## Bar Charts: Cross-Model Prediction Performance (AUROC)
### Overview
The image displays three separate bar charts arranged horizontally. Each chart compares the performance (measured in AUROC) of different language model configurations on four standard benchmarks (MMLU, TriviaQA, CoQA, WMT-14). The charts specifically evaluate the performance of one model architecture when used to predict the outputs of another, different architecture.
### Components/Axes
* **Y-Axis (All Charts):** Labeled "AUROC". The scale runs from 0.70 to 1.00, with major tick marks at 0.05 intervals (0.70, 0.75, 0.80, 0.85, 0.90, 0.95, 1.00).
* **X-Axis (All Charts):** Lists four benchmark categories: `MMLU`, `TriviaQA`, `CoQA`, `WMT-14`.
* **Chart Titles (Top):**
1. Left Chart: `Use LLaMA2 to predict Gemma-7B`
2. Middle Chart: `Use Gemma to predict LLaMA2-7B`
3. Right Chart: `Use Gemma to predict LLaMA3-8B`
* **Legends (Top-Right of each chart):** Each chart has a legend box identifying the four bar types. The colors/patterns are consistent across charts, but the labels change.
* **Left Chart Legend:**
* White bar: `Wb-S`
* Grey bar: `Gb-S`
* Green bar with diagonal stripes: `7B`
* Red bar with black dots: `13B`
* **Middle & Right Charts Legend:**
* White bar: `Wb-S`
* Grey bar: `Gb-S`
* Green bar with diagonal stripes: `2B`
* Red bar with black dots: `7B`
### Detailed Analysis
**Chart 1: Use LLaMA2 to predict Gemma-7B**
* **Trend:** Performance is generally high and stable across models for MMLU and TriviaQA. There is a notable drop for CoQA, followed by a recovery for WMT-14.
* **Data Points (Approximate AUROC):**
* **MMLU:** All four bars (`Wb-S`, `Gb-S`, `7B`, `13B`) are nearly equal, clustered around **0.83**.
* **TriviaQA:** Performance is higher. `Wb-S` (~0.88), `Gb-S` (~0.87), `7B` (~0.88), `13B` (~0.90). The `13B` model performs best.
* **CoQA:** A significant drop. `Wb-S` (~0.76), `Gb-S` (~0.74), `7B` (~0.74), `13B` (~0.75).
* **WMT-14:** Performance recovers. `Wb-S` (~0.85), `Gb-S` (~0.83), `7B` (~0.86), `13B` (~0.86).
**Chart 2: Use Gemma to predict LLaMA2-7B**
* **Trend:** Shows more variability. TriviaQA has the highest scores, while MMLU and CoQA are lower. The `7B` (red dotted) model often outperforms others.
* **Data Points (Approximate AUROC):**
* **MMLU:** All bars are low and equal, at approximately **0.72**.
* **TriviaQA:** High scores. `Wb-S` (~0.90), `Gb-S` (~0.81), `2B` (~0.84), `7B` (~0.92). The `7B` model is the clear leader.
* **CoQA:** Moderate scores. `Wb-S` (~0.81), `Gb-S` (~0.67), `2B` (~0.80), `7B` (~0.85). The `Gb-S` bar is notably the lowest in the entire chart.
* **WMT-14:** Moderate scores. `Wb-S` (~0.78), `Gb-S` (~0.72), `2B` (~0.76), `7B` (~0.78).
**Chart 3: Use Gemma to predict LLaMA3-8B**
* **Trend:** Performance is generally lower than in the first two charts, especially for CoQA and WMT-14. The `Wb-S` and `Gb-S` models often perform better than the smaller `2B` and `7B` models on several tasks.
* **Data Points (Approximate AUROC):**
* **MMLU:** All bars are equal, at approximately **0.83**.
* **TriviaQA:** `Wb-S` (~0.87), `Gb-S` (~0.86), `2B` (~0.77), `7B` (~0.84). The `2B` model shows a significant drop.
* **CoQA:** Low scores. `Wb-S` (~0.77), `Gb-S` (~0.74), `2B` (~0.72), `7B` (~0.74).
* **WMT-14:** The lowest scores in the chart. `Wb-S` (~0.75), `Gb-S` (~0.73), `2B` (~0.68), `7B` (~0.70).
### Key Observations
1. **Benchmark Difficulty:** CoQA consistently yields the lowest AUROC scores across all three prediction scenarios, suggesting it is the most challenging task for cross-model prediction in this experiment.
2. **Model Size Effect:** In Chart 1 (LLaMA2 predicting Gemma-7B), the largest model (`13B`, red dotted) generally performs best or ties for best. In Charts 2 and 3 (Gemma predicting LLaMA), the relationship is less clear; sometimes the larger model (`7B`, red dotted) wins, but sometimes the white (`Wb-S`) or grey (`Gb-S`) bars are superior.
3. **Task-Specific Performance:** TriviaQA often shows the highest AUROC values, indicating it may be an easier task for these models to predict on, or that the models' knowledge on this task is more aligned.
4. **Symmetry Break:** The performance when using LLaMA2 to predict Gemma (Chart 1) is not symmetric with using Gemma to predict LLaMA2 (Chart 2). The AUROC values and patterns differ, indicating the prediction difficulty is not reciprocal between model architectures.
### Interpretation
This data investigates the "predictability" or alignment between different Large Language Model (LLM) architectures. The AUROC metric likely measures how well one model can predict the correctness or output distribution of another on standardized benchmarks.
* **What it demonstrates:** The charts show that cross-model prediction performance is highly dependent on three factors: **1) The specific model architectures involved** (LLaMA2 vs. Gemma vs. LLaMA3), **2) The task domain** (e.g., knowledge QA vs. conversational QA vs. translation), and **3) The size/capability of the predictor model**. The lack of symmetry between Charts 1 and 2 is a key finding, suggesting that "Model A predicting Model B" is a different problem than "Model B predicting Model A."
* **Underlying Patterns:** The consistently lower scores on CoQA imply that conversational reasoning (the focus of CoQA) involves model-specific nuances that are harder to transfer or predict across architectures compared to more factual knowledge (TriviaQA, MMLU). The variable impact of model size suggests that simply making a predictor larger does not guarantee better cross-model prediction; the nature of the knowledge or reasoning being transferred is crucial.
* **Implication:** This type of analysis is valuable for understanding model similarity, knowledge overlap, and the potential for using one model as a "teacher" or "evaluator" for another. It suggests that creating universally predictive or evaluative models may be challenging, as performance is tightly coupled to the specific pair of models and the task at hand.
