On Characterizations of Large Language Models and Creativity Evaluation
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
Incredible as they may be, Large Language Models (LLMs) have their limitations. While they generate high-quality texts, excel at stylistic reproduction, and tap into an immense pool of information, they can produce wildly inaccurate responses. The hype around LLMs led to them being characterized as "reasoning", "sentient", or "knowing" like humans. We examine these characterizations and discuss what LLMs can't do and what they are surprisingly good at. LLMs are still susceptible to traditional issues with AI, probabilities are not knowledge, and they are not in the world. Nonetheless, LLMs, despite not being human, have great potential to perform various creative tasks. We conclude that LLMs are beyond "mere generation" and perceivable as creative, but we may need to reassess some frameworks for creativity evaluation.
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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