LLM-Based Explanation Interface

• LLM-Based Explanation Interfaces are systems that harness large language models to generate, manage, and present detailed, personalized explanations using interactive dialogues and visualizations.
• They employ advanced prompt engineering methods, including direct prompting and chain-of-thought reasoning, to align model outputs with human-understandable rationales.
• Deployed in fields like education, legal, code QA, and security, these interfaces improve transparency, auditability, and overall explanation effectiveness through measurable performance gains.

A LLM-based Explanation Interface is an end-to-end system or application layer that leverages the generative, reasoning, and dialogue capabilities of LLMs to produce, manage, and present explanations of AI model outputs, internal processes, or domain decisions to human stakeholders through text, visualization, or interactive dialogue. This paradigm spans from post-hoc rationales in black-box prediction to direct, context-aware, and personalized XAI for complex workflows, as observed across domains such as education, recommendation, legal advice, security, and knowledge graph question answering.

1. System Architectures: Modular Layering and Data Flow

Contemporary LLM-based explanation interfaces adhere to a modular architecture—comprising input parsing, context assembly, prompt engineering, inference, post-processing, and interactive presentation. Common components include:

• Backend Analytics/Model Layer: Generates predictions, feature attributions, event logs, or structured outputs. E.g., black-box predictors (Kroeger et al., 2023), Knowledge-Graph QA pipelines (Schiese et al., 20 Aug 2025), or multi-modal perception stacks (Kundu et al., 19 Dec 2025).
• Explanation Engine: Encapsulates prompt construction, context injection (examples, user profile, metadata), and LLM orchestration (single/multi-stage prompt chains), potentially integrating additional XAI toolkits or deterministic analyzers (Pehlke et al., 10 Nov 2025, Wang et al., 23 Jan 2024).
• Intermediate Artifact Management: Stores and logs structured reasoning artifacts, explanations, matrices, payoff tables, or embedding graphs for audit, visualization, and iterative refinement (Pehlke et al., 10 Nov 2025, Yan et al., 24 Jul 2025).
• Interface Layer: Manages user interaction, visualization, explainability modality toggling, and experiment controls, including multi-agent chat, explanation panel selection, and tutorial overlay (Abu-Rasheed et al., 29 Jan 2024, Wang et al., 23 Jan 2024).

Integrative designs pipeline context through these layers, as in educational dashboards parsing event logs to skill-mastery explanations (Deriyeva et al., 11 Nov 2025), or AR systems mapping real-time sensor streams and user profiles to grounded, immediate explanations (Kundu et al., 19 Dec 2025).

2. Explanation Generation Methodologies

The core methodology in LLM-based explanation interfaces centers on prompt engineering—constructing natural-language or structured prompts that steer the LLM or related agents to produce task-aligned explanations:

• Direct Prompting: System-initiated, context-rich prompts with optional few-shot examples guide the LLM to generate instance-specific natural-language rationales, rankings, or recommendations (e.g., model-agnostic feature attribution (Kroeger et al., 2023), code clone explanations (Racharak et al., 26 Sep 2025), knowledge graph data flow (Schiese et al., 20 Aug 2025), personalized educational recommendations (Rahdari et al., 2023)).
• Chain-of-Thought (CoT) and Scaffolded Reasoning: Multi-stage or multi-prompt chains, often incorporating theories of social science or domain expert heuristics, guide the LLM through intermediate reasoning steps or condensed causal attributions (Swamy et al., 12 Sep 2024, Rahdari et al., 2023).
• Contextual Enrichment: Personalization or domain data can be injected at the prompt level, either as aspect-based user embeddings (Rahdari et al., 2023), retrieved legal documents or precedents (Hu et al., 13 Aug 2024), or as real-time detected objects and user context in AR (Kundu et al., 19 Dec 2025).
• Self-Check and Verification Loops: Some systems invoke the LLM post-generation to revalidate explanation plausibility, detect contradictions, or ground outputs against upstream facts, sometimes falling back to deterministic templates upon error (Rahdari et al., 2023).
• Interactive Dialogue and Revision: Conversational frameworks allow follow-up, clarifying or drilling down on explanation steps, and dynamically refining or augmenting outputs in response to user requests (Wang et al., 23 Jan 2024).

