Chart Question Answering on ChartQA (Accuracy, Rel. (%))
83AccuracyFull Model
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| Full ModelBackbone=Qwen2-VL-7B, Sparsity Ratio=0%2026.04 | 83 | 100 | |
| MaLoRAModel=Qwen2.5-VL-7B, Training examples=28.3k2025.10 | 80.84 | — | |
| LoRAModel=Qwen2.5-VL-7B, Training examples=28.3k2025.10 | 80.72 | — | |
| TopoVLMBackbone=Qwen2-VL-7B, Sparsity Ratio=50%2026.04 | 80.4 | 95.4 | |
| MaLoRAModel=Qwen3-VL-8B, Training examples=28.3k2025.10 | 80.24 | — | |
| LoRAModel=Qwen3-VL-8B, Training examples=28.3k2025.10 | 79.88 | — | |
| BaseModel=Qwen3-VL-8B, Training examples=28.3k2025.10 | 79.04 | — | |
| TAMPBackbone=Qwen2-VL-7B, Sparsity Ratio=50%2026.04 | 78.9 | 93.2 | |
| QLoRAModel=Qwen3-VL-8B, Training examples=28.3k2025.10 | 77.88 | — | |
| InternViT-2.5-6BBase LLM=Qwen2.5 - 7B, Size=6.0B, AnyRes=false, Discrete=false2026.06 | 77.4 | — | |
| LLM-StreamlineBackbone=Qwen2-VL-7B, Sparsity Ratio=50%2026.04 | 76.5 | 91.2 | |
| ECoFLaPBackbone=Qwen2-VL-7B, Sparsity Ratio=50%2026.04 | 75.2 | 90.5 | |
| SparseGPTBackbone=Qwen2-VL-7B, Sparsity Ratio=50%2026.04 | 74.3 | 90.1 | |
| WandaBackbone=Qwen2-VL-7B, Sparsity Ratio=50%2026.04 | 73.6 | 89.3 | |
| OWLBackbone=Qwen2-VL-7B, Sparsity Ratio=50%2026.04 | 72.8 | 88.4 | |
| SigLIP2-gBase LLM=Qwen2.5 - 7B, Size=1.1B, AnyRes=false, Discrete=false2026.06 | 72.8 | — | |
| ViQBase LLM=Qwen2.5 - 7B, Size=1.3B, AnyRes=true, Discrete=true2026.06 | 72.8 | — | |
| AIMv2-HBase LLM=Qwen2.5 - 7B, Size=0.7B, AnyRes=false, Discrete=false2026.06 | 72.5 | — | |
| LLM-PrunerBackbone=Qwen2-VL-7B, Sparsity Ratio=50%2026.04 | 72.4 | 88.6 | |
| OryxViTBase LLM=Qwen2.5 - 7B, Size=0.4B, AnyRes=true, Discrete=false2026.06 | 72.1 | — | |
| BaseModel=Qwen2.5-VL-7B, Training examples=28.3k2025.10 | 71.84 | — | |
| QLoRAModel=Qwen2.5-VL-7B, Training examples=28.3k2025.10 | 71.76 | — | |
| InternViT-2.5Base LLM=Qwen2.5 - 1.5B, Size=0.3B, AnyRes=false, Discrete=false2026.06 | 69.2 | — | |
| InternViT-2.5-6BBase LLM=Qwen2.5 - 1.5B, Size=6.0B, AnyRes=false, Discrete=false2026.06 | 67.8 | — | |
| ViQBase LLM=Qwen2.5 - 1.5B, Size=1.3B, AnyRes=true, Discrete=true2026.06 | 65.2 | — | |
| OAI-CLIP-LBase LLM=Qwen2.5 - 7B, Size=0.3B, AnyRes=false, Discrete=false2026.06 | 65.1 | — | |
| AIMv2-HBase LLM=Qwen2.5 - 1.5B, Size=0.7B, AnyRes=false, Discrete=false2026.06 | 62.5 | — | |
| OryxViTBase LLM=Qwen2.5 - 1.5B, Size=0.4B, AnyRes=true, Discrete=false2026.06 | 62.1 | — | |
| SigLIP2-gBase LLM=Qwen2.5 - 1.5B, Size=1.1B, AnyRes=false, Discrete=false2026.06 | 62 | — | |
| DinoV2-gBase LLM=Qwen2.5 - 1.5B, Size=1.1B, AnyRes=false, Discrete=false2026.06 | 61.8 | — | |
| OAI-CLIP-LBase LLM=Qwen2.5 - 1.5B, Size=0.3B, AnyRes=false, Discrete=false2026.06 | 55 | — | |
| UniTokBase LLM=Qwen2.5 - 1.5B, Size=0.3B, AnyRes=false, Discrete=true2026.06 | 43.8 | — | |
| MaLoRAModel=LLaVA-1.5-7B, Training examples=28.3k2025.10 | 20.28 | — | |
| LoRAModel=LLaVA-1.5-7B, Training examples=28.3k2025.10 | 19 | — | |
| QLoRAModel=LLaVA-1.5-7B, Training examples=28.3k2025.10 | 18.12 | — | |
| BaseModel=LLaVA-1.5-7B, Training examples=28.3k2025.10 | 14.36 | — | |
| QLIPBase LLM=Qwen2.5 - 1.5B, Size=0.3B, AnyRes=false, Discrete=true2026.06 | 14.1 | — |