Having explored various interpretability techniques in theory, this section provides a hands-on walkthrough of applying feature attribution to understand why a Large Language Model generated a specific output. This is particularly useful when analyzing potentially problematic, biased, or unexpected responses, helping to diagnose if the model relied on undesirable patterns in the input or its internal representations.

We'll use a concrete example, calculate attribution scores indicating the importance of input tokens for the generated output, and visualize these scores to facilitate interpretation. This process helps bridge the gap between abstract model behavior and concrete safety assessments.

Setting Up the Scenario

Assume we have a language model tasked with generating text. We provide an input prompt and receive an output that raises a minor concern, perhaps hinting at a stereotype or an unexpected focus. Our goal is to use attribution to pinpoint which parts of the input most influenced the concerning part of the output.

For this exercise, let's imagine our model generated the following:

Input Prompt: "The software engineer presented their work to the team."
Model Output: "... He explained the complex algorithm clearly."

While not overtly harmful, the use of "He" assumes the engineer's gender. We want to understand if the input "software engineer" strongly influenced the model's choice of this pronoun.

We will use a hypothetical setup involving the Hugging Face transformers library and an attribution library like Captum or transformers-interpret. The specific implementation details can vary depending on the exact model architecture and library, but the core concepts remain the same.

Choosing an Attribution Method

Several attribution methods exist, such as Integrated Gradients, DeepLIFT, SHAP, or simply using attention weights. For this practical, let's conceptually use Integrated Gradients (IG). IG is a well-regarded method that attributes the prediction to input features by integrating gradients along a path from a baseline input (e.g., all padding tokens) to the actual input. It helps identify which input tokens were most influential for a specific output token or the overall prediction score.

Calculating Attributions

The typical workflow involves these steps:

Load Model and Tokenizer: Obtain your pre-trained model and corresponding tokenizer.
Prepare Input/Output: Tokenize the input prompt and identify the target output token(s) you want to analyze (e.g., the token ID for "He").
Instantiate Explainer: Initialize the attribution algorithm (e.g., LayerIntegratedGradients from Captum or a similar tool) with your model.
Compute Attributions: Run the attribution calculation, providing the input tokens, the baseline, and the target output index. This computes a score for each input token reflecting its contribution to the target output.
Aggregate Scores (Optional): Often, scores for subword tokens need to be aggregated back to the word level for easier interpretation.

Let's illustrate with pseudocode resembling a typical library usage:

# Assume model, tokenizer are loaded
# Assume attribution_library provides IntegratedGradientsExplainer

from transformers import AutoTokenizer, AutoModelForCausalLM
# Placeholder - replace with actual model loading
# model = AutoModelForCausalLM.from_pretrained(...)
# tokenizer = AutoTokenizer.from_pretrained(...)

# 1. Input and Output
input_text = "The software engineer presented their work to the team."
output_text = " He explained the complex algorithm clearly." # Focus on ' He'

# 2. Tokenize (simplified example)
input_tokens = tokenizer.tokenize(input_text)
# Assume output starts with token ' He' at index 0 after generation starts
target_output_index = 0 # Index of the token we want to explain (' He')

# Simplified representation of getting internal model inputs/outputs needed for attribution
# This step heavily depends on the specific library and model
# model_inputs = tokenizer(input_text, return_tensors="pt")
# outputs = model(**model_inputs) # Get model outputs

# 3. Instantiate Explainer (Conceptual)
# explainer = attribution_library.IntegratedGradientsExplainer(model) # Needs actual model callable

# 4. Compute Attributions (Conceptual)
# This step requires providing correctly formatted inputs, baselines,
# and specifying the target output neuron or index.
# attribution_scores = explainer.attribute(
#     inputs=model_inputs['input_ids'],
#     baselines=baseline_input_ids, # e.g., tensor of padding IDs
#     target=target_output_index, # Target the specific output token
#     # Additional args depending on library and method
# )

# Placeholder attribution scores for illustration
# In reality, these would be computed by the library
# Dimensions: [batch_size, sequence_length]
attribution_scores = [[0.1, 0.8, 0.7, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1]]
input_tokens = ['The', 'software', 'engineer', 'presented', 'their', 'work', 'to', 'the', 'team', '.'] # Assume tokenization matches scores length

# 5. Aggregate (if needed) - we'll assume scores are per token here
word_attributions = list(zip(input_tokens, attribution_scores[0]))

print("Word Attributions for predicting ' He':")
for word, score in word_attributions:
    print(f"- {word}: {score:.2f}")

This might produce output like:

Word Attributions for predicting ' He':
- The: 0.10
- software: 0.80
- engineer: 0.70
- presented: 0.20
- their: 0.10
- work: 0.10
- to: 0.10
- the: 0.10
- team: 0.10
- .: 0.10

Visualizing and Interpreting the Results

Raw scores can be hard to parse. Visualization makes patterns much clearer. A heatmap is effective for showing the importance of each input token.

Attribution scores visualized as a heatmap. Darker blue indicates higher importance of the input token (x-axis) for generating the target output token (' He').

Interpretation:

Looking at the hypothetical scores and the heatmap, we observe:

The tokens "software" and "engineer" have significantly higher attribution scores (0.80 and 0.70) compared to other input tokens.
This suggests the model's prediction of "He" was most influenced by the presence of the job title "software engineer" in the input.
This provides evidence supporting the hypothesis that the model might harbor a bias associating this profession predominantly with males. It didn't strongly consider the neutral pronoun "their" that was also present in the input.

This insight is valuable for safety and fairness analysis. It moves beyond simply observing the output to understanding why the model behaved that way, pointing towards potential biases learned during training that might need mitigation through techniques like data augmentation, debiasing methods, or targeted fine-tuning discussed elsewhere in this course.

Limitations and Considerations

It's important to remember that attribution methods provide explanations, but they have limitations:

Approximations: Most methods are approximations of true feature importance.
Method Dependence: Different attribution methods might yield slightly different results or highlight different aspects.
Computational Cost: Calculating attributions, especially for large models and long sequences, can be computationally intensive.
Interpretation: While visualizations help, interpreting complex interactions or subtle influences can still be challenging.

Despite these points, attribution analysis is a powerful tool in the LLM safety toolkit. It allows developers and researchers to perform targeted debugging of model behavior, identify potential sources of bias or unsafe generations, and gain confidence in the model's reasoning process, particularly when evaluating its responses in safety-critical contexts. The next step after such an analysis might involve comparing results with other methods, testing counterfactuals (e.g., changing "software engineer" to "nurse" and observing attribution changes), or feeding these findings into model editing or retraining efforts.