Prompt design is systematically optimized along axes of temperature tuning for determinism/variance, context assembly (examples, stepwise guides), and explicit anchoring to available data to maximize faithfulness and minimize hallucination (Schiese et al., 20 Aug 2025, Kundu et al., 19 Dec 2025).

3. Interfaces and Interaction Design Patterns

Presentation and interaction strategies are tailored to both human trust and workflow efficiency:

• Multimodal and Modular Views: Dashboards and interfaces frequently offer side-by-side panels for textual, graphical, hierarchical, and chatbot explanations, with toggles or sliders for end-user customization (Abu-Rasheed et al., 29 Jan 2024).
• Interactive Narratives and Debug Tools: Toolkits support stepwise reasoning review (CoT blocks, program tracing, or graphs), live variable mapping/color coding, error annotation, and provenance linking (Zhou et al., 27 Oct 2025, Wang et al., 23 Jan 2024, Yan et al., 24 Jul 2025).
• Personalization: Role-based or context-aware adaptation, such as user expertise modes (novice/expert), personalized aspect prompts in recommendation, or fine-tuned prompt clauses for past user behavior (Rahdari et al., 2023, Kundu et al., 19 Dec 2025).
• Actionability and Simulation: Especially in education and recommendation, explanations are coupled with actionable next steps, simulated interventions, and feedback aligned with pedagogical best practices (Swamy et al., 12 Sep 2024).
• Auditability and Logging: All explanation artifacts, prompts, and intermediate data are logged for post hoc inspection, transparency, and reproducibility (Pehlke et al., 10 Nov 2025, Fredes et al., 27 Aug 2024).
• Quality Controls: UI overlays and visual cues highlight confidence, uncertainty, or possible hallucination, and allow end users to rate, flag, or request regeneration (Schiese et al., 20 Aug 2025, Abu-Rasheed et al., 29 Jan 2024).

4. Evaluation Methodologies and Metrics

Quantitative and qualitative evaluation is multidimensional, most commonly employing:

• Faithfulness and Plausibility: Consistency with model reasoning or ground-truth explanations, e.g., feature agreement in attributions (FA/RA) (Kroeger et al., 2023), explanation tree similarity scores (MCS, TK) (Yang et al., 8 Dec 2024), or coverage of component logic (Song et al., 3 Jun 2024).
• User-Centered Assessments: Experimental designs range from within-subject educator validations (TOAST, SUS surveys) (Deriyeva et al., 11 Nov 2025, Kundu et al., 19 Dec 2025), to student preference and simulated actionability (Swamy et al., 12 Sep 2024).
• Robustness and Consistency: Verification agents (LLM-based or otherwise) perturb input/context to test explanation stability and report consistency scores (e.g., cosine similarity) (Yan et al., 24 Jul 2025).
• Utility and Actionability: Downstream effectiveness, such as improved learning rates, task completion times, or triage speed, is measured alongside subjective ratings of understandability, satisfaction, trust, and helpfulness (Kundu et al., 19 Dec 2025, Gandhi et al., 4 Feb 2025).
• Latency and Computational Performance: System benchmarks report end-to-end latency, throughput, and resource scaling (see 4.1 in (Gandhi et al., 4 Feb 2025), and performance details in (Kundu et al., 19 Dec 2025)).

Empirical findings routinely demonstrate statistically significant gains of LLM-based explanations over templates or human-written baselines on clarity, trust, and efficiency metrics.

