## Multi-Chart Performance Analysis: Llama 2 Models on Various Datasets ### Overview The image is a composite figure containing a grid of 40 line charts. It presents a performance comparison of four different methods (SD, SpecTr, RSD-C, RSD-S) across four Llama 2 model variants (7B, 13B, Chat-7B, Chat-13B) on three datasets (WMT, XSum, Dolly). Performance is measured across four metrics: block efficiency, MBSU, token rate, and accuracy, as a function of the number of tokens at the target. ### Components/Axes * **Grid Structure:** The charts are arranged in a 10-row by 4-column grid. * **Rows:** Grouped by model and dataset. From top to bottom: 1. Llama 2-7B on WMT 2. Llama 2-7B on XSum 3. Llama 2-13B on WMT 4. Llama 2-13B on XSum 5. Llama 2-Chat-7B on WMT 6. Llama 2-Chat-7B on XSum 7. Llama 2-Chat-7B on Dolly 8. Llama 2-Chat-13B on WMT 9. Llama 2-Chat-13B on XSum 10. Llama 2-Chat-13B on Dolly * **Columns:** Represent the four metrics. From left to right: 1. **block efficiency** 2. **MBSU** 3. **token rate** 4. **accuracy** * **X-Axis (Common):** Labeled "num. tokens at the target" at the bottom of the grid. The axis markers are at values: 6, 10, 14, 21, 30. * **Y-Axes:** Each column has its own y-axis scale and label. * **block efficiency:** Scale varies per chart, typically ranging from ~1.7 to 4.1. * **MBSU:** Scale varies per chart, typically ranging from ~1.3 to 3.7. * **token rate:** Scale varies per chart, typically ranging from ~0.3 to 1.9. * **accuracy:** Scale is consistently from 0.7 to 1.3 for all charts in this column. * **Legend:** Located at the very bottom of the entire figure. It defines the four data series: * **SD:** Orange dotted line with 'x' markers. * **SpecTr:** Red dash-dot line with '+' markers. * **RSD-C (ours):** Green dashed line with diamond markers. * **RSD-S (ours):** Solid blue line with circle markers. ### Detailed Analysis **Trend Verification & Data Point Extraction (Approximate Values):** **1. Llama 2-7B** * **WMT:** * *Block Efficiency:* All methods increase with token count. RSD-S (blue) is highest (~2.5 at 30 tokens), followed by RSD-C (green, ~2.3), SpecTr (red, ~2.2), and SD (orange, ~2.1). * *MBSU:* Similar increasing trend. RSD-S leads (~2.4 at 30), RSD-C (~2.2), SpecTr (~2.1), SD (~1.9). * *Token Rate:* RSD-S, RSD-C, and SpecTr are relatively flat and high (~1.3-1.4). SD shows a sharp decline from ~1.2 at 6 tokens to ~0.4 at 30 tokens. * *Accuracy:* All methods maintain a flat line at exactly 1.0 across all token counts. * **XSum:** * *Block Efficiency:* Strong upward trend. RSD-S (~4.0 at 30), RSD-C (~3.3), SpecTr (~3.2), SD (~3.1). * *MBSU:* Upward trend. RSD-S (~3.6 at 30), RSD-C (~3.1), SpecTr (~3.0), SD (~2.8). * *Token Rate:* RSD-S, RSD-C, SpecTr are high and stable (~1.7-1.8). SD declines from ~1.5 to ~0.7. * *Accuracy:* Flat at 1.0 for all. **2. Llama 2-13B** * **WMT:** * *Block Efficiency:* Increasing. RSD-S (~2.5 at 30), RSD-C (~2.3), SpecTr (~2.2), SD (~2.1). * *MBSU:* Increasing. RSD-S (~2.4 at 30), RSD-C (~2.2), SpecTr (~2.1), SD (~1.9). * *Token Rate:* RSD-S, RSD-C, SpecTr stable and high (~1.4-1.5). SD declines from ~1.2 to ~0.5. * *Accuracy:* Flat at 1.0. * **XSum:** * *Block Efficiency:* Increasing. RSD-S (~3.9 at 30), RSD-C (~3.3), SpecTr (~3.2), SD (~3.1). * *MBSU:* Increasing. RSD-S (~3.6 at 30), RSD-C (~3.1), SpecTr (~3.0), SD (~2.8). * *Token Rate:* RSD-S, RSD-C, SpecTr stable/high (~1.7-1.8). SD declines from ~1.5 to ~0.7. * *Accuracy:* Flat at 1.0. **3. Llama 2-Chat-7B** * **WMT:** * *Block Efficiency:* Increasing. RSD-S (~2.4 at 30), RSD-C (~2.1), SpecTr (~2.0), SD (~1.9). * *MBSU:* Increasing. RSD-S (~2.1 at 30), RSD-C (~1.9), SpecTr (~1.8), SD (~1.7). * *Token Rate:* RSD-S, RSD-C, SpecTr