| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Tweet Paraphrasing/Generation | LaMP Tweet | ROUGE-142.2 | 36 | |
| News Headline Generation | LaMP News | RG118.8 | 36 | |
| Personalized Scholarly Refinement | LaMP-5 | Rouge-L40.5 | 32 | |
| Scholarly Abstract Generation | LaMP Scholar | ROUGE-144.6 | 24 | |
| Product Rating Prediction | LaMP Rating | MAE0.236 | 24 | |
| Movie Recommendation | LaMP Movie | Accuracy57 | 24 | |
| Citation Recommendation | LaMP Citation | Accuracy73.8 | 24 | |
| LaMP-3 Personalization | LaMP-3 (val) | MAE0.231 | 23 | |
| Personalization | LaMP-2 | Acc67.9 | 22 | |
| Scholarly Title Generation | LaMP Scholarly Title Generation | ROUGE-143.75 | 21 | |
| Personalization | LaMP-3 | MAE0.241 | 21 | |
| LaMP-1 Personalization | LaMP-1 (val) | Accuracy68.2 | 21 | |
| Scholarly Title Generation | LaMP-5 1.0 (test) | ROUGE-10.483 | 17 | |
| Categorical classification | LaMP-2 (test) | Accuracy66.1 | 16 | |
| Personalized Classification | LaMP-2 (val) | Accuracy53.1 | 14 | |
| Maximum Inner Product Search | LaMP Rating (test) | Top-1 Accuracy100 | 14 | |
| Maximum Inner Product Search | LaMP Movie (test) | Top-1 Accuracy100 | 14 | |
| Personalized Text Generation | LaMP-7 (val) | ROUGE-1 Score53.5 | 13 | |
| Multi-Objective Bayesian Optimization | LAMP | Hypervolume0.648 | 10 | |
| Personalized Question Answering | LaMP-QA | Accuracy (Arts & Entertainment)53.27 | 10 | |
| News Headline | LaMP News Headline | ROUGE-1 Score21.5 | 9 | |
| Topic Writing | LaMP Topic Writing | ROUGE-10.291 | 9 | |
| Review Writing | LaMP Review Writing | ROUGE-135.3 | 9 | |
| Abstract Generation | LaMP Abstract Gen. | ROUGE-139.8 | 9 | |
| News Headline Generation | LaMP News Headline (test) | ROUGE-115.1 | 9 |