Personalizing Chatbot Conversations with IBM Watson
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
Systems powered by artificial intelligence are being developed to communicate with users in a progressively “human-like” conversational way, in order to make them more user-friendly. Such systems are utilized across many industries including teaching, marketing, and health care, and are commonly made available to the public as interactive chatbots. It is important to explore new possibilities in development to make these systems more personalized to their users by improving and expanding their functionality and interactivity. This project delves further into this topic by creating a system that generates increasingly customized responses to user input. One crucial way to improve the functionality of an artificial intelligence system is by molding a personal profile of the user, which can be referenced by the system in order to respond to the user’s needs in an adaptive way based on their preferences. The project is focused on investigating packages that can be used to more effectively respond to the user’s mood, personality, and language, including IBM Watson Tone Analyzer, Watson Personality Insights, and Watson Language Translator. These packages are then utilized to work towards creating an intelligent, interactive system that can effectively fulfill the individual needs of its users.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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