## Line Charts: Distillation Performance Comparison
### Overview
This image presents three line charts, arranged horizontally, comparing the performance of different distillation methods based on training steps. Each chart focuses on a specific distillation loss function: Embedding-based Distillation (Ldistill), InfoNCE (LNCE), and Score-based Distillation (Lscore). The performance metric used is Average nDCG@10. Each chart compares two learning rates: 1e-4 (lr) and 1e-5 (lr).
### Components/Axes
Each chart shares the following components:
* **X-axis:** Training Steps (ranging from 0 to 6000, with increments of 1000)
* **Y-axis:** Average nDCG@10 (ranging from 0 to 0.6, with increments of 0.1)
* **Legend:** Located in the bottom-right corner of each chart, indicating the learning rates:
* 1e-4 lr (represented by a blue line with a circular marker)
* 1e-5 lr (represented by an orange line with a circular marker)
* **Title:** Positioned at the top of each chart, specifying the distillation loss function.
### Detailed Analysis or Content Details
**Chart 1: Embedding-based Distillation (Ldistill)**
* **1e-4 lr (Blue Line):** The line slopes upward consistently from approximately 0.02 at 0 training steps to approximately 0.56 at 6000 training steps.
* (0, 0.02)
* (1000, 0.15)
* (2000, 0.28)
* (3000, 0.40)
* (4000, 0.48)
* (5000, 0.53)
* (6000, 0.56)
* **1e-5 lr (Orange Line):** The line starts at approximately 0.01 at 0 training steps and increases to approximately 0.12 at 6000 training steps.
* (0, 0.01)
* (1000, 0.04)
* (2000, 0.07)
* (3000, 0.09)
* (4000, 0.10)
* (5000, 0.11)
* (6000, 0.12)
**Chart 2: InfoNCE (LNCE)**
* **1e-4 lr (Blue Line):** The line initially decreases from approximately 0.48 at 0 training steps to approximately 0.40 at 1000 training steps, then plateaus around 0.42-0.45 for the remaining training steps.
* (0, 0.48)
* (1000, 0.40)
* (2000, 0.42)
* (3000, 0.43)
* (4000, 0.44)
* (5000, 0.44)
* (6000, 0.45)
* **1e-5 lr (Orange Line):** The line starts at approximately 0.42 at 0 training steps, decreases to approximately 0.35 at 1000 training steps, and then gradually increases to approximately 0.45 at 6000 training steps.
* (0, 0.42)
* (1000, 0.35)
* (2000, 0.38)
* (3000, 0.40)
* (4000, 0.42)
* (5000, 0.43)
* (6000, 0.45)
**Chart 3: Score-based Distillation (Lscore)**
* **1e-4 lr (Blue Line):** The line decreases from approximately 0.45 at 0 training steps to approximately 0.38 at 1000 training steps, then plateaus around 0.40-0.42 for the remaining training steps.
* (0, 0.45)
* (1000, 0.38)
* (2000, 0.40)
* (3000, 0.41)
* (4000, 0.41)
* (5000, 0.41)
* (6000, 0.42)
* **1e-5 lr (Orange Line):** The line starts at approximately 0.43 at 0 training steps, decreases to approximately 0.35 at 1000 training steps, and then gradually increases to approximately 0.47 at 6000 training steps.
* (0, 0.43)
* (1000, 0.35)
* (2000, 0.39)
* (3000, 0.42)
* (4000, 0.44)
* (5000, 0.45)
* (6000, 0.47)
### Key Observations
* For Embedding-based Distillation, the 1e-4 learning rate consistently outperforms the 1e-5 learning rate across all training steps.
* For InfoNCE and Score-based Distillation, the performance difference between the two learning rates is less pronounced, and the 1e-5 learning rate shows a recovery in performance towards the end of the training process.
* InfoNCE and Score-based Distillation exhibit a more stable performance compared to Embedding-based Distillation, with less dramatic changes in nDCG@10 over training steps.
### Interpretation
The charts demonstrate the impact of different distillation loss functions and learning rates on the performance of a model, as measured by Average nDCG@10. The superior performance of the 1e-4 learning rate in Embedding-based Distillation suggests that a larger learning rate is more effective for this specific loss function. The initial dip in performance for InfoNCE and Score-based Distillation with both learning rates could indicate a period of adjustment or exploration before the model converges. The eventual recovery of the 1e-5 learning rate in these two methods suggests that a smaller learning rate might be beneficial for fine-tuning or achieving a more stable solution. The overall trends suggest that the choice of distillation loss function and learning rate are crucial factors in optimizing model performance, and the optimal combination may depend on the specific characteristics of the dataset and model architecture.
