Scalable generation of texts using causal and temporal expansions of sentences
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
This paper presents a exible bottom-up process to incrementally generate several versions of the same text, building up the core text from its kernel version into other versions varying of the levels of details. We devise a method for identifying the question/answer relations holding between the propositions of a text, we give rules for characterizing the kernel version of a text, and we provide a procedure, based on causal and temporal expansions of sentences, which distinguishes semantically these levels of details according to their importance. This is based on the assumption that we have a stock of information from the interpreter's knowledge base available. The sentence expansion operation is formally defined according to three principles: (1) the kernel principle ensures to obtain the gist information; (2) the expansion principle defines an incremental augmentation of a text; and (3) the subsume principle defines an importance-based order among the possible details of the information. The system developed allows users to generate in a follow-up way their own text version which meets their expectations and their demands expressed as questions about the text under consideration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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