Why LLMs get dumb (Context Windows Explained)

Summary

This video delves into the limitations and challenges of large language models (LLMs) like ChatGPT, focusing on the concept of context windows. It explains how LLMs have short-term memory, which can become overloaded during long conversations, leading to memory loss and hallucinations. The script highlights the importance of token limits, GPU memory, and computational power in maintaining performance. It also explores solutions such as increasing context windows, using flash attention, and optimizing data compression. Finally, the video discusses the potential vulnerabilities of LLMs with larger context windows and the need for caution in their use.

Takeaways

• 😀 LLMs like ChatGPT, Gemini, Claude, and local models (e.g., Llama) have short-term memory, known as the context window, which allows them to remember things during a conversation.
• 😀 As the length of a conversation grows, LLMs struggle to keep track of everything, just like humans, leading to memory loss and potential confusion in longer chats.
• 😀 The context window is limited by the number of tokens (words and symbols) the model can handle at one time, such as 2048 tokens for a local model like GEMMA 3.4B.
• 😀 Tokens are how LLMs count words in conversations, and different models may handle tokens differently, leading to discrepancies in token calculations.
• 😀 If the context window is exceeded, LLMs can forget earlier parts of a conversation, resulting in a loss of continuity, as shown when the model forgets the book the user is reading.
• 😀 Increasing the context window can help LLMs remember more, but larger context windows require more GPU power and memory resources, as seen when trying to load a model with 131,000 tokens.
• 😀 Local models with large context windows (e.g., GEMMA 3.4B with 128,000 tokens) can face performance issues, including lag and memory overload, especially on less powerful hardware.
• 😀 Cloud-based models, like GPT-4 and Gemini, can handle much larger context windows, such as 2 million tokens, without the same hardware limitations as local models.
• 😀 Attention mechanisms in LLMs, like self-attention, determine which parts of the conversation are most important, enabling the model to focus on relevant information while processing a response.
• 😀 Using tools like R.Gina.ai to convert webpages into markdown can help improve the way LLMs handle information by providing cleaner, more structured input.
• 😀 Large context windows and the complexities of attention mechanisms in LLMs mean that longer conversations can result in slower performance and hallucinations, as the model struggles to prioritize and manage information.

Q & A

What causes an LLM (like ChatGPT) to forget or hallucinate during long conversations?

LLMs can forget or hallucinate during long conversations because of the limitations of their context window, which is the amount of information they can remember at any given time. As the conversation grows longer, the LLM's short-term memory gets filled, and it may start to forget earlier parts of the conversation or make inaccurate statements.

What is a context window in an LLM?

A context window is the maximum amount of tokens (words, spaces, and punctuation marks) an LLM can consider during a conversation. Each token takes up space in the LLM's short-term memory, and once that window is full, older tokens get pushed out, causing the model to lose context and possibly forget or confuse information.

How do tokens differ from characters or words when an LLM processes text?

Tokens are units that LLMs use to count and process text. They can represent parts of words, whole words, or even punctuation marks. For example, a single word may be broken into multiple tokens, and spaces or commas may also count as separate tokens.

Why do LLMs like ChatGPT sometimes seem to lose track of the conversation?

LLMs can lose track of the conversation when their context window is full. When too many tokens are accumulated during a long conversation, the model is unable to keep all the information in memory, leading to confusion or forgetting earlier parts of the dialogue.

What are the consequences of running an LLM with a larger context window?

Running an LLM with a larger context window can improve memory retention and allow for longer, more coherent conversations. However, it requires significantly more GPU resources, such as video RAM (VRAM), and can lead to slower response times or computational issues if the hardware can't handle the increased demand.

What are the challenges associated with running LLMs locally on personal hardware?

Running LLMs locally can be challenging because they require a lot of memory and computational power, especially with large context windows. For example, large models like GEMMA 3.4B may require high-end GPUs and large amounts of VRAM to process long conversations without lag or memory issues.

What are the benefits of using 'flash attention' in LLMs?

Flash attention is a technique that optimizes the processing of tokens in an LLM by avoiding the need to store the full attention matrix in memory. This allows the model to process tokens more efficiently, reducing memory usage and speeding up the model's response time.

What is the significance of compression techniques like KC and V cache optimizations in LLMs?

Compression techniques like KC and V cache optimizations help reduce the amount of memory required to process tokens by compressing the data. These techniques allow LLMs to handle larger context windows more effectively and efficiently, reducing the computational load and improving response times.

How does the self-attention mechanism work in LLMs?

In a self-attention mechanism, the LLM assigns attention scores to different tokens in a conversation based on their relevance. The model decides which tokens are most important to consider for generating a response, much like how humans prioritize key information during conversations.

Why do larger context windows create more vulnerabilities in LLMs?

Larger context windows increase the amount of information an LLM processes, which can lead to a greater attack surface. Malicious inputs hidden in the middle of long conversations may bypass the model's safety measures, making it more susceptible to harmful or inappropriate outputs.