Context-Aware Question-Answer for Interactive Media Experiences
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
Media content has become a primary source of information, entertainment, and even education. The ability to provide video content querying as well as interactive experiences is a new challenge. To this end, question answering (QA) systems such as Alexa and Google Assistant have become quite established in consumer markets but are limited to general information and lack context awareness. In this paper, we propose Context-QA, a light-weight context-aware QA framework, to provide QA experiences on multimedia content. The context awareness is achieved through our innovative Staged QA Controller algorithm that keeps the search for answers in the context most relevant to the question. Our evaluation results show that Context-QA improves the quality of the answers by up to 49% and uses up to 56% less time compared to the conventional QA model. Subjective tests show Context-QA improved results over conventional QA models, with 90% reporting enjoying this new media form.
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 | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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