Effective Neural Team Formation via Negative Samples
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
Forming teams of experts who collectively hold a set of required skills and can successfully cooperate is challenging due to the vast pool of feasible candidates with diverse backgrounds, skills, and personalities. Neural models have been proposed to address scalability while maintaining efficacy by learning the distributions of experts and skills from successful teams in the past in order to recommend future teams. However, such models are prone to overfitting when training data suffers from a long-tailed distribution, i.e., few experts have most of the successful collaborations, and the majority has participated sparingly. In this paper, we present an optimization objective that leverages both successful and virtually unsuccessful teams to overcome the long-tailed distribution problem. We propose three negative sampling heuristics that can be seamlessly employed during the training of neural models. We study the synergistic effects of negative samples on the performance of neural models compared to lack thereof on two large-scale benchmark datasets of computer science publications and movies, respectively. Our experiments show that neural models that take unsuccessful teams (negative samples) into account are more efficient and effective in training and inference, respectively.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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