how they process and respond to information.
- Lack of Long-Term Memory: Understand the limitations of GPTs and LLMs in retaining information over extended periods and how this impacts their functionality.
- Database-Querying Architectures: Discover how some advanced AI models interact with external databases to enhance information retrieval and processing.
- PDF Apps & Real-Time Fine-Tuning
Drop your questions and thoughts in the comments below and let’s discuss the future of AI! #GPTsExplained #LLMs #AITraining #MachineLearning #AIContextWindow #AILongTermMemory #AIDatabases #PDFAppsAI”
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Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover GPTs and LLMs, their pre-training and fine-tuning methods, their context window and lack of long-term memory, architectures that query databases, PDF app’s use of near-realtime fine-tuning, and the book “AI Unraveled” which answers FAQs about AI.
GPTs, or Generative Pre-trained Transformers, work by being trained on a large amount of text data and then using that training to generate output based on input. So, when you give a GPT a specific input, it will produce the best matching output based on its training.
The way GPTs do this is by processing the input token by token, without actually understanding the entire output. It simply recognizes that certain tokens are often followed by certain other tokens based on its training. This knowledge is gained during the training process, where the language model (LLM) is fed a large number of embeddings, which can be thought of as its “knowledge.”
After the training stage, a LLM can be fine-tuned to improve its accuracy for a particular domain. This is done by providing it with domain-specific labeled data and modifying its parameters to match the desired accuracy on that data.
Now, let’s talk about “memory” in these models. LLMs do not have a long-term memory in the same way humans do. If you were to tell an LLM that you have a 6-year-old son, it wouldn’t retain that information like a human would. However, these models can still answer related follow-up questions in a conversation.
For example, if you ask the model to tell you a story and then ask it to make the story shorter, it can generate a shorter version of the story. This is possible because the previous Q&A is passed along in the context window of the conversation. The context window keeps track of the conversation history, allowing the model to maintain some context and generate appropriate responses.
As the conversation continues, the context window and the number of tokens required will keep growing. This can become a challenge, as there are limitations on the maximum length of input that the model can handle. If a conversation becomes too long, the model may start truncating or forgetting earlier parts of the conversation.
Regarding architectures and databases, there are some models that may query a database before providing an answer. For example, a model could be designed to run a database query like “select * from user_history” to retrieve relevant information before generating a response. This is one way vector databases can be used in the context of these models.
There are also architectures where the model undergoes near-realtime fine-tuning when a chat begins. This means that the model is fine-tuned on specific data related to the chat session itself, which helps it generate more context-aware responses. This is similar to how “speak with your PDF” apps work, where the model is trained on specific PDF content to provide relevant responses.
In summary, GPTs and LLMs work by being pre-trained on a large amount of text data and then using that training to generate output based on input. They do this token by token, without truly understanding the complete output. LLMs can be fine-tuned to improve accuracy for specific domains by providing them with domain-specific labeled data. While LLMs don’t have long-term memory like humans, they can still generate responses in a conversation by using the context window to keep track of the conversation history. Some architectures may query databases before generating responses, and others may undergo near-realtime fine-tuning to provide more context-aware answers.
GPTs and Large Language Models (LLMs) are fascinating tools that have revolutionized natural language processing. It seems like you have a good grasp of how these models function, but I’ll take a moment to provide some clarification and expand on a few points for a more comprehensive understanding.
When it comes to GPTs and LLMs, pre-training and token prediction play a crucial role. During the pre-training phase, these models are exposed to massive amounts of text data. This helps them learn to predict the next token (word or part of a word) in a sequence based on the statistical likelihood of that token following the given context. It’s important to note that while the model can recognize patterns in language use, it doesn’t truly “understand” the text in a human sense.
During the training process, the model becomes familiar with these large datasets and learns embeddings. Embeddings are representations of tokens in a high-dimensional space, and they capture relationships and context around each token. These embeddings allow the model to generate coherent and contextually appropriate responses.
