Evaluating LLM Semantic Volatility & Alignment Biases

1. Introduction & Purpose

The EU Digital Services Act (DSA) mandates strict risk-mitigation and transparent content moderation frameworks, significantly elevating the operational role of certified “Trusted Flaggers.” Under Article 22, these entities require highly reliable, scalable, and legally defensible tooling to submit high-volume content flags to Very Large Online Platforms (VLOPs).

This paper presents a data-driven black-box audit of Large Language Models (LLMs) used within automated trust and safety pipelines. It bridges the gap between machine learning performance and regulatory compliance, presenting a benchmarking pipeline that tracks semantic volatility and systemic alignment bias across state-of-the-art open-weight LLMs, demonstrating how these operational constraints directly inform the 2Leaf Trusted Flagger terminal architecture.

2. Tenets / Guiding Principles

The core operational thesis of 2Leaf is the engineering of a unified terminal constructed explicitly around a scannable hierarchy of Mission, Concept, and Actionable Contact. To satisfy the legal standard of an Article 22 submission, every automated alert must be accompanied by an explicit, transparent rationale detailing why the content violates specific legal or platform terms.

Our infrastructure demands that any backend LLM component must not only be precise but structurally stable over time. If an automated pipeline exhibits arbitrary decision shifts on borderline toxic speech, the platform's regulatory defensibility collapses. Consequently, assessing the precise nature of model reasoning and model stability across multi-run distributions is a primary prerequisite for scalable operations.

3. State of the Landscape & Empirical Setup

To evaluate model performance on nuanced, legally sensitive text, we utilize a testing suite derived from the HateXplain dataset, categorizing content into Hate Speech, Offensive, and Normal text.

The MPAC Benchmark Suite

Using the Multi-Provider Agent Consensus (MPAC) tool, 2Leaf conducted a black-box behavioral audit across three frontier open-weight topologies:

• Google Gemma 4 31B IT: 31B parameters, Dense Architecture.
• DeepSeek V4 Flash: 304B total parameters (13B active), Mixture-of-Experts (MoE) Architecture.
• Mistral Small 2603: 119B parameters, Mixture-of-Experts (MoE) Architecture.

Instead of relying on inaccessible model internals, we established an empirical, output-based behavioral auditing taxonomy evaluating 1,923 contested posts through 5 identical longitudinal runs:

1. Traditional Macro NLP Classification Tracking: Measuring raw precision, recall, and accuracy configurations.
2. Deterministic Volatility Analysis: Running identical pipelines while locking hyperparameter temperature to a deterministic baseline (T = 0.0) to isolate variations arising purely from batching and backend expert routing.
3. Inter-Run Reliability Auditing: Computing Cohen's Kappa coefficients (κ) across subsequent sweeps to mathematically quantify boundary stability.

4. Findings & Strategic Insights

4.2 Algorithmic Failure Characteristics

Relying exclusively on macro-level accuracy metrics is structurally deceptive. Black-box evaluation exposes distinct structural policy blind spots across the selected architectures:

• Gemma's Structural Blindness (56.43% Mean Accuracy): Exhibits severe African American Vernacular English (AAVE) and coded internet slur deficits. It completely failed to decode terms like “mussie” or “odumba” if lacking explicit dictionary swears.
• DeepSeek's Hyper-Vigilance (46.98% Mean Accuracy): An unmediated “over-flagger” with exceptional hate capture (94.27% accuracy in that class) but catastrophic false-positive generation (50% of its hate speech flags are benign/slang), alongside a narrative detachment blind spot.
• Mistral's Safe-Harboring (37.78% Mean Accuracy): Driven by rigid corporate alignment guardrails, its safety logic heavily defaults to the “Offensive” classification, under-penalizing severe hate speech and generating 516 to 687 erroneous flags per sweep.

5. Execution: The 2Leaf Terminal Guardrails

The presence of a ~40% threshold drift in Mistral and persistent systemic failure clusters prove that raw LLM layers cannot support a compliant DSA Trusted Flagger pipeline. The 2Leaf Terminal implements dual-layer guardrails:

6. Conclusion & Next Steps

At a deterministic temperature (T = 0.0), Mixture-of-Experts routing dynamics introduce up to 43.1% classification drift on borderline toxic content. For certified DSA Trusted Flaggers, navigating this algorithmic volatility is mandatory for maintaining operational standing.

Integrating the ERASER Benchmark Suite: As 2Leaf scales into locally hosted open-weight instances, our research will compute token-level feature attribution metrics via the ERASER suite:

• Plausibility (Token-Discrete F1 & IoU F1): Quantifying how closely model attention fields map to human rationale masks.
• Faithfulness (Comprehensiveness & Sufficiency): Computing prediction confidence drops when rationale tokens are isolated or stripped.