## Line Graphs: MultiArith, SVAMP, Commonsense QA, ARC (Challenge)
### Overview
The image contains four line graphs comparing the performance of two reasoning methods ("Greedy Decode" and "Self Consistency") across four datasets: MultiArith, SVAMP, Commonsense QA, and ARC (Challenge). Each graph plots accuracy (%) against the number of sampled reasoning paths (0–40).
### Components/Axes
- **X-axis**: "#Sampled Reasoning Paths" (0–40, increments of 5).
- **Y-axis**: "Accuracy (%)" (ranges vary by dataset: MultiArith up to 75%, SVAMP up to 54%, Commonsense QA up to 62%, ARC up to 60%).
- **Legends**:
- Orange: "Greedy Decode (Single-path)"
- Blue: "Self Consistency (Multi-path)"
- **Datasets**:
- Top-left: MultiArith
- Top-right: SVAMP
- Bottom-left: Commonsense QA
- Bottom-right: ARC (Challenge)
### Detailed Analysis
#### MultiArith
- **Greedy Decode (Orange)**: Flat line at ~50% accuracy across all paths.
- **Self Consistency (Blue)**: Starts at ~45% (5 paths), rises sharply to ~75% by 40 paths.
#### SVAMP
- **Greedy Decode (Orange)**: Flat line at ~36% accuracy.
- **Self Consistency (Blue)**: Starts at ~33% (0 paths), increases to ~54% by 40 paths.
#### Commonsense QA
- **Greedy Decode (Orange)**: Flat line at ~54% accuracy.
- **Self Consistency (Blue)**: Starts at ~50% (0 paths), rises to ~62% by 40 paths.
#### ARC (Challenge)
- **Greedy Decode (Orange)**: Flat line at ~54% accuracy.
- **Self Consistency (Blue)**: Starts at ~50% (0 paths), increases to ~60% by 40 paths.
### Key Observations
1. **Self Consistency (Multi-path)** consistently outperforms **Greedy Decode (Single-path)** across all datasets.
2. **MultiArith** shows the steepest improvement for Self Consistency (45% → 75%).
3. **Greedy Decode** remains stagnant regardless of sampled paths, suggesting limited capacity for incremental gains.
4. **ARC (Challenge)** has the highest baseline accuracy (~54% for Greedy Decode) but the smallest improvement (~10% gain for Self Consistency).
### Interpretation
The data demonstrates that **Self Consistency (Multi-path)** significantly benefits from increased reasoning paths, particularly in complex tasks like MultiArith and Commonsense QA. This suggests that multi-path reasoning enables deeper exploration of logical steps, improving accuracy. In contrast, **Greedy Decode (Single-path)** lacks this adaptability, performing uniformly poorly. The ARC dataset’s high baseline accuracy for Greedy Decode implies it may rely on simpler heuristics, leaving less room for improvement. Overall, the results highlight the value of multi-path reasoning in tasks requiring nuanced problem-solving.
