The Deception of Consistency: Unraveling the randomness in LLMs

Your business needs consistent results, but are LLMs consistent?

Note: This post focuses solely on inconsistent outputs in LLMs and does not address hallucinations.

Have you ever wondered why (sometimes) the outputs of Large Language Models (LLMs) seem like they have a mind of their own? Amidst setting temperatures to 0 and controlling top p values, the cloak of consistency still eludes these AI marvels. A pressing question still lingers, just how much can we truly manipulate these AI behemoths? Can we ever truly understand the erratic nature of LLM outputs, even when the variables are rigorously controlled?

Dealing with something similar in your project? Reach out on LinkedIn today for consultations or development help for your generative AI projects!

What do we really mean by inconsistent LLM responses?

Inconsistency or Randomness in this context refers to the unpredictability in the model's output. It's like rolling a die; each roll (each LLM run with the same prompts) can give a different result within a defined set of possibilities. And don’t even get me started on sensitivity to how you word your prompts! And what’s worse, your prompt that works with one model might go completely haywire (or at least frustratingly different) when you feed it to other models. 

When an AI generates text, it doesn't always pick the most likely next word. Instead, it samples from a range of possible words, each with its own probability. This sampling introduces randomness.

In essence, randomness in generative AI and LLMs adds the spice of unpredictability, making interactions with these models feel more natural and engaging. Embracing this variability helps LLMs better emulate the richness and unpredictability of human language.

Why are outputs inconsistent in LLMs?

The basics? Let’s see.

1. Data Variability: LLMs are trained on vast amounts of diverse data from the internet. This diversity means they encounter a wide range of writing styles, topics, and contexts. As a result, their responses can vary widely depending on the input prompt and the specific part of the dataset ‘influencing’ (through learned parameters or weights) their output at that moment.

2. Context Sensitivity: LLMs generate responses based on the context provided in the prompt. Small changes in this context can lead to significantly different outputs. For instance, altering a single word or phrase can shift the model's understanding and thus its response.

3. Sampling and Randomness: LLMs use probabilistic sampling to generate text, meaning they select words and phrases based on their likelihood, given the context. This sampling introduces variability: even with the same input, the model might generate different outputs each time due to random choices made during sampling.

4. Model Complexity and Size: The complexity and size of LLMs contribute to their inconsistency. These models have millions or even billions of parameters, allowing them to capture subtle nuances in language but also making their behavior more intricate and less predictable.

What’s the deal with ‘temperature’ (and lesser known: top_p)

Temperature:

- What is it? Temperature is another setting that influences how creative or predictable the model's responses are.

- How does it work? Temperature controls the randomness of word selection during sampling. A higher temperature means the model is more likely to choose less probable words (from the options selected using top_p), even if they are not the most likely next word. A lower temperature makes it more likely to stick with the safest, most probable choices.

- Why is it used? It allows users to adjust how "creative" or "conservative" they want the model to be in its responses. Higher temperature settings can lead to more imaginative and varied outputs, while lower temperatures produce more predictable and safe responses.

Top p (or nucleus sampling):

- What is it? Top p sampling is a technique used in language models to select the options for next word or phrase to be generated.

- How does it work? Imagine the model has several possible words it could choose from to continue a sentence. Instead of considering all of them, it looks at the top p percent most likely options. For example, if p is set to 0.8 (80%), the model will only look at the words with a combined probability of getting selected 80% of the time.

- Why is it used? It helps balance between generating diverse responses and sticking to more probable ones. By focusing on the most likely choices (top p), the model can produce coherent sentences while still having some flexibility to surprise with less common words.

Temperature, top p and randomness

When you set both the temperature and top p parameters to 0, you are essentially asking the model to make the most deterministic predictions possible. Temperature controls the randomness in sampling words during generation, while top p controls the cumulative probability mass considered during sampling, fixing the list of words to choose from.

So, setting them to 0 should, in theory, lead to more consistent outputs.

But as many developers have observed in some scenarios (less often, but not never), even with both parameters set to 0, the model's inherent architecture and training data can still introduce nuances that result in slight inconsistencies. These models have been trained on vast amounts of data, and the interactions of different layers and components can sometimes lead to unexpected outputs.

