English Expression Guide for the Exam

This exam is written and answered in English. These templates help you write clear, precise, high-scoring answers.

Universal Answer Structures

Structure 1: "Evaluate a Suggestion" (most common question type)

[YES/NO], [suggestion] is [likely/unlikely] to improve validation accuracy.

This is because the current model is [overfitting/underfitting], as evidenced by 
[training accuracy being much higher/lower than validation accuracy].

[Suggestion] works by [mechanism], which [helps/does not help] with 
[overfitting/underfitting] because [specific reason].

Example: "Yes, L2 regularisation is likely to improve validation accuracy. The current model is overfitting, as the training accuracy (95%) is much higher than the validation accuracy (60%). L2 regularisation penalises large weights, encouraging the model to learn a simpler, more generalisable representation, which helps reduce overfitting."

Structure 2: "Explain Concept X"

[Concept] is [one-sentence definition].

It works by [mechanism in 1-2 sentences].

This is beneficial/important because [why it matters].

Structure 3: "Interpret Model Performance"

The [metric] is [value], which indicates [what this means].

However, [other metric] reveals that [deeper insight].

This is because [explanation of model behavior, e.g., class imbalance, 
predicting everything as one class].

Structure 4: "Compare A and B"

While [A] [feature of A], [B] [feature of B].

The key advantage of [A] is [advantage], whereas [B] excels at [advantage].

However, [A]'s main drawback is [drawback], which [B] addresses by [solution].

Topic-Specific Sentence Templates

Data Preprocessing（数据预处理 — 考察你能否从pipeline步骤反推数据特征）

• "The use of [median/most frequent] imputation suggests the data is [numerical/categorical] with missing values."
• "The standardisation step indicates that features are on different scales."
• "The log transformation suggests the data has a heavy-tailed distribution."
• "Removing the attribute is justified because [X]% of values are missing, and imputation would create misleading information."
• "Outlier removal is appropriate because the maximum value ([X]) is significantly larger than expected given the mean ([Y]) and standard deviation ([Z])."

Bias-Variance（偏差-方差 — 考察你能否从train/val数字诊断问题并给出建议）

• "The model displays high variance, as there is a clear gap between the training and validation [accuracy/loss]."
• "This indicates overfitting, where the model fits the training data too closely but fails to generalise."
• "The model appears to have high bias, as both training and validation accuracies are low."
• "This suggests underfitting — the model is not complex enough to capture the underlying patterns."
• "Applying regularisation can help reduce overfitting by limiting the complexity of the model."
• "Increasing the model size may help address underfitting by giving the model more capacity to learn."

CNN（卷积神经网络 — 考察维度计算和结构优势解释）

• "The output dimensions of the convolutional layer are calculated as: floor((n + 2p - f) / s) + 1."
• "With valid padding (p=0), the spatial dimensions of the output will be smaller than the input."
• "With same padding, the output spatial dimensions match the input spatial dimensions when stride is 1."
• "The depth of the output equals the number of filters applied."
• "Pooling reduces the spatial dimensions while preserving the depth."
• "Max pooling and average pooling produce output with the same dimensions; only the values differ."

Transformer & Attention（注意力机制 — 考察位置编码、遮蔽、多头注意力的作用）

• "The masking in the decoder prevents each position from attending to future tokens."
• "This preserves the autoregressive property, ensuring that predictions depend only on previously generated tokens."
• "Multi-head attention runs several attention functions in parallel, each with its own learned weight matrices."
• "This allows the model to focus on different aspects of the input simultaneously."
• "The [CLS] token in ViT aggregates information from all image patches for the final classification."
• "Positional encoding is necessary because the Transformer processes all positions in parallel, losing inherent ordering."

Learning Rate & Optimizers（学习率与优化器 — 考察loss曲线诊断和优化器选择）

• "A diverging loss curve indicates a learning rate that is too high, causing the optimisation to overshoot."
• "A very slowly decreasing loss suggests the learning rate is too small."
• "Learning rate scheduling, such as exponential decay, allows faster initial convergence while avoiding overshooting near the optimum."
• "The momentum mechanism maintains an exponentially decaying average of past gradients, smoothing the optimisation trajectory."

Evaluation Metrics（评估指标 — 考察混淆矩阵计算和类别不平衡下的指标选择）

• "The accuracy is calculated as (TP + TN) / (TP + TN + FP + FN) = ..."
• "Despite the seemingly acceptable accuracy, the model performs poorly at identifying positive instances."
• "The high recall but low precision indicates the model predicts most instances as positive."
• "This discrepancy highlights the importance of examining metrics beyond accuracy, particularly with imbalanced datasets."

Activation Functions（激活函数 — 考察sigmoid/softmax/ReLU的选择和死亡ReLU问题）

• "The dying ReLU problem occurs when neurons consistently receive negative inputs, causing them to output zero and stop learning."
• "LeakyReLU mitigates this by introducing a small positive slope for negative inputs."
• "For multi-label classification, sigmoid is the appropriate output activation because each output is treated independently."
• "Softmax is unsuitable for multi-label problems because it forces all outputs to sum to 1."

Batch Normalisation（批量归一化 — 考察BN的两个效果：加速训练+正则化）

• "Batch normalisation speeds up training by normalising activations within each mini-batch."
• "It reduces the risk of vanishing and exploding gradients by keeping activations in a healthy range."
• "The normalisation over mini-batches introduces noise, which has a regularising effect."
• "It also reduces sensitivity to weight initialisation by automatically adjusting activation distributions."

Regularisation (L1, L2, Dropout, Early Stopping)（正则化 — 考察过拟合时的解决方案及其机制）

• "L2 regularisation penalises large weights, encouraging the model to learn a simpler, more generalisable representation."
• "L1 regularisation drives some weights to exactly zero, performing automatic feature selection."
• "Dropout randomly deactivates neurons during training, forcing the network to learn redundant, distributed representations."
• "This prevents co-adaptation, where specific neurons become overly reliant on each other."
• "Early stopping halts training when validation loss stops improving, preventing the model from memorising training noise."
• "Regularisation constrains model complexity, which helps when the model is overfitting but worsens underfitting."

RNN / LSTM / GRU（循环神经网络 — 考察顺序处理优缺点和梯度消失的解决）

• "RNNs process tokens sequentially, naturally capturing temporal order without additional mechanisms."
• "However, sequential processing prevents parallelisation, making training slow for long sequences."
• "Vanilla RNNs suffer from vanishing gradients because gradients are multiplied through many time steps."
• "LSTM mitigates this by introducing a cell state and gating mechanisms (forget, input, output gates) that control information flow."
• "The forget gate decides what information to discard, while the input gate controls what new information to store."
• "GRU simplifies LSTM by combining the forget and input gates into a single update gate, reducing the number of parameters."

逻辑连接词完整指南（Logic Connectors for Exam Answers）

中国学生最常犯的逻辑问题不是内容错误，而是句子之间缺少逻辑连接。下面的连接词能让你的答案读起来像母语者写的。

因果关系（Cause & Effect）

转折关系（Contrast & Concession）

递进关系（Addition & Elaboration）

总结关系（Conclusion & Summary）

Common Mistakes in English Writing

Power Words for High-Scoring Answers