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.
🧠 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.
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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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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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Top 1000 Canada Quiz and trivia: CANADA CITIZENSHIP TEST- HISTORY - GEOGRAPHY - GOVERNMENT- CULTURE - PEOPLE - LANGUAGES - TRAVEL - WILDLIFE - HOCKEY - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
![zCanadian Quiz and Trivia, Canadian History, Citizenship Test, Geography, Wildlife, Secenries, Banff, Tourism](data:image/svg+xml,%3Csvg%20xmlns=%22http://www.w3.org/2000/svg%22%20viewBox=%220%200%20400%20266.66666666667%22%3E%3C/svg%3E)
Top 1000 Africa Quiz and trivia: HISTORY - GEOGRAPHY - WILDLIFE - CULTURE - PEOPLE - LANGUAGES - TRAVEL - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
![Africa Quiz, Africa Trivia, Quiz, African History, Geography, Wildlife, Culture](data:image/svg+xml,%3Csvg%20xmlns=%22http://www.w3.org/2000/svg%22%20viewBox=%220%200%20400%20266.66666666667%22%3E%3C/svg%3E)
Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada.
![Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada](data:image/svg+xml,%3Csvg%20xmlns=%22http://www.w3.org/2000/svg%22%20viewBox=%220%200%20300%20200%22%3E%3C/svg%3E)
Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA
![Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA](data:image/svg+xml,%3Csvg%20xmlns=%22http://www.w3.org/2000/svg%22%20viewBox=%220%200%20300%20200%22%3E%3C/svg%3E)
Health Health, a science-based community to discuss health news and the coronavirus (COVID-19) pandemic
- The pull-out method: Why this common contraceptive fails to deliverby /u/Kampala_Dispatch on July 26, 2024 at 7:51 pm
submitted by /u/Kampala_Dispatch [link] [comments]
- Health Canada data reveals surprising number of adverse cannabis reactions (spoiler: it's small)by /u/carajuana_readit on July 26, 2024 at 5:49 pm
submitted by /u/carajuana_readit [link] [comments]
- Online portals deliver scary health news before doctors can weigh inby /u/washingtonpost on July 26, 2024 at 4:37 pm
submitted by /u/washingtonpost [link] [comments]
- Vaccine 'sharply cuts risk of dementia' new study findsby /u/SubstantialSnow7114 on July 26, 2024 at 1:53 pm
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- Calls to limit sexual partners as mpox makes a resurgence in Australiaby /u/boppinmule on July 26, 2024 at 12:31 pm
submitted by /u/boppinmule [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 that in Thailand, if your spouse cheats on you, you can legally sue their lover for damages and can receive up to 5,000,000 THB ($140,000 USD) or more under Section 1523 of the Thai Civil and Commercial Codeby /u/Mavrokordato on July 26, 2024 at 6:57 pm
submitted by /u/Mavrokordato [link] [comments]
- TIL that with a population of 170 million people, Bangladesh is the most populous country to have never won a medal at the Olympic Games.by /u/Blackraven2007 on July 26, 2024 at 6:49 pm
submitted by /u/Blackraven2007 [link] [comments]
- TIL a psychologist got himself admitted to a mental hospital by claiming he heard the words "empty", "hollow" and "thud" in his head. Then, it took him two months to convince them he was sane, after agreeing he was insane and accepting medication.by /u/Hadeverse-050 on July 26, 2024 at 6:44 pm
submitted by /u/Hadeverse-050 [link] [comments]
- TIL Senator John Edwards of NC, USA cheated on his wife and had a child with another woman. He tried to deny it but eventually caved and admitted his mistake. He used campaign funds and was indicted by a grand jury. His life story inspired the show "The Good Wife" by Robert & Michelle Kingby /u/AdvisorPast637 on July 26, 2024 at 6:09 pm
submitted by /u/AdvisorPast637 [link] [comments]
- TIL Zhang Shuhong was a Chinese businessman who committed suicide after toys made at his factory for Fisher-Price (a division of Mattel) were found to contain lead paintby /u/Hopeful-Candle-4884 on July 26, 2024 at 4:43 pm
submitted by /u/Hopeful-Candle-4884 [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.
- Human decision makers who possess the authority to override ML predictions may impede the self-correction of discriminatory models and even induce initially unbiased models to become discriminatory with timeby /u/f1u82ypd on July 26, 2024 at 6:29 pm
submitted by /u/f1u82ypd [link] [comments]
- Study uses Game of Thrones (GOT) to advance understanding of face blindness: Psychologists have used the TV series GOT to understand how the brain enables us to recognise faces. Their findings provide new insights into prosopagnosia or face blindness, a condition that impairs facial recognition.by /u/AnnaMouse247 on July 26, 2024 at 5:14 pm
submitted by /u/AnnaMouse247 [link] [comments]
- Specific genes may be related to the trajectory of recovery for stroke survivors, study finds. Researchers say genetic variants were strongly associated with depression, PTSD and cognitive health outcomes. Findings may provide useful insights for developing targeted therapies.by /u/AnnaMouse247 on July 26, 2024 at 5:08 pm
submitted by /u/AnnaMouse247 [link] [comments]
- New experimental drug shows promise in clearing HIV from brain: originally developed to treat cancer, study finds that by targeting infected cells in the brain, drug may clear virus from hidden areas that have been a major challenge in HIV treatment.by /u/AnnaMouse247 on July 26, 2024 at 4:57 pm
submitted by /u/AnnaMouse247 [link] [comments]
- Rapid diagnosis sepsis tests could decrease result wait times from days to hours, researchers report in Natureby /u/Science_News on July 26, 2024 at 3:50 pm
submitted by /u/Science_News [link] [comments]
Reddit Sports Sports News and Highlights from the NFL, NBA, NHL, MLB, MLS, and leagues around the world.
- Canada's men's and women's soccer teams have relied on drones and spying for years, sources sayby /u/FireLychee on July 26, 2024 at 3:37 am
submitted by /u/FireLychee [link] [comments]
- After a grueling Tour de France, top riders are racing to recover for Paris Olympics time trialby /u/Oldtimer_2 on July 26, 2024 at 12:54 am
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- Canada WNT coach Bev Priestman suspended, sent home from Olympics amid spying scandalby /u/Oldtimer_2 on July 26, 2024 at 12:49 am
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- 'End of an Era': TNT's 'Inside the NBA' Ending After NBA Chooses Amazonby /u/lame_building14 on July 26, 2024 at 12:36 am
submitted by /u/lame_building14 [link] [comments]
- Canada axes coach from Olympics over drone useby /u/chief_sitass on July 26, 2024 at 12:31 am
submitted by /u/chief_sitass [link] [comments]