I like to think of inference as a collection of nearly infinite puzzles in which an LLM is capable of recognizing all the shapes of the pattern and reorganizing the pieces so they fit cohesively without necessarily knowing what that actual picture is. Still, because LLMs can compare the characteristics of every piece to every other one, they create increasingly more convincing works. The key to this taking shape is the metadata humans have attached to some of the pieces. These predictive processes are complex calculations beyond our general comprehension. LLMs currently have limitations in reasoning and beyond enhanced pattern matching, but the field continues to evolve and it’s been speculated that advances in LLM reasoning about inference may exist in OpenAI’s Q-star/Strawberry/Orion project.
How Large Language Models Think
Large Language Models don't just regurgitate information—they perform a complex dance of pattern recognition and probabilistic reasoning. This process, known as "inference," is the core of our current generation of AI models. Derived from the Latin "inferre," meaning "to bring in" or "to deduce," inference empowers machines to generate human-like responses, pushing the boundaries between artificial and human intelligence.
Inference refers to the ability of an LLM to infer or draw conclusions, and inference time is the specific period in which the LLM is making these inferences.
What is LLM Inference?
Inference in AI involves applying trained models to new data to make predictions or decisions, which is a process or method integral to the functionality of AI systems. Another way to imagine LLM inference is like a sophisticated game of word association. If you asked an AI something like, “How can I colour a bowl of chips orange for a Halloween party," it doesn't just retrieve a pre-written response. Instead, it breaks down your message into smaller pieces called tokens, such as "colour," "bowl," and "chips." These tokens are then converted into numerical vectors, which the AI uses to understand and compare meanings. In this case, a sophisticated AI might notice the British spelling of "colour" and infer that you're referring to "chips" as fries, not potato chips, so its reply might involve cutting potatoes into oblong chunky shapes rather than thin slivers.
Inference is not just about applying a model; it involves transforming complex understandings and relationships captured during training into actionable results or outputs. The process requires significant computational power, especially for models like GPT or BERT, as it deals with large amounts of data through deep neural networks. In OpenAI’s GPT, this transformation turned the input words into coherent text by predicting the next word in the sequence, creating a logical flow of ideas. In BERT, the input words were analyzed in both directions, allowing the model to understand the full context and provide more accurate interpretations or answers. Large language models rely on deep neural networks, which are like layers of connected "thinking" units that process data to identify patterns and relationships.
The Evolution of Inference
The concept of inference in AI has evolved significantly over time. Early AI systems relied on logical rules to make deductions, while modern systems use statistical models and deep learning techniques. As AI technologies advanced, so did the sophistication of inference, allowing for more nuanced predictions and the ability to learn from vast datasets. This evolution has made inference a cornerstone of AI's capability to understand and interact with complex environments.
The Inference Process: A Step-by-Step Breakdown
Tokenization: Your message gets broken down into smaller pieces called tokens. These could be words, parts of words, or even punctuation marks.
Encoding: These tokens are converted into numbers that the AI can understand.
Processing: The AI's neural network processes these numbers, using patterns it learned during training.
Prediction: Based on this processing, the AI predicts the most likely next token.
Repetition: Steps 3 and 4 repeat until the AI has generated a full response.
This process happens incredibly quickly, often in fractions of a second, but it's not perfect.
Inferencing speeds are measured in something called latency, the time it takes for an AI model to generate a token — a word or part of word— when prompted. -IBM
The Challenges of LLM Inference
While LLM inference can produce impressive results, it faces several hurdles:
Computational Cost: Running these models requires significant computing power.
Consistency: Because the process involves probability, responses can be inconsistent.
Bias: LLMs can reflect biases present in their training data.
Hallucination: Sometimes, LLMs confidently state incorrect information.
Recent Advances in LLM Inference
Researchers are constantly working to improve LLM inference. Some recent developments include:
Sparse Inference: This technique only activates relevant parts of the neural network, potentially speeding up processing.
In-context Learning: Some models can adapt their responses based on a few examples provided in the prompt, improving versatility.
Retrieval-Augmented Generation: This method allows models to access external information during inference, potentially improving accuracy.
Inference-Time Intervention (ITI): his approach involves modifying the model's behavior during inference without retraining, allowing for real-time adjustments to improve output quality, reduce biases, or align the model with specific goals.
What Does This Mean for People Using AI?
Understanding LLM inference can help us make more accurate requests, then interpret and critically evaluate AI responses. It reminds us that while AI can be incredibly useful, it's not infallible.