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Over the past few years, natural language processing has been declared "solved" more than once. Large language models now write code, pass professional exams, and generate prose that is often indistinguishable from human output. These accomplishments are real and deserve recognition. Yet beneath the impressive demonstrations and benchmark gains lies a growing discomfort. Language models are improving rapidly, while our understanding of language itself is not advancing at the same pace. In the rush toward scale, linguistic grounding, the foundation on which NLP was originally built, has quietly slipped out of focus.

Scale as a Shortcut

Scale became dominant because it produced visible progress without requiring deeper engagement with the underlying problem of how a language model interprets a prompt. Increasing parameter counts and training data yielded rapid gains on benchmarks and demonstrations, creating the appearance of linguistic competence while sidestepping questions of representation, meaning, and structure. This approach was attractive not only because it worked, but because it worked quickly.

In this sense, the neglect of classical NLP has not merely coincided with the rise of prompt engineering. It has actively enabled it. Prompt engineering functions as a compensatory practice, shifting the burden of linguistic precision from the model to the user. When a system lacks grounded language understanding, users are required to carefully shape inputs to coax desired behavior. The sophistication of prompts is often mistaken for model intelligence, masking the absence of deeper linguistic competence. Concerns about this form of progress through scale alone have been raised explicitly in prior work on large language models and their limitations (Bender et al., 2021).

Why the Turn Toward Scale Felt Reasonable

To be clear, the move toward scale was not irrational. Advances in compute availability, self supervised learning, and transfer learning delivered genuine breakthroughs. Models trained on massive corpora demonstrated behaviors that challenged earlier assumptions about representation and generalization. For a time, it appeared plausible that language understanding might simply emerge as a byproduct of scale.

Institutional incentives reinforced this trajectory. Benchmarks, leaderboards, funding mechanisms, and publication norms increasingly rewarded performance gains that correlated strongly with model size. As a result, scale became both the dominant research strategy and the primary signal of progress. Questions that did not immediately translate into benchmark improvements, such as interpretability, grounding, or linguistic structure, were deprioritized.

What Classical NLP Confronted That Modern Models Often Avoid

Classical NLP forced researchers to make assumptions explicit. Syntax, semantics, and pragmatics were not abstract ideals but operational constraints. Models failed in visible and interpretable ways, inviting analysis rather than celebration. Errors were signals rather than inconveniences.

By contrast, contemporary language models often exhibit surface fluency that conceals fragility. They generate plausible text even when internal representations are misaligned with meaning or intent. This distinction between linguistic form and linguistic understanding has been well articulated in prior work, which cautions against conflating fluent output with genuine language comprehension (Bender & Koller, 2020). The result is a system that appears linguistically capable while remaining fundamentally ungrounded.

The Cost of Ungrounded Language Models

The limitations of this approach become most apparent in extended or high stakes interactions. Hallucinations, inconsistency, and prompt sensitivity are not incidental flaws. They are structural consequences of ungrounded language modeling.

A brief technical clarification helps explain why these failures are not anomalous. Large language models generate text by sampling from conditional probability distributions, chaining together sequences of probabilistic decisions. Because each conditional probability is less than one, uncertainty compounds over long generations. As outputs grow longer, small errors accumulate, increasing the likelihood of drift, inconsistency, or outright inaccuracy. Empirical studies of neural text generation have shown that such degeneration is a predictable consequence of probabilistic sequence modeling rather than an implementation flaw (Holtzman et al., 2020).

This gap exposes a mismatch between how language models are evaluated and how they are used. Broad benchmarks reward aggregate performance but rarely capture reliability, consistency, or failure transparency within specific operational contexts.

Re-centering NLP Without Rejecting LLMs

Reintroducing linguistic grounding does not require abandoning large language models. It requires reconsidering how success is defined and measured. Instead of treating all language models as general purpose linguists, evaluation should emphasize practical performance metrics aligned with a system's intended role.

Such metrics might prioritize efficiency, reliability, interpretability, constraint adherence, or controlled failure behavior over generalized benchmark dominance. In this framing, linguistic grounding becomes a measurable engineering requirement rather than an abstract philosophical concern. This approach aligns more closely with how complex systems are designed, deployed, and trusted in practice.

