Performance Prediction on WMT En-De 2019 (val)
0.29MAELLM-PP
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| LLM-PPBackbone=GPT-4, Ablation=+ Third instruction2023.10 | 0.29 | 0.67 | |
| LLM-PPBackbone=GPT-42023.10 | 0.32 | 0.65 | |
| LLM-PPBackbone=GPT-4, Ablation=+ Role + Hyp.2023.10 | 0.32 | 0.67 | |
| LLM-PPBackbone=GPT-4, Ablation=+ Fifth instruction2023.10 | 0.32 | 0.65 | |
| LLM-PPBackbone=GPT-4, Ablation=+ Fourth instruction2023.10 | 0.33 | 0.71 | |
| LLM-PPBackbone=GPT-4, Ablation=Demonstrations only2023.10 | 0.34 | 0.61 | |
| LLM-PPBackbone=GPT-4, Ablation=+ First instruction2023.10 | 0.34 | 0.58 | |
| LLM-PPBackbone=GPT-4, Ablation=+ Second instruction2023.10 | 0.35 | 0.66 | |
| LLM-Distill-PPTeacher Backbone=GPT-42023.10 | 0.38 | 0.68 | |
| LLM-PPBackbone=ChatGPT2023.10 | 0.72 | 0.56 | |
| Neuron-wise MoSType=Baseline2023.10 | 0.87 | 0.67 | |
| HATType=Baseline2023.10 | 0.91 | 0.72 | |
| Supernet (Sandwich)Type=Baseline2023.10 | 0.91 | 0.72 | |
| LLM-PPBackbone=Mistral2023.10 | 0.92 | 0.18 | |
| LLM-Distill-PPTeacher Backbone=ChatGPT2023.10 | 0.95 | 0.65 | |
| Layer-wise MoSType=Baseline2023.10 | 0.96 | 0.74 |