Context Window
“Larger context basically means you can write a larger text prompt, and get a larger and more detailed response back. So you could for example copy the text from multiple pages from a book (up to 300 pages, if the claims from the announcement are accurate), and then ask it to summarize the content, analyze, identify key points or themes, etc.” https://www.reddit.com/r/ChatGPT/comments/17pa61n/what_does_the_128k_context_window_mean_for/
“It also means that the AI will remember more of your long conversations. For example, let’s say you ask it to give you ideas for a story and it says, “This is a character named Paul whose brother is named Lenny.”” https://www.reddit.com/r/ChatGPT/comments/17pa61n/what_does_the_128k_context_window_mean_for/
“Then you keep asking for more and more details about the story, and it comes up with a story about Paul traveling to France and doing all of these interesting things. If you chat long enough and then ask it for the name of Paul’s brother, that first message could land outside of the context window, which means the AI will forget the answer it previously gave you. It might reply that Paul’s brother is named Dave, or it might even say that Paul doesn’t have a brother.” https://www.reddit.com/r/ChatGPT/comments/17pa61n/what_does_the_128k_context_window_mean_for/
“A longer context window allows you to have much longer conversations before it starts to “forget” things.” https://www.reddit.com/r/ChatGPT/comments/17pa61n/what_does_the_128k_context_window_mean_for/
The largest models, such as Google's Gemini 1.5, presented in February 2024, can have a context window sized up to 1 million (context window of 10 million was also “successfully tested”).
- Snippet from Wikipedia: Large language model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) and provide the core capabilities of chatbots such as ChatGPT, Gemini and Claude. LLMs can be fine-tuned for specific tasks or guided by prompt engineering. These models acquire predictive power regarding syntax, semantics, and ontologies inherent in human language corpora, but they also inherit inaccuracies and biases present in the data they are trained on.
They consist of billions to trillions of parameters and operate as general-purpose sequence models, generating, summarizing, translating, and reasoning over text. LLMs represent a significant new technology in their ability to generalize across tasks with minimal task-specific supervision, enabling capabilities like conversational agents, code generation, knowledge retrieval, and automated reasoning that previously required bespoke systems.
LLMs evolved from earlier statistical and recurrent neural network approaches to language modeling. The transformer architecture, introduced in 2017, replaced recurrence with self-attention, allowing efficient parallelization, longer context handling, and scalable training on unprecedented data volumes. This innovation enabled models like GPT, BERT, and their successors, which demonstrated emergent behaviors at scale such as few-shot learning and compositional reasoning.
Reinforcement learning, particularly policy gradient algorithms, has been adapted to fine-tune LLMs for desired behaviors beyond raw next-token prediction. Reinforcement learning from human feedback (RLHF) applies these methods to optimize a policy, the LLM's output distribution, against reward signals derived from human or automated preference judgments. This has been critical for aligning model outputs with user expectations, improving factuality, reducing harmful responses, and enhancing task performance.
Mechanistic interpretability seeks to precisely identify and understand how individual neurons or circuits within LLMs produce specific behaviors or outputs. By reverse-engineering model components at a granular level, researchers aim to detect and mitigate safety concerns such as emergent harmful behaviors, biases, deception, or unintended goal pursuit before deployment.
Benchmark evaluations for LLMs have evolved from narrow linguistic assessments toward comprehensive, multi-task evaluations measuring reasoning, factual accuracy, alignment, and safety. Hill climbing, iteratively optimizing models against benchmarks, has emerged as a dominant strategy, producing rapid incremental performance gains but raising concerns of overfitting to benchmarks rather than achieving genuine generalization or robust capability improvements.
The convergence of large-scale supervised pretraining, transformer architectures, and reinforcement learning–based fine-tuning marks the current frontier of LLM technology. This combined trajectory underpins the rapid progress in AI systems that deliver tangible benefits to end users: higher accuracy, greater adaptability, improved safety, and broader applicability across scientific, commercial, and creative domains.