HouseOfTheDragonQA: Open-Domain Long-form Context-Aware QA Pairs for TV Series
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a novel approach to develop an open-domain and long-form Over-The-Top (OTT) Question-Answering (QA) dataset, HouseOfTheDragonQA, specifically oriented to the “House of the Dragon” TV series. Most of the existing QA datasets have focused on short, fact-based answers sourced almost solely from Wikipedia articles-bereft of the depth and contextual richness required for sophisticated narrative understanding. Our dataset is curated using legally admissible and high-quality open-domain sources to combine full episode summaries from HBO and fandom wiki websites, user reviews from IMDb and Rotten Tomatoes, and structured data from repositories such as WikiData. The dataset provides a multidimensional context, capturing complex character dynamics and plot developments from these varied sources. On equal terms, rigorous data preprocessing and filtering methods ensure that only meaningful and non-spam, unbiased reviews will be present in this enhanced dataset. The long-form answers generated from this enriched context provide comprehensive insights, making this dataset particularly valuable for improving conversational AI, narrative analysis, sentiment analysis, summarization techniques, and relation extraction. Comparative analysis with state-of-the-art QA datasets like SQuAD 2.0, TriviaQA, and Natural Questions (NQ) demonstrates the unique advantages of our dataset in terms of contextual complexity and answer length. The inclusion of detailed reviews offers added layers of audience sentiment and narrative interpretation, setting a new benchmark for quality in domain-specific QA tasks. Our effort enables advanced comprehension of entertainment-industry content and paves the way for more knowing and creative AI-driven interactions within digital media settings.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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