Last update: Saturday 2/1/25
Chain of reasoning (also called chain-of-thought prompting) is a technique that helps language models break down complex problems into smaller, sequential steps. Rather than jumping straight to an answer, the model explicitly works through the problem step-by-step, similar to how humans show their work when solving math problems.
For example, if asked to calculate the cost of a shopping trip, a model using chain of reasoning would first list out each item's price, then add them together, then apply any discounts or tax, showing each step of the calculation. This process helps reduce errors and makes the model's thinking more transparent. It's particularly effective for tasks involving math, logic, or complex reasoning.Reinforcement Learning from Human Feedback (RLHF) is a training method where the model learns to generate better outputs based on human evaluations. The process typically works in three main phases: First, humans provide examples of good responses to various prompts, which the model learns from. Second, humans rank different possible responses from best to worst, helping create a "reward model" that can predict which outputs humans would prefer. Finally, the model is fine-tuned using reinforcement learning to maximize these predicted human preferences.
It's like having a writing teacher who gives feedback on essays - the model learns what makes a response helpful, accurate, and appropriate by receiving consistent human guidance. This process helps align the model's outputs with human values and preferences.
"Mixture of Experts (MoE): This is an architecture where instead of having one large neural network process all inputs, you have multiple "expert" networks that specialize in different types of inputs, along with a "gating" network that decides which expert(s) should handle each input. This allows for more efficient use of model capacity since only relevant experts are activated for each task."
- "Transformers: A transformer is a neural network architecture that revolutionized natural language processing. Let me break it down:
Note that a justly acclaimed visual explanation of transformers can be found here ➡ Transformers (how LLMs work) explained visually (YouTube)Key Components and Concepts:
1. Core Innovation: Instead of processing text sequentially (like older RNN models), transformers process all words in parallel and use attention mechanisms to understand relationships between words.
2. Main Structure:
- Encoder: Processes the input text
- Decoder: Generates the output text
- Each contains multiple "layers" of identical structure
3. Key Mechanisms:
- Self-Attention: Allows each word to "look at" all other words to understand context
- Feed-Forward Networks: Process the attention outputs
- Position Encodings: Since the model processes all words at once, these tell it word order
- Layer Normalization: Helps keep training stable
- Residual Connections: Help prevent information loss
Real-world example of how attention works in a transformer:
For the sentence "The dog chased its ball":
- When processing "its", the self-attention mechanism gives high weights to "dog"
- When processing "ball", it might pay attention to both "dog" and "chased"
Modern language models like GPT are "decoder-only" transformers, while models like BERT are "encoder-only". The original transformer paper was called "Attention is All You Need" (2017) because it showed you could achieve excellent results using just attention mechanisms, without the recurrent or convolutional layers that were previously thought necessary."
"Attention (Basic Concept):
Attention in neural networks is like a focused spotlight mechanism. When processing a sequence (like a sentence), for each element (like a word), attention helps the model figure out which other elements it should "pay attention to" in order to understand the current element better. It does this by computing relationship scores between elements.
For example, in the sentence "The cat sat on the mat because it was comfortable", attention helps the model understand that "it" refers to "mat" by creating stronger attention scores between these words. The model essentially asks "when processing each word, how much should I focus on every other word?"
The actual mechanics involve calculating similarity scores between elements, converting these to weights (usually through softmax), and using these weights to create a weighted sum of values.
Multi-head Latent Attention (MLA):
This is an enhanced version of attention with two key modifications:
1. "Multi-head" means the attention mechanism is split into several parallel "heads", each potentially learning different types of relationships. One head might focus on syntactic relationships, another on semantic relationships, etc.
2. The "latent" part is what makes it special: Instead of directly computing attention between input elements, MLA introduces a set of learnable intermediate vectors (latent vectors). The attention process goes through these latent vectors first. Think of them as learned "reference points" or "memory slots" that help organize and structure the attention process.
The advantage of this approach is that it can:
- Reduce computational complexity (especially for long sequences)
- Provide a more structured way of organizing attention patterns
- Potentially capture more abstract relationships through these learned latent intermediaries"
"Softmax is a mathematical function that takes a vector of numbers and turns them into a probability distribution - meaning all the numbers become values between 0 and 1, and they all sum up to 1 (or 100%).
Let me give you a concrete example:
Let's say we have three raw scores from a neural network: [2.0, 1.0, 0.1]
Softmax transforms these in two steps:
1. First, it exponentiates each number (e^x):
- e^2.0 ≈ 7.389
- e^1.0 ≈ 2.718
- e^0.1 ≈ 1.105
2. Then it divides each by the sum of all exponentials (7.389 + 2.718 + 1.105 = 11.212):
- 7.389/11.212 ≈ 0.66 (66%)
- 2.718/11.212 ≈ 0.24 (24%)
- 1.105/11.212 ≈ 0.10 (10%)
Now we have probabilities that sum to 100%!
In the context of attention, softmax is used to convert raw "similarity scores" between words into attention weights. The higher scores get emphasized (become larger probabilities) while lower scores get diminished (become smaller probabilities), but everything stays proportional and sums to 1. This makes it perfect for deciding how much attention to pay to each word.
The exponential nature of softmax also helps create sharper distinctions - big differences in input numbers become even bigger differences in probabilities, which helps the model make clearer decisions about what to focus on."
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