Movies
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Node Classification | Movies | Accuracy65.74 | 139 | |
| Entity Matching | Movies | F1 Score90.2 | 26 | |
| Recommendation | Movies | HR@100.17 | 15 | |
| Sequential Recommendation | Movies | Recall24.59 | 14 | |
| Node Classification | Movies MAGB | Accuracy53.98 | 13 | |
| Node Clustering | Movies | NMI56.53 | 12 | |
| Link Prediction | Movies | Accuracy (%)92.45 | 11 | |
| Uncertainty Estimation | Movies | AUROC80.14 | 10 | |
| Hallucination Detection | Movies (test) | AuROC94.15 | 10 | |
| Recommendation | Movies | NDCG0.0777 | 8 | |
| Rationale Extraction | Movies | Accuracy82.9 | 7 | |
| Recommendation | Movies | Recall@1012.31 | 7 | |
| Entity Resolution | Movies | FP31.31 | 6 | |
| Uncertainty Estimation | Movies (test) | AUPRC (pp)66.56 | 6 | |
| Speaker-Attributed Automatic Speech Recognition | Movies (test) | CER7.5 | 6 | |
| Error Detection | Movies | Precision0.934 | 6 | |
| Text Classification | Movies ERASER (test) | Task Weighted Avg F191 | 5 | |
| Link Prediction | Movies | MRR0.6242 | 3 | |
| Rationale Explanation | Movies Human Evaluation (Ranking) | Rationale Quality (Rank-1)275 | 3 | |
| Multi-speaker Transcription | Movies internal (test) | DER23.56 | 3 | |
| Entity Resolution | Movies (1:9) | FP Rate42.17 | 3 | |
| Sentiment Analysis | Movies (Out-of-Distribution) | Accuracy94.5 | 3 | |
| Link Prediction | Movies (test) | Accuracy42.4 | 2 | |
| Entity Resolution | Movies | End-to-end running time (min)67.68 | 2 |
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