However, pre-training is just the beginning. Fine-tuning is a subsequent step that tailors the model to specific domains or tasks. It involves training the model further on a smaller, domain-specific dataset. This process adjusts the model’s parameters, enabling it to generate responses that are more relevant to the specialized domain.
Now, let’s discuss memory and the context window. LLMs like GPT do not possess long-term memory in the same way humans do. Instead, they operate within what we call a context window. The context window determines the amount of text (measured in tokens) that the model can consider when making predictions. It provides the model with a form of “short-term memory.”
For follow-up questions, the model relies on this context window. So, when you ask a follow-up question, the model factors in the previous interaction (the original story and the request to shorten it) within its context window. It then generates a response based on that context. However, it’s crucial to note that the context window has a fixed size, which means it can only hold a certain number of tokens. If the conversation exceeds this limit, the oldest tokens are discarded, and the model loses track of that part of the dialogue.
It’s also worth mentioning that there is no real-time fine-tuning happening with each interaction. The model responds based on its pre-training and any fine-tuning that occurred prior to its deployment. This means that the model does not learn or adapt during real-time conversation but rather relies on the knowledge it has gained from pre-training and fine-tuning.
While standard LLMs like GPT do not typically utilize external memory systems or databases, some advanced models and applications may incorporate these features. External memory systems can store information beyond the limits of the context window. However, it’s important to understand that these features are not inherent to the base LLM architecture like GPT. In some systems, vector databases might be used to enhance the retrieval of relevant information based on queries, but this is separate from the internal processing of the LLM.
In relation to the “speak with your PDF” applications you mentioned, they generally employ a combination of text extraction and LLMs. The purpose is to interpret and respond to queries about the content of a PDF. These applications do not engage in real-time fine-tuning, but instead use the existing capabilities of the model to interpret and interact with the newly extracted text.
To summarize, LLMs like GPT operate within a context window and utilize patterns learned during pre-training and fine-tuning to generate responses. They do not possess long-term memory or real-time learning capabilities during interactions, but they can handle follow-up questions within the confines of their context window. It’s important to remember that while some advanced implementations might leverage external memory or databases, these features are not inherently built into the foundational architecture of the standard LLM.
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So, if you’re ready to embark on this incredible journey of unraveling the mysteries of AI, go ahead and grab your copy of “AI Unraveled” today. Trust me, you won’t regret it!
On today’s episode, we explored the power of GPTs and LLMs, discussing their ability to generate outputs, be fine-tuned for specific domains, and utilize a context window for related follow-up questions. We also learned about their limitations in terms of long-term memory and real-time updates. Lastly, we shared information about the book “AI Unraveled,” which provides valuable insights into the world of artificial intelligence. Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!
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- Irregular bone marrow cells may increase heart disease riskby /u/Passervore on April 26, 2024 at 2:55 pm
submitted by /u/Passervore [link] [comments]
- ‘Real hope’ for cancer cure as personal mRNA vaccine for melanoma trialledby /u/Well_Socialized on April 26, 2024 at 2:53 pm
submitted by /u/Well_Socialized [link] [comments]
- 20% of grocery store milk has traces of bird flu, suggesting wider outbreak | The milk is still considered safe, but disease experts are alarmed by the prevalence.by /u/chrisdh79 on April 26, 2024 at 2:48 pm
submitted by /u/chrisdh79 [link] [comments]
- "Teachers and family dismissed my cry for help—it was almost too late"by /u/newsweek on April 26, 2024 at 12:41 pm
submitted by /u/newsweek [link] [comments]
- A new kind of gene-edited pig kidney was just transplanted into a personby /u/Sariel007 on April 26, 2024 at 12:32 pm
submitted by /u/Sariel007 [link] [comments]
Today I Learned (TIL) You learn something new every day; what did you learn today? Submit interesting and specific facts about something that you just found out here.
