LLM-Driven Interpretability Pipeline

• LLM-driven interpretability pipelines are modular systems that use language models to generate explicit, audit-ready feedback for data annotation and decision support.
• They integrate multiple stages—from feature extraction and prompt generation to statistical analysis—to provide transparent and measurable interpretability metrics.
• Empirical evaluations show that prompt engineering significantly influences feedback quality, thereby improving reliability and accessibility in diverse AI workflows.

A LLM-driven interpretability pipeline is a modular system architecture that leverages LLMs to generate, structure, and evaluate human-interpretable feedback, explanations, or rules connected to both human annotation processes and downstream automated decision tasks. The core function is to externalize reasoning or provide explicit, audit-ready attributions for data labeling, problem-solving, or decision support tasks across diverse domains such as computer vision, education, and domain-specific annotation. Recent research demonstrates both the technical feasibility and empirical benefits of these pipelines for improving accessibility, reliability, and transparency in data-centric AI workflows (Li et al., 26 May 2025).

1. System Architecture and Workflow

The canonical LLM-driven interpretability pipeline is composed of several tightly integrated modules, which process user input, extract and featurize relevant artifacts, generate structured prompts, solicit and evaluate LLM feedback, and compute measurable interpretability metrics. The following high-level structure is representative of an advanced pipeline in image annotation with virtual sketch-based assistants (Li et al., 26 May 2025):

1. User Interface (UI):
   • Inputs: Raw image, free-form sketch annotations via stylus/tablet.
   • Outputs: Visual rendering of the annotation and LLM-generated, context-sensitive feedback.
2. Sketch Feature Extractor:
   • Receives normalized, resampled 2D point lists per sketch.
   • Computes a vector of sketch recognition (SR) features (e.g., stroke geometry, bounding box, densities) using formulas from Rubine and Long.
3. Prompt Generator:
   • Assembles prompts that combine task description, output expectations, optional evaluation rubrics, perfect-example references, and the sketched image.
   • Supports multiple strategies: Zero-shot (basic, rubric), Few-shot (basic, rubric).
4. LLM Feedback Engine:
   • Dispatches prompts and image context to an LLM backend (such as GPT-4).
   • Returns terse, natural-language feedback (up to 3 sentences) focused on annotation quality and guidance.
5. Interpretability Analyzer:
   • Inputs: LLM feedback, user queries, context, and ground-truth grading.
   • Evaluates interpretability using specific metrics: context precision, faithfulness, and relevancy (via RAGAS).
   • Performs statistical and correlation analyses to link human gesture characteristics to feedback quality.
6. Results Visualization and Reporting:
   • UI layer displays interpretability metrics and guidance for further annotation improvement.

This modular design enables continuous delivery of interpretable feedback and supports detailed statistical evaluation of LLM reasoning with respect to both prompt content and human-generated features.

2. Feature Engineering and Mathematical Formulation

The interpretability pipeline's core involves extracting mathematically defined feature vectors from user input, encoding sketch geometry through a set of domain-specific formulas:

• Sketch Features (SR):
  • $R_1 = \cos \theta_0$, $R_2 = \sin \theta_0$, with $\theta_0 = \angle(p_1 - p_0)$.
  • $R_3 = \lVert d \rVert = \sqrt{b_w^2 + b_h^2}$.
  • $R_4 = \operatorname{atan2}(b_h, b_w)$.
  • $R_5 = \lVert p_n - p_0 \rVert$, ...
  • Compound features such as $L_{14}$ (average rotation), $L_{15}$ (density), and $L_{17}$ (openness) are derived.
  • $\Delta_\text{stroke\_dist}$ captures inter-stroke centroid separation.
• Evaluation Metrics:
  • $context\_precision = |A \cap C| / |A|$ (overlap between LLM answer and prompt context).
  • $faithfulness = score_{LLM}(A \mid C) \in [0,1]$ (how well LLM claims are supported).
  • $relevancy = sim_{semantic}(A, Q) \in [0,1]$ (answer-question semantic similarity).

This mathematical encoding ensures reproducibility and enables subsequent hypothesis testing and robust comparison of annotation behaviors and LLM response characteristics.

