Rule based chatbot design methods: A review
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 use of chatbots in various sectors including the health sector is becoming important. Rule based chatbots are one of the commonly used chatbots which is easier to implement and with less error. For example, in assisting by providing preliminary diagnostics to the youth and advising them to provide appropriate medical care and awareness creation. In resource limited environment where there is a shortage of medical experts as well as other resources, rule based chatbot can be a supportive tool to support patients and health workers. In addition, in infections like sexually transmitted infections, having an anonymous chatbot is ideal for supporting the youth who fear to openly visit health centers due to stigma and discrimination. Hence, a rule based chatbot can be designed to support them in providing preliminary diagnostics, advising them to visit health centers as well as creating awareness from reliable sources. Hence, this paper, reviews key rule based chatbot design approaches, their advantages and disadvantages.
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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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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