- TIL A group of horses were trained to communicate whether they wanted a jacket. All horses in the group successfully communicated that they did want a jacket when it was cold and did not want a jacket when it was hot.by /u/PunnyBanana on April 26, 2024 at 2:20 pm
submitted by /u/PunnyBanana [link] [comments]
- TIL when the artists arrived to record "We Are the World," Stevie Wonder told them that if the song wasn't finished in one take, he and Ray Charles would drive them home.by /u/RequirementSouth4482 on April 26, 2024 at 1:53 pm
submitted by /u/RequirementSouth4482 [link] [comments]
- TIL E.T was a 12-year-old disabled boy in a suitby /u/DiaBoloix on April 26, 2024 at 12:43 pm
submitted by /u/DiaBoloix [link] [comments]
- TIL that a politician gave a food review of kebab while speaking in parliament. Australian Senator Sam Dastyari gave a "10 out of 10" rating to the kebab snack pack sold at King Kebab House, and advised others to also enjoy "a great Australian tradition of meat in a box".by /u/TMWNN on April 26, 2024 at 11:47 am
submitted by /u/TMWNN [link] [comments]
- TIL the infamous "Jump the Shark" episode of Happy Days (Season 5, Episode 3) was created as a way to showcase Henry Winkler's real-life water skiing skills. The episode drew over 30 million viewers.by /u/ColeBelthazorTurner on April 26, 2024 at 11:36 am
submitted by /u/ColeBelthazorTurner [link] [comments]
Reddit Science This community is a place to share and discuss new scientific research. Read about the latest advances in astronomy, biology, medicine, physics, social science, and more. Find and submit new publications and popular science coverage of current research.
- Researchers have developed a nanomaterial that could be used to treat neurodegenerative diseases, such as Alzheimer's or Parkinson's. The new "protein-like polymer" has been shown to alter the interaction between two key brain proteins in cell cultures, releasing an important antioxidant on demand.by /u/alexbeadlesci on April 26, 2024 at 3:34 pm
submitted by /u/alexbeadlesci [link] [comments]
- EV drivers need to transition from the “monitor fuel gauge model” (driver refuels when fuel is running out) which represents how most people refuel a petrol or diesel car, to the “event-triggered model” (driver plugs in as soon as arriving home or work) which is optimum for EV use, finds new study.by /u/mvea on April 26, 2024 at 1:49 pm
submitted by /u/mvea [link] [comments]
- Recent research challenges the common belief that childhood trauma affects the experience of ayahuasca, a plant-based psychedelic. Surprisingly, the study finds no connection between prior childhood trauma and the intensity of challenges faced when under the influence of ayahuasca.by /u/mvea on April 26, 2024 at 1:29 pm
submitted by /u/mvea [link] [comments]
- ‘Uncharted territory’: Dual fusion breakthrough in generating denser and safer plasmaby /u/Cleancoolenergy on April 26, 2024 at 1:12 pm
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- Researchers have found a fast, and inexpensive way to create geometric patterns in carbon nanotube films. The resulting films turned out to have superior properties for manufacturing components for 6G communication devices and flexible and transparent electronics — such as wearable health trackers.by /u/Skoltech_ on April 26, 2024 at 11:56 am
submitted by /u/Skoltech_ [link] [comments]
Reddit Sports Sports News and Highlights from the NFL, NBA, NHL, MLB, MLS, and leagues around the world.
- Falcons GM explains shocking selection of Michael Penix Jr. that left Kirk Cousins 'disappointed'by /u/Oldtimer_2 on April 26, 2024 at 1:45 pm
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- Joel Embiid scores 50 points to lead 76ers past Knicks 125-114 to cut deficit to 2-1by /u/Oldtimer_2 on April 26, 2024 at 1:13 pm
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- 49ers excited to have Aiyuk, Deebo, Pearsall togetherby /u/Oldtimer_2 on April 26, 2024 at 1:12 pm
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- Nuggets breeze past Lakers, take 3-0 series leadby /u/Oldtimer_2 on April 26, 2024 at 1:10 pm
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- Brazil legend Marta to retire from international footballby /u/PrincessBananas85 on April 26, 2024 at 1:08 pm
submitted by /u/PrincessBananas85 [link] [comments]