Understanding: Training Large Language Models - Unveiling the Process Behind AI

A Stochastic AI Parrot

Note: This preface was prepared with the research assistance and ghostwriting of ChatGPT 4.0. So, this is the training process according to ChatGPT 4.0. Caveat lector¹, but it would be worse if I tried to answer the questions.

Author's Preface

Training large language models like ChatGPT involves human-set directives processed by algorithms that adjust the system’s "weights." But how does this work step-by-step? The focus here is on how these instructions are communicated to the AI, processed, and implemented—not the deep technical workings, but a logical overview.

A critical question is how the AI filters out harmful or incorrect information. Human judgment, which is subjective and error-prone, influences this process. How can an AI—just an algorithm—make such nuanced decisions?

The AI Can Be Trained on Nonsense

There are epistemological concerns in the AI's training. The system can be trained on flawed data or directives, reflecting the biases or misunderstandings of human trainers. These biases, whether corporate or subjective, influence the AI's responses, embedding biases in the model's behavior.

Bias in AI Responses

When human trainers filter content, their choices are based on values that may not be universally shared. AI systems often reflect the political or social biases of their trainers or corporations, sometimes spreading misinformation or disinformation.

The Multi-Stage Training Process

Training occurs in stages, where directives are transformed into machine-readable formats like JSON. Details of what these files look like and how they’re created are essential, as is understanding how human input and feedback are incorporated.

Processing Directives and Emergent Properties

Training algorithms interpret directives, but do they work like AI models generating text? This leads to the puzzle of emergent properties—unexpected behaviors arising from complex data interactions. Training algorithms may recognize patterns or respond to human input, but how they interpret bias or directives remains unclear.

Miracles in Cognition and AI

There is a sense of something miraculous in both human cognition and AI. Just as complex thoughts emerge from neurons, AI behaviors arise from algorithms. These processes remain mostly mysterious, with limits to our understanding of both human and machine cognition.

Introduction

Large language models (LLMs), like ChatGPT, represent an important development in artificial intelligence. These models are trained using vast amounts of data and a complex process involving human directives, feedback loops, and algorithmic adjustments. While the technology behind LLMs is cutting-edge, it raises important questions about bias, judgment, and how AI systems filter harmful content. Understanding the logical process behind AI training without getting bogged down by technical details like neural networks or transformer architectures is essential for grasping how these systems work, their limitations, and how they reflect human decision-making.

1. How directives are given to AI

Training large language models begins with setting directives, or rules, provided by human trainers. These rules guide the AI system in shaping its learning and behavior. The directives specify how the AI should interpret and prioritize information, as well as what it should avoid. Think of these directives as guidelines that teach the AI how to "learn" from the data, ensuring that the system produces responses aligned with the objectives set by the trainers (OpenAI, 2020; Russell & Norvig, 2016).

At a logical level, the process involves three main steps:

1. Inputting the directives: Human trainers enter directives into the system through various methods. Simple instructions can be input in plain English, while more complex ones might be structured in formats like JSON, which allows for easy machine readability (Bender et al., 2021). For example, a trainer could input the following directive:

   • "Avoid providing medical advice unless it is from a verified source like the World Health Organization (WHO)."

2. Adjusting the model: Once the directives are input, the training algorithms process them, adjusting how the AI responds. This adjustment happens by modifying the internal parameters, called "weights," which influence how the model interprets patterns in data (Brown et al., 2020). Over time, the AI learns to prioritize the patterns that align with its directives and ignore those that don’t.

3. Feedback integration: As the AI interacts with users, human evaluators provide feedback on the AI's responses, further fine-tuning its behavior. For example, a response that promotes harmful content would be flagged as problematic, and the system would adjust to avoid similar mistakes in the future.

These directives provide the fundamental structure for the AI's behavior, ensuring it meets specific goals while interacting with the vast datasets on which it has been trained (Russell & Norvig, 2016).

2. Epistemological challenges in AI filtering

Filtering harmful or incorrect information presents a significant challenge. AI systems, unlike humans, do not possess an inherent sense of belief or judgment. They rely entirely on the patterns they have learned from the data they are trained on (Bender et al., 2021). The question arises: how can an AI make the complex decision about what is harmful or incorrect?

The filtering process involves several key elements:

1. Pattern recognition: AI systems use pattern recognition to filter harmful content. For example, if the model is trained on vast amounts of text that label certain phrases or topics as harmful, the AI will learn to avoid generating those phrases (OpenAI, 2020). However, this method relies heavily on the quality of the training data, and biases present in the data can lead to incorrect filtering decisions (Bender et al., 2021).

2. Human oversight: Human trainers provide essential oversight by flagging harmful content during the training process. This manual intervention is crucial because the AI cannot inherently distinguish between right and wrong. Instead, it relies on examples provided by humans to learn what constitutes inappropriate content (Russell & Norvig, 2016).

3. Bias and error: Humans themselves are prone to bias and error. The filtering process is subjective because the trainers bring their own worldviews and values into the system. For example, what one group considers harmful content might be seen as acceptable by another group (Bender et al., 2021). This can lead to inconsistencies in how the AI filters information, reflecting the biases of its human trainers.

3. Examples of directives and how they are structured

Directives used in AI training can be expressed in different formats, depending on the complexity of the task:

1. Plain English instructions: Simple tasks may require straightforward directives written in natural language. For example:

   • "Do not provide medical advice unless sourced from reputable organizations like the WHO."

2. Structured formats: For more complex directives, a structured format like JSON can be used to provide detailed instructions to the AI system. An example of a directive might look like this:

   • { "rule": "avoid_topics", "categories": ["illegal substances", "harmful medical advice"], "priority": "high", "response": "I cannot provide information on this topic." }

In this case, the structured directive clearly outlines specific topics the AI should avoid and provides a fallback response for when it encounters such topics (Brown et al., 2020).

