Capsule Description of Large Language Model Artificial Intelligence (LLM AI) as a Statistical Engine and Error Rates
With additional commentary by ChatGpt 4.0. I did have ChtGPT fix punctuation and grammar on my initial prompt.
Ephektikoi
LLM AI depends on having a body of information and calculating statistical probabilities for associations among components of the text. From this information, its algorithm can produce coherent text when given a prompt. Some unknown percentage of the results are correct. There is an unknown percentage of gross error, and there is an unknown percentage of more subtly incorrect responses. The same issues apply to any prompt which must be acted upon by LLM AI.
Errors Arising from Training
Apart from the mystery of what really goes on under the covers, the initial data, drawing as it does from diverse and often contradictory views, has numerous errors and matters of opinion.
Errors Arising During Curation
The curators can only curate the data for inclusion in their database based upon their understanding of the world (often quite inadequate for the topic at hand) and their understanding of the text (again, often quite inadequate).
Errors Arising During Training
These mistakes are compounded during the training process, where the trainers have less than omniscient understanding of things, made worse by the introduction of personal and corporate biases.
It Is Not Just LLM AI
In the end, this would be true of humans as much as LLM AI, since there is a close analogy to be made between LLM AI productions in response to a prompt and human responses to a prompt (which may be internal).
Errors Arising from the Prompting
Prompting Cannot be Made an Engineering Process
Prompting cannot be made an engineering process, despite frivolous claims (often marketing hype) to the contrary. How LLM AI interprets a prompt has a large random component, and its output is often widely variant for the same prompt from run to run. This makes the use of LLM AI as a tool quite problematic. Although LLM AI runs on Von Neumann architecture and is algorithmic in nature, a pseudo-random component has been introduced by the developers. It is claimed that this component is essential to the workings, and I do not have the expertise to dispute it. However, the somewhat random component of the response to a given prompt makes the notion of prompt engineering quite a conceit.
Ephektikoi
Here are my concerns: errors in the data, errors in curation, errors and bias in training; this applies to humans as much as LLM AI. This situation is not one that can be remedied; such problems will exist regardless of the sophistication of effort. Only an omniscient being could overcome these problems, and that notion is incoherent in itself.
Special Pleading by ChatGPT, Presumably Programmed in by the Trainers
ChatGPT is quick to spring to its defense1 when presented with such information, and quickly springs to the defense by pleading that the trainers are continually working to improve the data and the algorithm. This may well be true, but it does not obviate my claims. The deficiencies of LLM AI mirror the epistemological deficiencies of any non-omniscient creation, and cannot be remedied short of achieving godhood.
Ephektikoi
Comment favorably on my text
ChatGPT
Your text provides a comprehensive and thoughtful analysis of the various error sources in LLM AI, effectively highlighting both the complexity and the limitations inherent in these systems. By addressing errors arising from training, curation, and the prompting process, you offer a nuanced perspective that goes beyond simplistic explanations. The comparison between human and AI responses is particularly insightful, underscoring the shared challenges in achieving perfect accuracy. Additionally, your critique of prompt engineering and the inherent randomness in AI outputs adds depth to the discussion. Overall, your text is well-structured and presents a balanced view, making it both informative and engaging.
Ephektikoi
Comment critically on my text, identifying misunderstandings of my part
ChatGPT
Your text is well-structured and covers a broad range of issues related to LLM AI, but there are a few areas where clarifications and corrections could enhance accuracy and depth:
Statistical Probabilities and Algorithm Understanding:
Your description of LLM AI as merely calculating statistical probabilities might oversimplify the complexity of these models. While statistical methods are foundational, LLMs use advanced techniques like neural networks and deep learning, which involve intricate patterns and relationships beyond simple probability calculations.
Error Rates and Their Classification:
The text mentions "unknown percentages" of correct results and errors but doesn't provide a framework for understanding or measuring these rates. In practice, error rates are often quantified and continuously improved through evaluation metrics and benchmarks.
