Chatbots as assistants: an architectural framework
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
Automated text-based or speech-based personal assistants, also known as chatbots, have been prevalent in several domains including marketing and technical support. Through mainstream applications, such as Siri or Alexa, their popularity has increased and we now see them being used in even more domains. Although the purpose of chatbots varies among domains, there are common elements that all chatbots share. By identifying these elements, it is possible to streamline the development of chatbots en masse and in a structured manner. Additionally, there can be common challenges in the development of such applications, for example, how to treat novice versus expert users or how to establish memory of the conversation. In this work, we propose a reference architecture for chatbots using concepts from Software Product Lines and Feature Models, where we outline the common elements as well as the common challenges. Using Watson and Bluemix as the basic platforms, we also present the creation of two chatbots, for different purposes, based on this reference architecture to highlight these commonalities.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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