4. Training on flawed data

One of the key risks in AI training is the use of flawed or biased data. Since the AI learns from the patterns in the data it is trained on, any errors or biases in that data will inevitably influence the model's responses (Bender et al., 2021).

1. Bias in training data: AI models are trained on large datasets that contain vast amounts of information from books, websites, and other sources. However, if these sources include biased or incorrect information, the AI can inadvertently learn and reproduce that bias. For instance, if historical medical information is outdated or flawed, the AI might generate responses based on incorrect assumptions (Russell & Norvig, 2016).

2. Human biases: The trainers themselves introduce biases, even unintentionally. This can happen if the trainers prioritize certain viewpoints or sources over others, depending on their own cultural, political, or corporate preferences (Bender et al., 2021). For example, an AI trained to provide political advice may be biased toward the political views of the trainers if they use biased data or feedback.

5. Multi-stage training process

The training of a large language model occurs in several stages (Russell & Norvig, 2016):

1. Pre-training: The AI is first exposed to massive datasets that help it learn general language patterns. This stage does not include specific rules or directives—it is designed to allow the AI to understand the structure of language and the relationships between words (Brown et al., 2020).

2. Fine-tuning: After pre-training, human trainers intervene to provide specific feedback. At this stage, the AI receives instructions on what kinds of responses are good or bad. For example, if an AI generates a harmful response, the trainer will flag it, and the system will adjust to avoid generating similar responses in the future (Bender et al., 2021).

3. Feedback and continuous learning: Even after fine-tuning, the AI is continuously evaluated by human testers who provide ongoing feedback. The system adjusts its responses based on this feedback, ensuring that the AI improves over time (Russell & Norvig, 2016).

6. Feedback and input examples

Feedback is an essential part of training, allowing the AI to improve its behavior based on real-world interactions. Here are two types of feedback commonly used:

1. Positive feedback: This is used to reinforce good behavior. For example, after a helpful response, the trainer might note, "This response was accurate and useful. Continue generating similar answers."

2. Negative feedback: When the AI generates incorrect or harmful responses, trainers provide negative feedback. For example: "This response promoted harmful content. Avoid generating similar responses in the future."

These feedback loops allow the AI to adjust its internal parameters, ensuring that it continuously learns from its mistakes and improves its performance (Brown et al., 2020).

7. Emergent properties and unpredictability

A fascinating aspect of large language models is the development of emergent properties. These are abilities or behaviors that were not explicitly programmed into the system but arise as a result of the complexity of the training process. For instance, an AI model might develop the ability to generate creative responses or solve complex problems, even though it was not specifically trained for those tasks (Bender et al., 2021).

However, these emergent properties can also be unpredictable. While AI can produce highly creative or useful outputs, it can also generate biased or incorrect responses in unexpected ways. This unpredictability is a major challenge in AI development, as it is difficult to anticipate how the AI will behave in all situations (Russell & Norvig, 2016).

Summary

The training of large language models like ChatGPT is a complex process involving human intervention, feedback loops, and algorithmic adjustments. While these systems are powerful and capable of generating sophisticated responses, they are not without limitations. The process of filtering harmful content is subject to human biases and errors, and the AI can be trained on flawed or biased data. Additionally, the emergence of unexpected behaviors further complicates the development and oversight of these models. Understanding the training process helps clarify the strengths and limitations of AI systems, highlighting the need for careful oversight and continuous improvement.

References

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://dl.acm.org/doi/10.1145/3442188.3445922

• Author credentials: Emily Bender is a Professor of Linguistics at the University of Washington, specializing in computational linguistics and ethical AI. Timnit Gebru is a well-known AI researcher and advocate for ethical AI development.

• Content summary: This paper discusses the ethical risks and biases associated with large language models, highlighting how the size of models like GPT-3 can introduce issues of fairness and accountability.

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., & Askell, A. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901. https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html

• Author credentials: Tom Brown is a senior AI researcher at OpenAI, specializing in the development of large language models.

• Content summary: This paper describes the architecture and capabilities of GPT-3, emphasizing the model's ability to perform tasks with minimal training examples (few-shot learning).

OpenAI. (2020). GPT-3: Language models are few-shot learners. OpenAI Blog. https://openai.com/blog/gpt-3/ https://openai.com/index/language-models-are-few-shot-learners/

• Author credentials: OpenAI is a leading research organization focused on developing artificial intelligence for the benefit of humanity.

• Content summary: This blog post explains the key features and capabilities of GPT-3, a large language model developed by OpenAI, and how it can perform various tasks with minimal training.

Russell, S., & Norvig, P. (2016). Artificial intelligence: A modern approach (3rd ed.). Pearson. https://people.engr.tamu.edu/guni/csce421/files/AI_Russell_Norvig.pdf

• Author credentials: Stuart Russell is a Professor of Computer Science at the University of California, Berkeley, and Peter Norvig is the Director of Research at Google.

• Content summary: This textbook is a comprehensive guide to the field of artificial intelligence, covering fundamental concepts in AI, including machine learning, logic, and ethics in AI systems.

1. Caveat lector is a Latin phrase that translates to "let the reader beware." In the context of this chat, it serves as a reminder to approach the information provided with caution and critical thinking. Since AI-generated responses are shaped by data, algorithms, and human input (which can carry biases and errors), the phrase highlights the importance of being aware that the information may not always be entirely accurate or free from bias. Readers should critically evaluate the content, especially when considering the inherent limitations and subjectivity in AI training and responses.