Speed and the Erosion of Rhetoric

The speed and fluency of large language models introduce a more subtle consequence, one that extends beyond technical performance into the domain of rhetoric itself. When responses are generated instantly and at scale, the precision of linguistic form becomes increasingly important. A poorly structured interrogative, an imprecise modifier, or an unintended presupposition can subtly alter meaning or imply intentions that were never present. In slower, more deliberative modes of communication, such rhetorical missteps are often noticed and corrected. In high speed language generation, they propagate unchecked.

This marks a critical juncture not only for NLP research, but for how language is used, interpreted, and trusted. When linguistic form is treated as a surface detail rather than a foundational structure, both human and machine communication become more vulnerable to misinterpretation. The result is not merely technical inaccuracy, but rhetorical drift, where meaning shifts not through argument or intent, but through speed.

What We Trade for Speed

The trajectory of modern NLP invites a broader question that extends beyond artificial intelligence alone. In prioritizing speed, scale, and instant gratification, it is worth asking what is being traded away in the process. Reaching a destination faster is not inherently progress if it comes at the cost of accuracy, understanding, or reliability.

Real learning, whether in humans or machines, emerges through constraint, error, and sustained engagement with complexity. When struggle is engineered out in favor of immediate fluency, the result may appear impressive in the short term, but it risks hollowing out the foundations that make learning meaningful. If this trade off remains unexamined, the consequences will extend beyond technical systems and into how society values understanding itself.

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://doi.org/10.1145/3442188.3445922

Bender, E. M., & Koller, A. (2020). Climbing towards NLU: On meaning, form, and understanding in the age of data. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 5185–5198. https://doi.org/10.18653/v1/2020.acl-main.463

Holtzman, A., Buys, J., Du, L., Forbes, M., & Choi, Y. (2020). The curious case of neural text degeneration. International Conference on Learning Representations.

I keep returning to the idea that Large Language Model (LLM) is a misleading name—not because it's technically incorrect, but because it leaves out the most important parts of what these systems actually are and how they function.

The term emphasizes scale while obscuring discipline, constraints, and intent. In doing so, it encourages a kind of magical thinking about AI that shows up in classrooms, boardrooms, and product roadmaps alike.

"Large" Is Not a Capability

Calling something "large" feels explanatory, but it really isn't. Size is an implementation detail, not a defining property. A 3B-parameter model and a 70B-parameter model are performing the same fundamental operation: probabilistic next-token prediction conditioned on context.

One may generalize better, hallucinate less, or tolerate messier prompts—but neither suddenly develops understanding.

Yet large carries cognitive baggage. Bigger feels smarter. Bigger feels closer to human intelligence. Bigger feels inevitable. This framing distracts us from what actually matters: training regimes, data quality, conditioning, alignment methods, and deployment constraints. Those are the levers that determine usefulness—not raw parameter counts.

The Disappearance of NLP

More concerning is what the term LLM quietly erases: Natural Language Processing.

LLMs are not post-NLP systems. They are the current culmination of decades of NLP research: tokenization and subword modeling, distributional semantics and embeddings, syntax and semantic structure, pragmatics via context windows, and discourse coherence via attention.

None of this disappeared. It was absorbed, abstracted, and hidden behind the transformer architecture.

This matters—especially in education. When we teach "LLMs" without explicitly grounding them in NLP, we produce users who can prompt but cannot reason about failure modes, bias, ambiguity, or linguistic structure. They learn how to use the system, not how to understand its behavior.

That's not progress. It's technical illiteracy with a friendly interface.

The Illusion of Generality

The phrase Large Language Model also suggests a level of general intelligence that simply isn't there.

In practice, these systems are task-conditioned inference engines. Every apparent capability is shaped by prompt structure, instruction tuning, reinforcement signals (RLHF/RLAIF), tooling and retrieval layers, and context window limits.

What looks like reasoning is better described as conditional pattern completion across tasks. The model does not decide what to do; humans encode the task into the prompt or system scaffolding.

LLMs work precisely because humans do the hard part: framing problems into consumable task representations.

Naming Shapes Thinking

This is ultimately a framing problem.

When people hear LLM, they infer understanding, intent, and cognitive depth. When they hear NLP system or task-conditioned model, they expect probabilistic behavior, failure cases, and the need for oversight.

That difference matters.

It shows up when organizations deploy chatbots as decision-makers instead of decision-support tools. It shows up when students outsource thinking instead of augmenting it. It shows up when leaders assume scale alone will solve correctness, alignment, or safety.

