Conditional Question Generation Model Based on Diffusion Model
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
The ultimate goal of conditional question generation is to generate high-quality questions with diversity, and the classifier-based diffusion model for conditional problem generation mainly categorizes the source data through classifiers so that high-quality problems can be generated. However, this kind of generation method has the drawback of complex joint training process and over-dependence on labeled data, which can lead to the lack of diversity and quality of generation. To tackle this problem, we propose a novel classifier-free diffusion model for conditional question generation. First, discrete text data are mapped into continuous vector data as input of the model in terms of an embedding function. Second, we design a classifier-free training method, which embeds the condition into the data fitting process, and the vector data completes the training under the condition. Finally, with the aid of rounding function, the samples generate the discrete text problem data. Experiments show that our proposed approach achieves a relatively decent average score and realizes better problem diversity than other state-of-the-art methods.
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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 | 0.001 | 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 | 0.001 |
| 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