Puzzling, right? Well, not so much. Read ahead to find out.

What else might introduce randomness in LLMs?

1. Model Initialization and Training Variability

• Initialization Seeds: The weights of neural networks are often initialized randomly before training begins. Different initializations can lead to different local minima in the loss landscape, which can affect the final trained model. 

• Training Data Shuffling: The order in which training data is presented to the model during training can influence the learning process. Even with the same data, different shuffles can result in slightly different models.

These 2 explain why outputs can be different even if LLMs are trained with the same data, same runs, same parameters.

2. Floating-Point Precision

• Computational Precision: LLMs perform numerous floating-point operations. Tiny differences in precision can accumulate and result in variations in the final output, even if the model parameters and inputs are the same.

3. Tokenization Differences

• Tokenization Ambiguities: The process of converting text into tokens can introduce slight variations. Different tokenization strategies or versions can lead to different token sequences, affecting the model’s input and subsequently its output.

4. Software and Hardware Differences

• Framework Versions: Different versions of the software libraries used for training and inference (like TensorFlow or PyTorch) can have slight differences in implementation, leading to variability in outputs.

• Hardware Differences: The specific hardware used for running the model (e.g., different GPUs or TPUs) can introduce slight differences due to variations in how operations are performed at the hardware level.

5. Inference Environment

• Load and Resource Management: The computational load and resource management of the system running the inference can impact performance. In a multi-threaded environment, for instance, slight differences in thread scheduling can cause variability.

• External Factors: Factors such as system noise, thermal conditions, or power fluctuations can subtly affect computations, leading to minor inconsistencies. When several of these inconsistencies occur over a series of computations - the results can look perceivably different.

6. Model Updates and Versions

• Model Evolution: As models are updated and improved over time, even small changes in the architecture or training process can lead to different outputs. Users might not always be aware of these updates.

But is all randomness bad? Read on!

When is randomness desirable in LLMs?

Randomness is not necessarily a bad thing. Here are some areas in which randomness in LLMs can actually be harnessed to get better results:

1. Creative Writing and Content Generation:

   - Storytelling: In scenarios where the AI is used to generate narratives or creative stories, randomness can introduce unexpected plot twists and character developments, making the storytelling more engaging and unpredictable.

   - Marketing Copy: For generating ad copy or promotional content, randomness can help in brainstorming fresh ideas and phrases, ensuring that the text is not repetitive and stands out.

2. Dialogue and Interaction:

   - Chatbots and Virtual Assistants: Randomness can make interactions with chatbots or virtual assistants feel more natural and less scripted. It allows for varied responses to similar queries, making the conversation flow more like a human interaction.

   - Customer Support: In customer service applications, randomness can prevent the model from giving identical responses to similar customer inquiries, thereby providing a more personalized and tailored experience.

3. Creativity and Exploration:

   - Content Creation Tools: In tools designed for creative writing, poetry generation, or brainstorming, randomness fosters exploration and innovation by offering diverse suggestions and ideas.

   - Educational Tools: Randomness can aid in generating varied examples and explanations in educational applications.

4. Exploratory Research:

   - Idea Generation: In research and development contexts, where exploring new ideas and hypotheses is crucial, randomness can spark unconventional connections and insights, driving innovation.

5. Entertainment and Gaming:

   - Interactive Fiction: In interactive storytelling or game development, randomness can create dynamic and unique experiences for each playthrough, increasing replay value.

   - Game Dialogue: Randomness in generating game dialogue can make non-player characters (NPCs) feel more diverse and realistic, reacting differently to player actions.

In a nutshell, randomness in generative AI and LLMs is not just about chance or chaos; it's about infusing creativity and dynamism into artificial intelligence, making the possibilities truly endless.  It adds a layer of unpredictability that can mimic human creativity and adaptability, making interactions with these models more natural and compelling.

Do you wish to explore how you can leverage LLMs and their inherent creativity to boost your creative processes? Reach out on LinkedIn today for consultations and/or development help!

But what if your business depends upon consistent responses and dependable AI technology?

Stay tuned for my next issue where I’ll deep dive into how you can manage this randomness and make it work for your business!