The technology didn't create these misunderstandings. The naming did.

What Should We Call Them?

If we were being academically honest, we might prefer terms like task-conditioned neural NLP models, instruction-tuned language models, token-level predictive systems, or contextual inference engines.

None of these are particularly marketable. But they are accurate—and accuracy matters, especially in education and research.

Tools, Not Thinkers

This perspective aligns closely with what I think of as an augmented craftsman approach to software and AI. LLMs are not replacements for judgment; they are amplifiers of intent. They excel when used deliberately, constrained carefully, and understood deeply.

Poor naming encourages overtrust. Good framing encourages skill.

LLMs don't understand language. They operate on it. They don't reason. They approximate reasoning patterns. And they don't replace expertise—they reveal whether it was there to begin with.

Calling them Large Language Models makes them sound like something they are not. Until we fix how we talk about them, we'll continue to misunderstand how—and when—they should be used.

But there is a problem with that intuition.

The Question

In a recent follow-up study, I revisited a simple question: what actually happens when you increase training token count while holding everything else constant—same model, same hardware, same optimizer, same number of epochs. And crucially, what happens when you measure energy directly instead of inferring cost indirectly.

The answer is uncomfortable. Training efficiency collapses long before performance meaningfully improves.

Experimental Design

To isolate the effect of token count, the experiment was intentionally constrained. A fixed 1.1 billion parameter TinyLlama model was trained on identical GPU hardware, an AWS ml.g5.xlarge instance with an NVIDIA A10G GPU. The optimizer, learning rate, precision mode, and number of training epochs were held constant. No schedulers, no fine-tuning tricks, and no adaptive behaviors were introduced.

The only variable that changed was training token count. Three conditions were evaluated: 500 thousand tokens, 1 million tokens, and 2 million tokens. Each condition was repeated 50 times, yielding 150 independent training runs. This repetition is important, because many scaling studies rely on single runs or heterogeneous infrastructure, making it difficult to disentangle true effects from noise.

What Was Measured

Most scaling studies focus on performance metrics such as loss or perplexity. Those are important, but they tell only half the story. This study measured three things directly: model behavior using inverse perplexity, wall-clock execution time captured through runtime and sampling counts, and actual GPU power consumption recorded in watts using NVIDIA's management library.

Power was not estimated or inferred. It was measured every 60 seconds during training and aggregated using a root mean square formulation to reflect sustained energy usage. From these measurements, an energy-aware parameter efficiency metric was computed. The question this metric answers is simple: how much useful model behavior do we get per unit of compute and energy.

The Results: Efficiency Collapses

The results were stark. As token count increased, efficiency did not merely decline—it collapsed monotonically. Moving from 500 thousand to 1 million tokens reduced efficiency substantially. Moving from 1 million to 2 million tokens reduced it even further. Every step up in token count produced a statistically distinct outcome.

At the same time, model performance barely moved. Inverse perplexity changed modestly across conditions. The model did not get worse, but it did not get meaningfully better either. What did change dramatically was cost.

Power Consumption: The Physical Reality

On a hunch, I ran a second analysis that ignored model performance entirely. Instead, I asked a simpler question: does training token count alone affect sustained GPU power consumption.

The answer was unambiguous. Power usage increased sharply and monotonically with token count. A separate repeated measures analysis of variance on raw RMS power consumption showed an extremely large effect, even though the hardware, software, and training configuration never changed.

This is the key insight. The efficiency decline is not a quirk of a composite metric. It is driven by physical energy demand. Token count directly increases runtime and sustained power draw. Performance gains do not keep up.

Convergent Evidence

Two independent analyses, one on energy-aware efficiency and one on raw power consumption, converged on the same conclusion. That convergence matters. It shows that the result is not an artifact of metric construction or statistical modeling. It is a property of the system itself.

What This Means for Practitioners

This does not mean scaling is wrong. It means scaling without cost awareness is incomplete. If you are operating at frontier scale with effectively unlimited resources, the trade-off may be acceptable. But for researchers, startups, educators, or anyone outside massive industrial labs, the results are sobering. Doubling training tokens roughly doubles runtime. Energy consumption rises predictably. Efficiency drops even when accuracy does not meaningfully improve.

More tokens often buy you less than you think, at a much higher cost than you realize.