stable/high (~1.3). SD declines from ~1.1 to ~0.5. * *Accuracy:* Flat at 1.0. * **XSum:** * *Block Efficiency:* Increasing. RSD-S (~3.2 at 30), RSD-C (~2.8), SpecTr (~2.7), SD (~2.6). * *MBSU:* Increasing. RSD-S (~2.9 at 30), RSD-C (~2.5), SpecTr (~2.4), SD (~2.3). * *Token Rate:* RSD-S, RSD-C, SpecTr stable/high (~1.6). SD declines from ~1.4 to ~0.6. * *Accuracy:* Flat at 1.0. * **Dolly:** * *Block Efficiency:* Increasing. RSD-S (~3.5 at 30), RSD-C (~3.2), SpecTr (~3.1), SD (~3.0). * *MBSU:* Increasing. RSD-S (~3.1 at 30), RSD-C (~2.8), SpecTr (~2.7), SD (~2.6). * *Token Rate:* RSD-S, RSD-C, SpecTr stable/high (~1.5). SD declines from ~1.3 to ~0.5. * *Accuracy:* Flat at 1.0. **4. Llama 2-Chat-13B** * **WMT:** * *Block Efficiency:* Increasing. RSD-S (~2.5 at 30), RSD-C (~2.3), SpecTr (~2.2), SD (~2.1). * *MBSU:* Increasing. RSD-S (~2.4 at 30), RSD-C (~2.2), SpecTr (~2.1), SD (~1.9). * *Token Rate:* RSD-S, RSD-C, SpecTr stable/high (~1.5). SD declines from ~1.3 to ~0.5. * *Accuracy:* Flat at 1.0. * **XSum:** * *Block Efficiency:* Increasing. RSD-S (~3.3 at 30), RSD-C (~2.8), SpecTr (~2.7), SD (~2.6). * *MBSU:* Increasing. RSD-S (~3.0 at 30), RSD-C (~2.6), SpecTr (~2.5), SD (~2.4). * *Token Rate:* RSD-S, RSD-C, SpecTr stable/high (~1.7). SD declines from ~1.5 to ~0.6. * *Accuracy:* Flat at 1.0. * **Dolly:** * *Block Efficiency:* Increasing. RSD-S (~3.3 at 30), RSD-C (~3.0), SpecTr (~2.9), SD (~2.8). * *MBSU:* Increasing. RSD-S (~3.0 at 30), RSD-C (~2.7), SpecTr (~2.6), SD (~2.5). * *Token Rate:* RSD-S, RSD-C, SpecTr stable/high (~1.6). SD declines from ~1.4 to ~0.6. * *Accuracy:* Flat at 1.0. ### Key Observations 1. **Consistent Hierarchy:** Across nearly all charts for block efficiency and MBSU, the performance order is consistent: **RSD-S (blue) > RSD-C (green) > SpecTr (red) > SD (orange)**. 2. **Token Rate Divergence:** The "token rate" metric shows a critical split. RSD-S, RSD-C, and SpecTr maintain a high, stable rate as token count increases. In stark contrast, **SD (orange) exhibits a severe, near-linear decline** in token rate for all models and datasets. 3. **Perfect Accuracy:** The "accuracy" column shows all methods achieve a perfect score of 1.0 across all conditions, indicating no degradation in output correctness despite differences in efficiency or speed. 4. **Model & Dataset Scaling:** The absolute values for block efficiency and MBSU are generally higher on the XSum and Dolly datasets compared to WMT. The Chat-tuned models show slightly different absolute values but follow the same relative trends as their base counterparts. ### Interpretation This data strongly suggests that the proposed methods, **RSD-S and RSD-C, offer a significant improvement in generation efficiency (block efficiency, MBSU) over the baselines (SpecTr, SD) without sacrificing accuracy.** The most dramatic finding is the failure mode of the SD method: while it maintains accuracy, its **token generation rate collapses as the target length increases**, making it impractical for longer sequences. SpecTr offers a middle ground, but is consistently outperformed by the RSD variants. The charts demonstrate that the RSD methods successfully decouple generation speed (token rate) from sequence length, maintaining high throughput where SD does not. This is a crucial property for efficient large language model inference. The consistency of these results across multiple model sizes (7B, 13B), training paradigms (base, chat), and diverse tasks (translation, summarization, instruction following) indicates the robustness of the advantage held by the RSD approaches. The perfect accuracy scores imply that the efficiency gains are not achieved by compromising on the quality of the generated text.