3. Prompt Engineering and Strategy Influence

The design of LLM prompt templates and context inclusion exerts significant control over feedback interpretability:

• Template Variants:
  • Zero-shot Basic: Task description, output expectations, and a single image.
  • Zero-shot Rubric: Adds a 4-item rubric focused on boundary, enclosure, gap, and overlay to Basic.
  • Few-shot Basic: Infuses a “perfect example” sketch alongside the basic instructions.
  • Few-shot Rubric: Includes rubric and perfect-example.

Prompt-type selection significantly impacts evaluation metrics:

• Few-shot Basic yields a median context_precision increase of ≈0.05 compared to Few-shot Rubric.
• Basic templates (zero/few-shot) achieve higher faithfulness and relevancy scores than rubric-enforced prompts (median Δ≈0.1 for faithfulness, Dunn’s p < .05 for relevancy).

These findings highlight the dominance of prompt engineering over geometric Sketch features in determining the interpretability and quality of LLM feedback (Li et al., 26 May 2025).

4. Interpretability Analysis and Statistical Methodology

Analysis modules quantitatively assess the linkage between user input characteristics and LLM feedback reliability:

• Metric correlation:

Distance correlation $dCor(f_j, M_k)$ is computed for each feature-metric pair, revealing weak predictive power for geometry (e.g., $|dCor| < 0.3$ and $R_3$ vs. relevancy $dCor ≈ 0.12$).

• Statistical testing:

Shapiro–Wilk for normality, Kruskal–Wallis $\chi^2$ for group differences, and Dunn’s post hoc corrections quantify the significance of strategy impacts.

Descriptive statistics for experiment results:

Prompt Type	context_precision	faithfulness	relevancy
ZS-Basic	0.88	0.75	0.82
ZS-Rubric	0.80	0.60	0.70
FS-Basic	0.93	0.78	0.85
FS-Rubric	0.85	0.65	0.72

Significant differences across prompt types confirm that interpretability is more responsive to prompt structure than to the micro-geometry of sketches.

5. Implementation, Representative Workflow, and Example

The full operation of the pipeline is captured by the following pseudocode:

for each image I in dataset: strokes = GPSR_resample(contours(I)) features_f = compute_SR_features(strokes) for prompt_type in {ZS-Basic, ..., FS-Rubric}: P = render_prompt(prompt_type, I, example(I), features_f) A = GPT4.generate(P) M = RAGAS.evaluate(Q, C=P+I, A, ground_truth(I)) record features_f, M, prompt_type

A prototypical example:

• User sketches the outline of a human figure.
• LLM, prompted in FS-Basic mode, returns: “Your red sketch neatly encloses the person but leaves a small gap at the feet. Try tracing closer to the mask boundary.”
• The interpretability analyzer assigns $M = \{0.95, 0.80, 0.90\}$ (context_precision, faithfulness, relevancy), visualized via bar chart.

This closed loop supports immediate user feedback, metric display, and iterative annotation quality improvement.

6. Insights, Limitations, and Extensions

Empirical insights and domain adaptation points include:

• Sketch geometry exerts only minor influence on LLM interpretability metrics; prompt design is the primary driver.
• Rubric constraints can restrict LLM directness, thus sometimes reducing truthfulness and clarity.
• The modular pipeline model enables adaptation:
  • Medical imaging: Swap SR features for organ-shape quantifiers, adopt clinical rubrics, and use domain-validated LLM scoring.
  • Robotics: Map sketch extractor to gesture/trajectory encoding; analyze path safety/fidelity with custom interpretability analyzers.

Limitations include the weak predictive value of most geometric features and the potential reduction in LLM feedback clarity when rubric constraints are enforced. Expanding to more diverse domains will require careful selection and engineering of feature extractors, rubrics, and interpretability metrics.

7. Significance and Future Directions

LLM-driven interpretability pipelines establish a technological foundation for embedding robust, scalable, and user-accessible interpretability into annotation and decision-support systems. They enable empirical assessment of both human and model behavior, support rigorous statistical evaluation, and facilitate process transparency via explicit, machine-auditable metrics. Future progress will likely focus on generalization to new application domains, integration with domain-specific lexicons, and the development of more adaptive and user-responsive prompting strategies that balance structure, clarity, and output fidelity (Li et al., 26 May 2025).