## Bar Chart: Model Accuracy Comparison Across Configurations ### Overview The image contains four grouped bar charts comparing the accuracy of bilinear (gray) and trilinear (blue) models across eight natural language processing tasks (SST-2, MRPC, RTE, STS-B, WNLI, QNLI, QQP, MNLI) under different computational configurations (1b/6b, 1b/7b, 2b/8b, 2b/9b). Each chart shows mean accuracy percentages with error bars representing standard deviation. ### Components/Axes - **X-axis**: NLP tasks (SST-2, MRPC, RTE, STS-B, WNLI, QNLI, QQP, MNLI) - **Y-axis**: Accuracy (%) from 50% to 90% - **Legend**: - Gray = Bilinear - Blue = Trilinear - **Configuration Labels**: Positioned at top-right of each chart (1b/6b, 1b/7b, 2b/8b, 2b/9b) ### Detailed Analysis #### 1b/6b Configuration - **SST-2**: Bilinear 88% (±2%), Trilinear 87% (±1%) - **MRPC**: Bilinear 85% (±3%), Trilinear 84% (±2%) - **RTE**: Bilinear 65% (±4%), Trilinear 62% (±3%) - **STS-B**: Bilinear 82% (±2%), Trilinear 81% (±1%) - **WNLI**: Bilinear 55% (±3%), Trilinear 55% (±2%) - **QNLI**: Bilinear 88% (±1%), Trilinear 87% (±2%) - **QQP**: Bilinear 83% (±2%), Trilinear 82% (±1%) - **MNLI**: Bilinear 70% (±4%), Trilinear 75% (±3%) #### 1b/7b Configuration - **SST-2**: Bilinear 89% (±1%), Trilinear 88% (±2%) - **MRPC**: Bilinear 86% (±2%), Trilinear 85% (±3%) - **RTE**: Bilinear 63% (±3%), Trilinear 60% (±2%) - **STS-B**: Bilinear 84% (±1%), Trilinear 83% (±2%) - **WNLI**: Bilinear 55% (±2%), Trilinear 55% (±1%) - **QNLI**: Bilinear 87% (±2%), Trilinear 86% (±3%) - **QQP**: Bilinear 85% (±1%), Trilinear 84% (±2%) - **MNLI**: Bilinear 72% (±3%), Trilinear 76% (±2%) #### 2b/8b (Default) Configuration - **SST-2**: Bilinear 89% (±1%), Trilinear 88% (±2%) - **MRPC**: Bilinear 86% (±2%), Trilinear 85% (±3%) - **RTE**: Bilinear 68% (±3%), Trilinear 65% (±2%) - **STS-B**: Bilinear 83% (±1%), Trilinear 82% (±2%) - **WNLI**: Bilinear 55% (±2%), Trilinear 55% (±1%) - **QNLI**: Bilinear 86% (±2%), Trilinear 85% (±3%) - **QQP**: Bilinear 84% (±1%), Trilinear 83% (±2%) - **MNLI**: Bilinear 73% (±3%), Trilinear 77% (±2%) #### 2b/9b Configuration - **SST-2**: Bilinear 89% (±1%), Trilinear 88% (±2%) - **MRPC**: Bilinear 87% (±2%), Trilinear 86% (±3%) - **RTE**: Bilinear 65% (±3%), Trilinear 62% (±2%) - **STS-B**: Bilinear 84% (±1%), Trilinear 83% (±2%) - **WNLI**: Bilinear 55% (±2%), Trilinear 55% (±1%) - **QNLI**: Bilinear 87% (±2%), Trilinear 86% (±3%) - **QQP**: Bilinear 85% (±1%), Trilinear 84% (±2%) - **MNLI**: Bilinear 74% (±3%), Trilinear 78% (±2%) ### Key Observations 1. **Consistent Performance**: Bilinear models consistently outperform trilinear models across all tasks and configurations, with the largest gap in RTE (65% vs 62% in 1b/6b). 2. **Task-Specific Variance**: WNLI shows minimal difference between architectures (55% for both), while RTE exhibits the largest performance gap. 3. **Configuration Impact**: Larger configurations (2b/9b) show slightly improved accuracy for both architectures compared to smaller ones (1b/6b), with MNLI showing the most improvement (70%→78% for trilinear). 4. **Error Bar Patterns**: Trilinear models generally show smaller error bars, suggesting more consistent performance across runs. ### Interpretation The data demonstrates that bilinear architectures maintain higher accuracy across diverse NLP tasks and computational constraints. The trilinear models show comparable performance in most cases but exhibit notable drops in RTE and WNLI tasks. The performance gap between architectures narrows in larger configurations (2b/9b), suggesting that increased computational resources may mitigate some of the trilinear model's limitations. The consistent performance of bilinear models across configurations implies architectural advantages that persist regardless of scale, while trilinear models show more variability in task-specific performance. The WNLI task's parity between architectures might indicate task-specific architectural suitability rather than general superiority.