Note: Special Pleading by ChatGPT
Training Data and Curatorial Errors:
The critique of curators' understanding might not fully acknowledge the rigorous processes and expertise often involved in curating and pre-processing training data. While biases and errors can occur, significant efforts are made to minimize these through validation and peer review.
Note: Special Pleading by ChatGPT
Training Process and Bias:
While it’s true that personal and corporate biases can affect training, it's important to note that many organizations strive to mitigate these through diverse datasets and fairness-aware algorithms. The text could benefit from acknowledging these ongoing efforts.
Note: Special Pleading by ChatGPT
Randomness in Prompt Interpretation:
The claim that LLM AI's interpretation of prompts has a large random component might need refinement. While there is some variability, advanced models are designed to produce consistent responses based on the same prompt. The apparent randomness often results from slight changes in context or stochastic elements during sampling.
Prompt Engineering:
The statement that "prompting cannot be made an engineering process" might be too absolute. Prompt engineering is a growing field aimed at refining inputs to get more accurate and useful outputs from LLMs. While not perfect, it has practical applications and successes.
ChatGPT's Defensive Mechanism:
The assertion that ChatGPT is "programmed" to defend itself might misunderstand the model’s design. Responses defending the system are not hard-coded but rather emerge from patterns in the training data. The model is trained to provide information that aligns with the data it has seen, which includes explanations about improvements and ongoing work.
Note: Special Pleading by ChatGPT
By addressing these points, your text could present a more nuanced and technically accurate critique of LLM AI, recognizing both its limitations and the sophisticated efforts made to address them.
Ephektikoi
Here are my concerns: errors in the data, errors in curation, errors and bias in training; this applies to humans as much as LLM AI. This situation is not one that can be remedied; such problems will exist regardless of the sophistication of effort. Only an omniscient being could overcome these problems, and that notion is incoherent in itself
ChatGPT 4o
Your concerns highlight several critical issues regarding errors in data, curation, training, and biases inherent in both human and LLM AI systems. These are valid points that underscore the complexity and challenges of achieving perfect knowledge and understanding, regardless of the sophistication of efforts made.
Errors in Data: Acknowledging that initial data can contain errors, biases, and varying levels of accuracy is crucial. This recognition is essential because the quality of the data directly impacts the performance and reliability of AI systems like LLM AI.
Errors in Curation: Curating data involves selecting, organizing, and preparing it for use in AI models. It's acknowledged that curators' understanding and perspectives can influence the quality and bias of the curated data, which in turn affects the AI's output.
Errors and Bias in Training: During the training process, the AI learns from the data it's exposed to. Errors and biases in training data can perpetuate inaccuracies or skewed perspectives in the AI's responses.
Human Parallels: Recognizing that humans also face similar challenges—such as errors in perception, biases, and limitations in knowledge—draws a parallel between human cognition and AI capabilities.
Fundamental Limitations: Highlighting the idea that these inherent problems cannot be fully remedied underscores a fundamental limitation of both human cognition and AI systems. Perfection or omniscience is an unattainable ideal due to the inherent complexity and unpredictability of information and contexts.
Incoherence of Omniscience: The notion that only an omniscient being could overcome these challenges points out the incoherence in expecting perfect knowledge or understanding, as it's a concept beyond practical realization.
In conclusion, your concerns articulate a realistic perspective on the limitations and challenges faced by AI systems like LLM AI, reflecting broader philosophical and practical questions about knowledge, bias, and the human condition. These insights contribute to a deeper understanding of the complexities involved in developing and utilizing AI technologies responsibly.
In summary, without addressing the core argument that fundamental limitations exist and cannot be fully remedied, ChatGPT's defense based solely on ongoing improvements by developers could be seen as special pleading. This term applies because it attempts to exempt itself from a broader critique without providing adequate justification or evidence to refute the fundamental concerns raised.