## Line Chart: NNCD Improvement vs. Number of Shots
### Overview
The chart compares the NNCD (Normalized Negative Log-Likelihood Difference) improvement across three language models (RWKV 169M, GPT2 117M, OPT 125M) at varying numbers of training shots (5, 10, 50, 100). Error bars represent uncertainty in measurements.
### Components/Axes
- **X-axis**: Number of shots (5, 10, 50, 100)
- **Y-axis**: NNCD Improvement (range: -0.5 to 0.2)
- **Legend**: Located at bottom-right, mapping colors to models:
- Green: RWKV 169M
- Blue: GPT2 117M
- Red: OPT 125M
### Detailed Analysis
1. **RWKV 169M (Green)**
- **5 shots**: -0.30 (±0.10)
- **10 shots**: -0.35 (±0.12)
- **50 shots**: -0.25 (±0.08)
- **100 shots**: -0.28 (±0.07)
- **Trend**: Slight improvement with more shots, but remains negative. Error bars decrease with shot count.
2. **GPT2 117M (Blue)**
- **5 shots**: 0.12 (±0.08)
- **10 shots**: 0.13 (±0.07)
- **50 shots**: 0.14 (±0.06)
- **100 shots**: 0.15 (±0.05)
- **Trend**: Consistent upward trajectory. Error bars shrink slightly at higher shot counts.
3. **OPT 125M (Red)**
- **5 shots**: -0.45 (±0.15)
- **10 shots**: -0.40 (±0.12)
- **50 shots**: -0.35 (±0.09)
- **100 shots**: -0.30 (±0.08)
- **Trend**: Gradual improvement with more shots. Error bars decrease as shot count increases.
### Key Observations
- **GPT2 117M** consistently outperforms others, maintaining positive NNCD improvement across all shot counts.
- **RWKV 169M** and **OPT 125M** show negative improvement at low shot counts but improve marginally with more data.
- **Error bars** are largest at 5-10 shots, indicating higher uncertainty in small datasets.
- **GPT2's stability** (smallest error bars) suggests robustness to data scarcity.
### Interpretation
The data demonstrates that **GPT2 117M** achieves the highest NNCD improvement, likely due to its architecture or training methodology. All models benefit from increased shot counts, but **RWKV** and **OPT** require significantly more data to approach GPT2's performance. The error bars highlight the challenge of training with limited data, particularly for smaller models (e.g., OPT 125M at 5 shots shows ±0.15 uncertainty). This suggests that **data efficiency** is a critical factor in model performance, with GPT2's architecture potentially offering better generalization from limited examples.