The Path Forward

Scaling laws taught us how performance grows. This study shows how efficiency collapses when energy is treated as a first-class variable. The implication is not to stop scaling. It is to measure it honestly. If we want sustainable and accessible machine learning, we cannot evaluate training strategies using performance metrics alone. We need to ask not just what works, but what it costs to work.

Because the GPU always keeps the receipts.

The rapid growth of large language models has transformed AI but introduced immense compute costs and environmental impact. Linear scaling of datasets often yields diminishing returns, pricing out smaller teams and independent researchers.

This quantitative study tested whether training token count influences parameter efficiency while holding model size constant. Using scaling laws as a guide, the goal was to find token-to-parameter ratios that lower compute demands and broaden accessibility.

Methodology

TinyLlama (1.1B parameters) was trained under three token conditions: 500,000; 1,000,000; and 2,000,000 tokens. Training ran on an AWS SageMaker notebook to mirror a constrained edge environment. Repeated measures ANOVA with Bonferroni corrections assessed differences.

Results

Token count showed a significant effect on parameter efficiency, F(2, 98) = 77.3166, p < .001, η² = .5268. The 2,000,000-token condition differed from both 500,000 and 1,000,000, though its average efficiency was lower—suggesting a non-linear relationship.

Implications

Tuning token-to-parameter ratios could cut compute needs and open participation to smaller organizations. Thoughtful scaling may also reduce the carbon footprint of training by meaningful margins, making research more sustainable and inclusive.

Next Steps

Future work could probe additional token intervals and energy metrics to refine efficiency strategies. Collaboration across the community can accelerate practical recipes that balance capability, cost, and environmental impact.

In a fast-paced AI landscape, the yardsticks we use shape what we build. Statistical significance has long been a go-to for judging results, but it can miss practical impact. Accuracy alone can also obscure how models behave in real-world settings.

The Limitations of Traditional Metrics

Statistical significance helps confirm that effects are real, yet it says little about usefulness. A 5% lift might mean one extra correct prediction out of 1,000—hardly transformative. Accuracy, precision, and recall also give snapshots that can miss demographic nuances or shifting data.

Efficiency and Perplexity: A Partial Picture

Throughput, latency, and perplexity are valuable, but narrow. A chatbot can be fast and low-perplexity while still misunderstanding intent—user satisfaction may lag far behind headline scores.

Introducing New Metrics

Visualizations: A Powerful Tool

Distribution plots reveal variance across datasets and models; confidence intervals show stability; effect sizes highlight practical differences beyond significance. Together, they surface trends hidden by single-number summaries.

The Importance of Context

Metrics must travel with the model. A score earned in a controlled lab can collapse in production. Evaluations should reflect deployment environments—data drift, demographic shifts, latency limits, and safety constraints.

Moving Forward with Metrics

Broadening beyond accuracy and p-values helps align AI with real-world value. Blending energy-aware metrics, task-specific scores, and clear visualizations yields a fuller picture of impact, guiding models that are not just statistically sound but genuinely useful.

In fast-paced ML work, understanding how training token counts influence parameter efficiency can materially shift outcomes. This post summarizes findings from 150 trials that varied token counts and applied repeated measures ANOVA to uncover practical guidance.

The study leveraged the script analyze_results.py (Dwyer, 2025) with common Python libraries to process results. Repeated measures ANOVA highlighted whether efficiency differed across token settings—well-suited for within-subject comparisons (Kraska, 2010).

Key Findings

Training token count significantly affected parameter efficiency: F(2, 98) = 77.3166, p < .001, η² = .5268. Pairwise comparisons with Bonferroni correction showed 2M tokens underperformed 500K (t = 12.9365, p < .001) and 1M (t = 9.0908, p < .001), while 500K vs. 1M was not significant (t = 1.9377, p = .1753).

This suggests a threshold where more tokens stop helping—and can even hurt—making it vital to tune token budgets rather than scale blindly.

Sphericity and Corrections

Mauchly’s test could not yield stable covariance estimates, prompting a conservative Greenhouse–Geisser correction (ε ≈ .964) instead of Huynh–Feldt to control Type I error risk. No extreme outliers in efficiency or perplexity were observed, bolstering confidence in the conclusions.

Final Thoughts

Careful token-count tuning, paired with robust statistics, can improve efficiency without runaway compute. As models and datasets grow, staying alert to diminishing returns will keep training strategies both effective and sustainable.