Management and Performance Program Chatbot: A Use Case of Large Language Model in the Federal Public Sector in Brazil
Bibliographic record
Abstract
Governments are facing the benefits and perils of recent developments in artificial intelligence. One key objective is using AI to enhance the efficiency and effectiveness of their public service provision. While academic research has examined these advances, there is a lack of empirical evidence regarding user experiences. This paper explores the initial results of using a chatbot based on a Large Language Model (LLM) to address user queries about the Management and Performance Program (PGD). Survey analysis demonstrated that ChatPGD is likely to reduce program-related response time and increase effectiveness. However, the initial tests indicated a need to refine response algorithms to detect and mitigate potential biases and to strengthen the system infrastructure to support new enhancements. This paper examines the chatbot's application in harnessing government databases, highlighting the potential benefits, challenges, and perspectives of embedding AI and LLMs in public service delivery. The insights can support the development of other public service LLMs.
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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 | 0.004 | 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 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".