# Technical Data Extraction: Model Accuracy vs. Context Length ## 1. Component Isolation * **Header:** None present. * **Main Chart Area:** A 2D line graph plotted on a Cartesian coordinate system with a light-gray dashed grid. * **Legend:** Located in the center-right of the plot area. * **Axes:** Y-axis (left) representing "Accuracy" and X-axis (bottom) representing "Context length". --- ## 2. Axis Labels and Markers ### Y-Axis (Vertical) * **Label:** Accuracy * **Scale:** Linear, ranging from 0.0 to 1.0. * **Major Tick Markers:** 0.0, 0.2, 0.4, 0.6, 0.8, 1.0. ### X-Axis (Horizontal) * **Label:** Context length * **Scale:** Linear, ranging from approximately 500 to 5500. * **Major Tick Markers:** 1000, 2000, 3000, 4000, 5000. --- ## 3. Legend and Data Series Identification The legend contains two entries, which correspond to the two lines plotted: 1. **Blue Line (Dark Blue/Teal):** `finetune on 32k(base=500)` 2. **Red Line:** `Llama2-7B-Baseline` --- ## 4. Trend Verification and Data Extraction ### Series 1: Llama2-7B-Baseline (Red Line) * **Visual Trend:** The line starts at near-perfect accuracy (~0.98) at the shortest context length. It maintains high performance (fluctuating between 0.85 and 1.0) until a context length of approximately 3800. At the 4000 mark, the line exhibits a catastrophic "cliff-edge" drop, falling vertically to 0.0 and remaining at 0.0 for all subsequent context lengths. * **Key Data Points (Estimated):** | Context Length | Accuracy | | :--- | :--- | | ~500 | 0.98 | | 1000 | 0.98 | | 2000 | 1.0 | | 2200 | 0.84 | | 3800 | 0.80 | | 4000 | 0.0 | | 5000+ | 0.0 | ### Series 2: finetune on 32k(base=500) (Blue Line) * **Visual Trend:** This line starts high (~0.98) but immediately begins a steep decline as context length increases. By a context length of 1000, accuracy has dropped significantly. It continues to trend downward with high volatility (zig-zagging) between 0.0 and 0.4. Unlike the baseline, it does not hit a hard zero at 4000, but its overall performance is significantly lower than the baseline in the 500-3800 range. * **Key Data Points (Estimated):** | Context Length | Accuracy | | :--- | :--- | | ~500 | 0.98 | | 1000 | 0.26 | | 1500 | 0.10 | | 2000 | 0.36 | | 3000 | 0.10 | | 4000 | 0.02 | | 4500 | 0.18 | | 5500 | 0.12 | --- ## 5. Summary of Findings The chart compares the performance of a baseline Llama2-7B model against a version finetuned on 32k context with a base of 500. * **The Baseline (Red)** is highly effective within its native context window (up to ~3800-4000 tokens) but fails completely and immediately once that limit is exceeded. * **The Finetuned Model (Blue)** shows a significant degradation in accuracy even at relatively short context lengths (starting at 1000). While it technically "survives" past the 4000-token limit where the baseline fails, its accuracy remains very low (generally below 0.2) and unstable across the entire extended range.

# Technical Document Extraction: Accuracy vs. Context Length ## Chart Description This image is a **line graph** comparing the accuracy of two language models across varying context lengths. The graph includes two data series, axis labels, a legend, and gridlines for reference. --- ### **Key Components** 1. **Axes** - **X-axis (Horizontal)**: - Label: `Context length` - Range: `1000` to `5000` (in increments of 1000) - Ticks: `1000`, `2000`, `3000`, `4000`, `5000` - **Y-axis (Vertical)**: - Label: `Accuracy` - Range: `0.0` to `1.0` (in increments of 0.2) - Ticks: `0.0`, `0.2`, `0.4`, `0.6`, `0.8`, `1.0` 2. **Legend** - Located in the **upper right corner**. - Entries: - `finetune on 32k(base=500)` (blue line) - `Llama2-7B-Baseline` (red line) 3. **Gridlines** - Dashed gray lines span the plot area, aligning with axis ticks. --- ### **Data Series Analysis** #### **1. `finetune on 32k(base=500)` (Blue Line)** - **Trend**: - Starts at **1.0 accuracy** at `context length = 1000`. - Sharp decline to **~0.2 accuracy** by `context length = 2000`. - Fluctuates between **0.1 and 0.3** for `context lengths = 3000` to `5000`. - **Key Data Points**: - `1000`: 1.0 - `2000`: ~0.2 - `3000`: ~0.15 - `4000`: ~0.05 - `5000`: ~0.15 #### **2. `Llama2-7B-Baseline` (Red Line)** - **Trend**: - Maintains **~1.0 accuracy** until `context length = 4000`. - Abrupt drop to **0.0 accuracy** at `context length = 4000`. - Remains at 0.0 for `context lengths = 4000` to `5000`. - **Key Data Points**: - `1000`: 1.0 - `2000`: 1.0 - `3000`: 1.0 - `4000`: 0.0 - `5000`: 0.0 --- ### **Cross-Reference Verification** - **Legend Colors**: - Blue (`finetune`) matches the blue line. - Red (`Llama2-7B-Baseline`) matches the red line. - **Spatial Grounding**: - Legend is positioned in the **upper right** (coordinates: `[x=0.8, y=0.9]` relative to the plot area). --- ### **Observations** 1. The `finetune on 32k(base=500)` model shows **significant performance degradation** as context length increases beyond 1000. 2. The `Llama2-7B-Baseline` model maintains high accuracy until `context length = 4000`, after which it fails completely. 3. Both models exhibit **non-linear behavior**, with sharp transitions at specific context lengths. --- ### **Conclusion** The graph highlights a critical divergence in model performance: the `finetune` model degrades gradually, while the `Llama2-7B-Baseline` model fails catastrophically at `context length = 4000`. Further investigation is warranted to understand the root cause of the red line's abrupt collapse.