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Why LLMs get dumb (Context Windows Explained)

#AI Tools
2025-04-14 17:4010 min read

Content Introduction

The video discusses the challenges of conversing with large language models (LLMs) like ChatGPT, particularly issues related to context windows, memory limitations, and hallucinations. It emphasizes the memory constraints in LLMs, which leads to forgetfulness during lengthy conversations similar to human interactions. The speaker illustrates this by comparing conversations with LLMs to personal experiences, highlighting that the longer and more complex the conversation, the more challenging it becomes to maintain coherence. Solutions such as increasing context length and leveraging techniques like flash attention and paged cache are proposed to tackle these issues. The video ends by promoting tools that can enhance the processing of information by LLMs, underscoring the significance of a powerful GPU and efficient memory usage for optimal performance.

Key Information

  • The speaker discusses interacting with Large Language Models (LLMs), mentioning they can give unexpected or confusing responses during long conversations.
  • The concept of 'context windows' is introduced, which refers to the memory LLMs can hold during conversations.
  • Different models like ChatGPT, Gemini, and Claude are introduced and their ability to remember and forget information is explained.
  • As conversation length increases, the models may forget previous context, leading to irrelevant or incorrect responses.
  • The speaker illustrates a conversation scenario highlighting how LLMs 'hallucinate' or make mistakes when they lose track of context.
  • Concepts such as 'self-attention mechanisms' and how they function in LLMs are discussed, emphasizing how words are weighted based on their relevance.
  • The need for efficient GPU resources to run LLMs with large context windows is addressed, along with methods to optimize memory usage.
  • The importance of using strong GPUs and the challenges faced when operating with large models is highlighted.
  • A practical solution involving the use of a tool called 'Gina' is introduced, which helps convert web pages into formats usable for LLMs.
  • Finally, the potential risks associated with LLMs, such as overloading memory and vulnerabilities to hacking, are discussed.

Timeline Analysis

Content Keywords

LLMs

Large Language Models (LLMs) can forget information, hallucinate, and process multiple topics, leading to inaccuracies in conversations. The nature of memory in LLMs is often limited by their context windows.

Context Windows

Context windows dictate how much information LLMs can retain and utilize in a conversation. Size limitations of these windows can affect the performance of LLMs, often leading to failures in memory recall and accuracy.

Tokenization

Tokens are used by AI to measure input length. Different LLMS calculate tokens differently, which can affect how they interpret and respond to inputs, requiring granular attention mechanisms.

AI Memory

AI memory refers to the short-term and context-specific memory in LLMs, which can sometimes forget information over longer conversations, impacting performance and user experience.

AI Speed

As context increases in complexity, the speed of LLMs may decrease, resulting in slower responsiveness in conversation. The computational load on the system's GPU also influences speed.

Flash Attention

An experimental feature aimed to optimize how models handle context, allowing for quicker processing of input without compromising more significant amounts of data.

Scaling AI Models

Scaling AI models involves balancing the demand for processing power against hardware limitations, like GPU VRAM, ensuring the model remains efficient while expanding its capabilities.

AI Hallucination

AI hallucination refers to instances where the model generates responses that are incorrect or irrelevant due to context overload or inaccuracies in memory processing.

Local AI Models

Local AI models provide users with the capability to run AI on personal hardware, making them faster but reliant on local resources, such as GPU VRAM.

AI Applications

Applications utilizing AI models must efficiently manage conversations and retain context to improve accuracy and relevance, especially when querying information.

Related questions&answers

Why do LLMs sometimes give strange answers?

LLMs can hallucinate, forget context, or supply erroneous information due to limitations in their memory and processing.

What is a context window in LLMs?

A context window refers to the maximum amount of previous conversation that the model can remember at any one time.

How do LLMs remember details from a conversation?

LLMs have short-term memory that allows them to keep track of a limited amount of context, similar to how humans remember details during a conversation.

What happens when conversations go longer than the LLM’s context window?

When conversations exceed the context window, LLMs may start to forget earlier parts of the conversation, leading to less coherent interactions.

Why might an LLM forget what was previously discussed?

LLMs can forget previous information when the conversation extends beyond their memory capacity, which is defined by the context window.

How can I improve my experience with LLMs?

To enhance interactions with LLMs, consider starting a new chat for different topics and ensuring that the context remains relevant.

What are the limitations of LLMs regarding context?

LLMs have a limited context window that can affect their ability to manage information over longer interactions, potentially leading to inaccuracies.

What technological advancements are improving LLM memory?

Technologies like flash attention and paged cache help manage memory limitations by improving how data is processed.

Can LLMs process large amounts of data efficiently?

While LLMs can process large amounts of data, they may slow down or become less accurate if their memory usage exceeds available resources.

What is flash attention in LLMs?

Flash attention is a technique that optimizes how LLMs calculate attention scores, improving performance and memory usage.

What can I do if my LLM seems to lose track of the conversation?

If an LLM loses track, consider limiting the length of the conversation or initiating a new chat to allow for better context retention.

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