A oclusão pode facilitar a compreensão humana? Avaliação de explicabilidade no reconhecimento de entidades nomeadas
Bibliographic record
Abstract
Técnicas de Explicabilidade são métodos que auxiliam usuários a entender os resultados de um modelo de aprendizado de máquina. Nesse contexto, este trabalho investiga se a técnica de explicabilidade de Oclusão consegue gerar respostas similares às esperadas por humanos na classificação de palavras para o Reconhecimento de Entidades Nomeadas. Para isso, utilizou-se uma LSTM bidirecional e o conjunto de dados CoNLL 2003, bem como foi utilizado a anotação manual de 849 sentenças criando-se, assim, uma base de dados de referência. Os resultados mostram que a Oclusão é capaz de indicar pelo menos uma palavra relevante e compatível com a compreensão humana.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.018 |
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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".