Rhetorical Questions in LLM Representations: A Linear Probing Study

1. 1
   Define Your Linguistic Nuance

   Clearly identify the specific linguistic feature (e.g., rhetorical question, sarcasm, irony) you want to investigate within an LLM's representations. Define its characteristics and how it differs from similar constructs.
2. 2
   Prepare a Labeled Dataset

   Collect a dataset of text examples, ensuring it contains instances of your defined linguistic nuance. Manually or programmatically label each example to indicate the presence or absence of the feature.
3. 3
   Extract LLM Embeddings

   Choose a pre-trained Large Language Model (e.g., BERT, RoBERTa, etc.). For each text example in your dataset, use the LLM to extract contextualized embeddings (e.g., the [CLS] token embedding or mean pooling of all tokens).
4. 4
   Train a Linear Classifier

   Using the extracted LLM embeddings as features and your labels as targets, train a simple linear classifier (e.g., Logistic Regression, Linear SVM). This classifier acts as your 'probe' into the LLM's internal state.
5. 5
   Evaluate Probing Performance

   Assess the performance of your linear classifier on a held-out test set. A high classification accuracy indicates that the LLM's embeddings effectively encode the linguistic feature, making it linearly separable.
6. 6
   Interpret and Apply Findings

   Analyze the results to understand how well the LLM captures your target nuance. Use these insights to inform prompt engineering, fine-tuning strategies, or to develop more context-aware AI applications that leverage the LLM's internal representations.

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# --- Simulate LLM embeddings and labels for a linguistic feature (e.g., rhetorical questions) ---
# In a real scenario, you would replace these with actual data from an LLM and your labeled dataset.
num_samples = 100
embedding_dim = 768 # Common embedding dimension for models like BERT
X_embeddings = np.random.rand(num_samples, embedding_dim) # Simulated LLM embeddings
y_labels = np.random.randint(0, 2, num_samples)      # Simulated binary labels (0: no feature, 1: feature present)

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X_embeddings, y_labels, test_size=0.2, random_state=42)

# --- Perform Linear Probing: Train a simple linear classifier ---
classifier = LogisticRegression(max_iter=1000) # Use max_iter=1000 for better convergence
classifier.fit(X_train, y_train)

# --- Evaluate the classifier's performance ---
y_pred = classifier.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)

print(f"Linear Probing Accuracy (simulated data): {accuracy:.2f}")
print("\nThis accuracy indicates how well a simple linear model can 'read' the linguistic feature from the LLM's embeddings.")
print("Higher accuracy suggests the LLM effectively encodes the feature in a linearly separable way.")

• Paperarxiv.org