Towards Reliable and Efficient Natural Language Processing in Emergent Technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
LLMs have created new possibilities for machines to learn from data and produce content without human intervention. We address the question of whether they provide reliable and efficient Natural Language Processing. We differentiate Generative Grammar Language Models from Large Language Models. We take Structure Dependency to be a first principle of language, a prerequisite to Semantic Compositionality, applying in conjunction with Principles of Efficient Computation to generate structured content. We discuss the behavior of ChatGPT-3.5, GPT-4.o mini and Grok 3 in response to complex queries including pronominal Binding, Coreference, and structural ambiguity and point to the relevance of deep syntactic and semantic principles for LLMs. We outline consequences for the development of reliable and efficient Natural Language Processing systems in emergent technologies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it