5. Domain-Specific Adaptations and Exemplars

LLM-based explanation interfaces are instantiated across numerous domains with tailored mechanisms:

• Educational Analytics: Skill mastery interpretation and actionable student feedback pipelines, integrating model predictions, XAI attributions, theory-driven selection, and Hattie/Grice-aligned output structuring (Deriyeva et al., 11 Nov 2025, Swamy et al., 12 Sep 2024).
• Code Understanding: Black-box explanation of code clone detectors through in-context LLM guidance, KLN sampling, and code line attribution, with accuracy up to 98% using zero-temperature decoding (Racharak et al., 26 Sep 2025).
• Legal Reasoning: Chain-of-retrieval plus LLM pipelines, with per-sentence legal grounding, user-in-the-loop article selection, and transparent similarity-based mapping of rationales to statutes and cases (Hu et al., 13 Aug 2024).
• Security and Provenance: Multi-stage pipelines that combine statistical anomaly detection, provenance graph correlation, and staged LLM CoT for event narrative generation, supporting kill-chain mapping and precision control (Gandhi et al., 4 Feb 2025).
• Visual Model Explanation: Hierarchical attribute tree construction via LLM-text/image interaction, with tree refinement and correspondence to vision model feature space, supporting plausibility and calibration metrics (Yang et al., 8 Dec 2024).
• Recommendation and AR: Aspect-driven, reasoning-scaffolded personalized explanations in both web and AR contexts; unified LLM modules cover all XAI dimensions, integrating embeddings, context, and prompt adaptation (Rahdari et al., 2023, Kundu et al., 19 Dec 2025).
• Event Sequence Explanation: Latent logic-tree induction from LLM priors, amortized EM via GFlowNets, posterior weighting, and online extraction for symbolic, probabilistic explanations matching domain knowledge (Song et al., 3 Jun 2024).

6. Best Practices, Limitations, and Generalization

Interface design is guided by principles of modularity, prompt transparency, artifact auditability, dynamic user adaptation, and hybrid analysis (Pehlke et al., 10 Nov 2025). Effective systems rely on prompt grounding, minimal hallucination, robust fallback (e.g., template coverage or user feedback), and iterative improvement based on logging and user rating.

Current limitations include context window and computational constraints (especially for few-shot and chain-of-prompt interfaces), occasional hallucination or misattribution, dependency on prompt calibration, and human factors such as variable expertise or cognitive load (Racharak et al., 26 Sep 2025, Zhou et al., 27 Oct 2025). Most studies address single or homogeneous populations; generalization and fairness across broader domains, diverse LLM architectures, and multi-modality remain active research directions (Deriyeva et al., 11 Nov 2025, Kundu et al., 19 Dec 2025).

A notable finding is the superior efficacy of LLM-generated explanations—including post-hoc, dialogue-based, and artifact-driven approaches—over static templates and even expert-crafted baselines, as evidenced by quantitative gains in explanation effectiveness, user trust, and completion rates across controlled experiments (Deriyeva et al., 11 Nov 2025, Kundu et al., 19 Dec 2025, Swamy et al., 12 Sep 2024, Gandhi et al., 4 Feb 2025).

7. Summary Table: Core Functions Across Domains

Domain	Core Mechanism	Notable Feature
Education	Prompt-conditioned, multi-theory CoT	Actionable feedback, student preference >89% (Swamy et al., 12 Sep 2024)
Code QA	In-context few-shot, line attribution	Up to 98% accuracy, explainable code lines (Racharak et al., 26 Sep 2025)
Law/Compliance	Sentence-level retrieval, similarity-mapping, user selection	Interactive credibility, repair (Hu et al., 13 Aug 2024)
Security	Staged LLM CoT, provenance graphs, anomaly detection	Zero FPs in CADETS, 70% cut in triage time (Gandhi et al., 4 Feb 2025)
AR/Recommendation	Unified LLM, aspect/personalization injection	40% faster, high trust/satisfaction (Kundu et al., 19 Dec 2025)

These interfaces consistently demonstrate that careful orchestration of LLM prompting, human-centered UX, and feedback-driven evaluation can meaningfully bridge the interpretability gap for complex AI systems in high-value domains.