## Grouped Bar Chart: Prediction Flip Rate by Dataset and Model
### Overview
The image displays two side-by-side grouped bar charts comparing the "Prediction Flip Rate" for two different large language models (Llama-3-8B and Llama-3-70B) across four question-answering datasets. The charts evaluate the stability of model predictions under two different anchoring conditions.
### Components/Axes
* **Chart Titles (Top Center):**
* Left Chart: `Llama-3-8B`
* Right Chart: `Llama-3-70B`
* **Y-Axis (Left Side of Each Chart):**
* Label: `Prediction Flip Rate`
* Scale: 0 to 60, with major tick marks at 0, 20, 40, and 60.
* **X-Axis (Bottom of Each Chart):**
* Label: `Dataset`
* Categories (from left to right): `PopQA`, `TriviaQA`, `HotpotQA`, `NQ`
* **Legend (Bottom Center, spanning both charts):**
* A red/brown square labeled: `Q-Anchored (exact_question)`
* A gray square labeled: `A-Anchored (exact_question)`
* **Data Series:** Each dataset category has two bars, one for each anchoring condition, placed side-by-side.
### Detailed Analysis
**Llama-3-8B Chart (Left Panel):**
* **Trend Verification:** For all four datasets, the Q-Anchored (red/brown) bar is significantly taller than the A-Anchored (gray) bar, indicating a higher prediction flip rate when the question is anchored.
* **Data Points (Approximate Values):**
* **PopQA:** Q-Anchored ≈ 53, A-Anchored ≈ 10
* **TriviaQA:** Q-Anchored ≈ 68, A-Anchored ≈ 39
* **HotpotQA:** Q-Anchored ≈ 39, A-Anchored ≈ 9
* **NQ:** Q-Anchored ≈ 69, A-Anchored ≈ 22
**Llama-3-70B Chart (Right Panel):**
* **Trend Verification:** The same pattern holds: Q-Anchored bars are consistently taller than A-Anchored bars across all datasets. The overall height of the bars appears slightly lower compared to the 8B model for most categories.
* **Data Points (Approximate Values):**
* **PopQA:** Q-Anchored ≈ 66, A-Anchored ≈ 14
* **TriviaQA:** Q-Anchored ≈ 57, A-Anchored ≈ 18
* **HotpotQA:** Q-Anchored ≈ 54, A-Anchored ≈ 17
* **NQ:** Q-Anchored ≈ 42, A-Anchored ≈ 26
### Key Observations
1. **Consistent Anchoring Effect:** Across both model sizes and all four datasets, anchoring the prompt with the exact question (`Q-Anchored`) leads to a substantially higher prediction flip rate than anchoring with the exact answer (`A-Anchored`).
2. **Dataset Variability:** The magnitude of the flip rate varies by dataset. For example, `TriviaQA` and `NQ` show very high Q-Anchored flip rates for the 8B model, while `HotpotQA` shows the lowest for both anchoring types in that model.
3. **Model Size Comparison:** The larger Llama-3-70B model generally exhibits lower flip rates than the 8B model, particularly for the Q-Anchored condition on datasets like `TriviaQA` and `NQ`. However, for `PopQA`, the 70B model's Q-Anchored flip rate is higher.
4. **Relative Stability:** The A-Anchored condition results in flip rates mostly below 30, suggesting predictions are more stable when anchored to an answer format.
### Interpretation
This data suggests that the **format of the prompt significantly influences the stability of a large language model's predictions**. When a model is prompted with a question (`Q-Anchored`), its output is more volatile and prone to "flipping" (changing) compared to when it is prompted with a fixed answer format (`A-Anchored`).
The **Peircean investigative reading** implies that the "exact_question" anchor introduces more interpretative ambiguity or contextual variability for the model, leading to less deterministic outputs. In contrast, the "exact_question" anchor (likely meaning the prompt is structured around providing an answer) constrains the model's response space, leading to greater consistency.
The **anomaly** is that while the 70B model is generally more stable, it is not uniformly so; its higher flip rate on `PopQA` under Q-Anchoring indicates that dataset-specific characteristics can interact with model scale in non-linear ways. This highlights that prompt engineering for stability must be tailored to both the model and the specific knowledge domain (dataset).
