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
Learning language representations is a key component in many natural language processing tasks, and their usefulness is most often challenged by specialized target domains and vocabulary. We have witnessed several neural causal language models (CLM) that learn contextual representations such as ELMo [8]. More recently, the Transformer architecture [10] has tremendously improved language representation learning, giving birth to new architectures such as BERT [4], a masked language model, pushing the state-of-the-art of natural language understanding to an unprecedented level of performance on standard benchmarks. Moreover, it has been found that Transformer-based CLM, such as GPT [9], are excellent feature extractors as well as being impressive text generators. BART [7], an architecture combining the backbone of both BERT and GTP proved to be particularly effective at generating text while being competitive in comprehension tasks. BARThez, the French version of BART, was recently introduced as a pre-trained model on a very large monolingual French corpus [6]. In this paper, we introduce CriminelBART, a fine-tuned version of BARThez specialized for criminal law using a French Canadian corpus of legal judgments, and we evaluate its performance on different tasks.
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