## Charts: ECE-F1 Scores and Accuracy vs Confidence
### Overview
The image presents a series of five charts (a-e) comparing the performance of different language models (RL-DoublyCal, LLM Reasoner, GPT-3.5-turbo, GPT-4o-mini, DeepSeek-V3, and Gemini-2.5-flash) based on two primary metrics: Expected Calibration Error (ECE) and F1 scores (chart a), and Accuracy vs. Confidence (charts b-e). Charts (b) through (e) also include a count of data points at each confidence level, represented by bar graphs.
### Components/Axes
* **Chart (a): ECE – F1 scores**
* X-axis: ECE (%) - Scale: 0 to 50, with markers at 10, 20, 30, 40, 50.
* Y-axis: F1 scores (%) - Scale: 0 to 100, with markers at 10, 20, 30, 40, 50, 60, 70, 80, 90, 100.
* Legend:
* RL-DoublyCal (Red Square)
* LLM Reasoner (Green Circle)
* GPT-3.5-turbo (Orange Diamond)
* GPT-4o-mini (Light Blue Triangle)
* DeepSeek-V3 (Yellow Star)
* Gemini-2.5-flash (Teal Pentagon)
* Annotations:
* Avg. F1=76.5, Avg. ECE=7.6 (Top-left)
* Avg. F1=45.8, Avg. ECE=29.4 (Bottom-right)
* **Charts (b) - (e): Accuracy vs. Confidence**
* X-axis: Confidence - Scale: 0.0 to 1.0, with markers at 0.0, 0.2, 0.4, 0.6, 0.8, 1.0.
* Y-axis: Accuracy - Scale: 0.0 to 1.0, with markers at 0.0, 0.2, 0.4, 0.6, 0.8, 1.0.
* Secondary Y-axis (right side): Count - Scale: 0 to 5000, with markers at 1000, 2000, 3000, 4000, 5000.
* Legend (consistent across b-e):
* RL-DoublyCal (Red Line with Square Markers)
* LLM Reasoner (Blue Dashed Line with Square Markers)
* **Chart Titles:**
* (a) ECE – F1 scores
* (b) GPT-3.5-turbo
* (c) GPT-4o-mini
* (d) DeepSeek-V3
* (e) Gemini-2.5-flash
### Detailed Analysis or Content Details
* **Chart (a): ECE – F1 scores**
* RL-DoublyCal: Starts at approximately (10, 75), decreases to (30, 40).
* LLM Reasoner: Starts at approximately (10, 80), decreases to (40, 40).
* GPT-3.5-turbo: Starts at approximately (10, 78), decreases to (30, 50).
* GPT-4o-mini: Starts at approximately (10, 82), decreases to (30, 60).
* DeepSeek-V3: Starts at approximately (10, 70), decreases to (30, 45).
* Gemini-2.5-flash: Starts at approximately (10, 65), decreases to (30, 40).
* **Chart (b): GPT-3.5-turbo**
* RL-DoublyCal: Accuracy increases from ~0.1 at confidence 0.0 to ~0.9 at confidence 1.0. Count peaks around confidence 0.8 at ~2500.
* LLM Reasoner: Accuracy increases from ~0.1 at confidence 0.0 to ~0.8 at confidence 1.0. Count peaks around confidence 0.9 at ~3000.
* **Chart (c): GPT-4o-mini**
* RL-DoublyCal: Accuracy increases from ~0.1 at confidence 0.0 to ~0.9 at confidence 1.0. Count peaks around confidence 0.8 at ~3500.
* LLM Reasoner: Accuracy increases from ~0.1 at confidence 0.0 to ~0.9 at confidence 1.0. Count peaks around confidence 0.9 at ~4000.
* **Chart (d): DeepSeek-V3**
* RL-DoublyCal: Accuracy increases from ~0.1 at confidence 0.0 to ~0.9 at confidence 1.0. Count peaks around confidence 0.8 at ~3000.
* LLM Reasoner: Accuracy increases from ~0.1 at confidence 0.0 to ~0.8 at confidence 1.0. Count peaks around confidence 0.9 at ~3500.
* **Chart (e): Gemini-2.5-flash**
* RL-DoublyCal: Accuracy increases from ~0.1 at confidence 0.0 to ~0.9 at confidence 1.0. Count peaks around confidence 0.8 at ~4000.
* LLM Reasoner: Accuracy increases from ~0.1 at confidence 0.0 to ~0.8 at confidence 1.0. Count peaks around confidence 0.9 at ~4500.
### Key Observations
* In Chart (a), there is a clear negative correlation between ECE and F1 scores. Higher ECE generally corresponds to lower F1 scores.
* GPT-4o-mini consistently exhibits the highest F1 scores across different ECE levels.
* Charts (b) through (e) show that both RL-DoublyCal and LLM Reasoner generally exhibit increasing accuracy with increasing confidence.
* The count of data points is generally higher for LLM Reasoner at higher confidence levels.
### Interpretation
The data suggests that model calibration (as measured by ECE) is strongly related to overall performance (F1 score). Models that are well-calibrated (low ECE) tend to achieve higher F1 scores. GPT-4o-mini appears to be the best-performing model in terms of F1 score, while also maintaining reasonable calibration.
The accuracy vs. confidence plots (b-e) indicate that all models become more accurate as their confidence increases. However, the rate of accuracy improvement varies between models. The bar graphs showing the count of data points suggest that LLM Reasoner is more frequently used or generates more predictions at higher confidence levels.
The consistent upward trend in accuracy with confidence across all models suggests that confidence scores are generally reliable indicators of prediction quality. The differences in the count distributions might reflect differences in the models' decision-making processes or the characteristics of the datasets they were evaluated on. The annotations on chart (a) provide average values, which can be used to compare the overall performance of the models.
