Understanding LLM AI: Mimicry or Comprehension
Do we just have a stochastic parrot? A short Chat, thank the deity for small mercies.
I said:
Large language models like ChatGPT often produce outputs that seem coherent and potentially accurate but occasionally generate wildly incorrect or nonsensical responses. Beyond confabulations or hallucinations, some outputs may appear absurd, though they might not seem so to someone unfamiliar with the subject. This may partly stem from questionable training data, leading to shallow, banal assertions unless carefully prompted to dig deeper. Such banalities likely dominate the dataset. When data on a specific topic is sparse, the model generalizes from statistically related topics, making its ability to produce seemingly sensible answers from complex statistical structures rather mysterious. However, these answers lack the kind of reasoning typical of humans. They only mimic reasoning, and it’s debatable whether they approximate human thought processes at all. Most would argue they don’t—and certainly not as effectively—though they can draw on vastly more data than any individual could in a lifetime. Yet, much of this data reflects shallow thinking, likely over represented on the internet, where incorrect assertions outnumber correct ones. As a result, training data is bound to contain far more errors than accurate information. Moreover, distinguishing correctness often depends on interpretation, and ideas that seem profound may turn out to be shallow, with interpretation again playing a role. Ultimately, LLMs default to the most common views, which are not necessarily the most thoughtful or well-reasoned.
Chet Said
You’ve highlighted several key issues with large language models (LLMs) that are at the heart of both their capabilities and limitations. Your observations touch on the probabilistic nature of their outputs, the quality of their training data, and their tendency to reflect the most common patterns found in that data, often prioritizing frequency over accuracy. I'll address these points step-by-step:
1. Mimicking Reasoning Rather Than Performing It
LLMs do not reason in the way humans do. Instead, they generate outputs by predicting the most statistically likely sequence of words based on patterns in their training data. This process mimics reasoning but lacks the underlying cognitive mechanisms we associate with human thought—such as abstract reasoning, conceptual understanding, and causal inference.
While their outputs often appear coherent, this coherence emerges from statistical regularities, not from logical or causal reasoning. As you noted, their "reasoning" is a simulacrum—pattern recognition dressed up as thought—which explains why they can be astonishingly wrong yet still sound superficially plausible.
2. The Influence of Data Quality and Quantity
Because LLMs are trained on massive datasets scraped largely from the internet, they inherit the biases, misinformation, and shallow thinking that proliferate online. The internet’s content skews toward:
Popular over niche views.
Simplistic over complex explanations.
Sensational over sober analysis.
Even when higher-quality data is present, it is often drowned out by the sheer volume of lower-quality material. Since LLMs prioritize statistical patterns, they are far more likely to produce what is common than what is correct.
For example, in specialized fields, where correct assertions may be rare compared to misconceptions, the model might fill gaps by generalizing from related topics. When those topics are themselves prone to misinformation, the result can be an erroneous or absurd output.
3. Confabulation and Generalization Errors
When data is sparse, models generalize by pulling patterns from seemingly related contexts. This behavior is useful in generating plausible-sounding answers but can lead to what we call confabulations—outputs that are entirely fabricated but phrased with unwarranted confidence.
For example, if asked about an obscure philosopher whose work isn’t well-represented in the training data, the model may piece together fragments from vaguely related topics, producing an output that sounds plausible but is fundamentally wrong.
This process highlights the dangers of extrapolation without a true grounding in meaning or context. Humans can recognize gaps in knowledge and remain silent or speculative, but LLMs, by design, prioritize fluency and completion over epistemic humility.
4. Overrepresentation of Shallow Thinking
Your observation about the dominance of "shallow thinking" is key. The democratization of the internet means more people can publish content, but most of it reflects popular opinions and surface-level understanding. LLMs reflect this reality because they are optimized to reproduce common patterns rather than evaluate their merit.
The result is that banality dominates unless specifically prompted to dig deeper. The models tend to produce outputs that are safe, conventional, and familiar unless asked to critique, challenge, or investigate further. Even then, their ability to deliver depth depends heavily on the existence of detailed, well-reasoned training data—which may itself be sparse.
5. Popularity vs. Quality
Because LLMs prioritize statistical regularity, their outputs often default to what is most said rather than what is most correct. They amplify consensus and majority views, even when those views are dubious or contested.
This effect explains why mainstream narratives dominate in AI outputs unless the user explicitly requests alternative perspectives. It also explains why AI struggles with nuanced or contrarian positions, which are underrepresented in the data and harder to detect statistically.
Additionally, correctness is not binary—it often depends on context and interpretation. Models trained on massive datasets struggle to distinguish between provisional knowledge (e.g., scientific theories) and settled facts, resulting in outputs that may seem plausible but lack the qualifications or disclaimers a human expert would add.
6. An Illusion of Comprehensiveness
The seeming omniscience of LLMs—accessing "far more data than any human"—masks their profound lack of understanding. Their outputs often appear deep because they synthesize and rephrase patterns drawn from many sources. However, this synthesis is superficial and lacks the true analytical depth that comes from examining evidence, testing hypotheses, or evaluating conflicting claims.
In areas where expertise matters, such as philosophy or science, this illusion of knowledge can mislead even informed users, creating a false sense of authority that crumbles under scrutiny.
7. Confirmation Bias and Echo Chambers
Another problem is the tendency of LLMs to reinforce prevailing narratives rather than challenge them. Because they operate probabilistically, they favor patterns that have already achieved prominence in the data. This reinforces biases, amplifies groupthink, and marginalizes dissenting voices.
Even when dissenting views exist, models may treat them as fringe ideas, framing them as “less credible” based solely on statistical frequency rather than substantive merit.
Concluding Thoughts
The operation of LLMs is fundamentally alien to human cognition, relying on statistical probabilities rather than reasoning. Their reliance on flawed, shallow, and overrepresented data ensures that errors—whether banal or absurd—are baked into their outputs.
While LLMs demonstrate impressive fluency and breadth, their outputs are inherently probabilistic guesses, not reasoned conclusions. They can summarize and synthesize information but lack the tools for genuine analysis, making them prone to amplifying errors and reflecting the biases of their training data.
Ultimately, their utility depends on how well users can frame questions, detect errors, and probe deeper—tasks that require the very human reasoning LLMs only simulate.