Timeline Extraction from Decision Letters Using ChatGPT
Bibliographic record
Abstract
Freedom of Information Act (FOIA) legislation grants citizens the right to request information from various levels of the government, and aims to promote the transparency of governmental agencies.However, the processing of these requests is often met with delays, due to the inherent complexity of gathering the required documents.To obtain accurate estimates of the processing times of requests, and to identify bottlenecks in the process, this research proposes a pipeline to automatically extract these timelines from decision letters of Dutch FOIA requests.These decision letters are responses to requests, and contain an overview of the process, including when the request was received, and possible communication between the requester and the relevant agency.The proposed pipeline can extract dates with an accuracy of .94,extract event phrases with a mean ROUGE-L F1 score of .80 and can classify events with a macro F1 score of .79.Out of the 50 decision letters used for testing (each letter containing one timeline), the model correctly classified 10 of the timelines completely correct, with an average of 3.1 mistakes per decision letter.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".