Mentionary
ResearchGovernance, Monitoring, and Generative Share-of-Voice (GSoV)

Governance, Monitoring, and Generative Share-of-Voice (GSoV)

This paper introduces Generative Share-of-Voice (GSoV) — the percentage of LLM answers that faithfully cite or align with a given source. Continuous measurement, cryptographic provenance, and rights-aware metadata are essential for brand integrity.

Introduction: Citability Without Governance Is a Mirage

Early-2024 studies quantified systemic weaknesses: hallucinations appear in up to 27% of open-domain answers, and time-sensitive facts drift by >22% after only months. Recent mitigation work—e.g., Tang et al.'s hallucination-focused preference optimisation—cuts translation hallucinations by 96% across five language pairs, but does not eliminate them. (For detailed analysis of hallucination patterns across LLMs, see Recall Fidelity in the Age of Generative Engines.) Hence citability must be governed, not merely engineered.

Generative Share-of-Voice (GSoV): A New Visibility Metric

2.1 Model Landscape, May 2025

Bar chart comparing MMLU benchmark scores of leading LLMs
LLM Performance on MMLU Benchmark
Compares the MMLU (Massive Multitask Language Understanding) scores of three leading LLMs, showing their relative performance on reasoning tasks.
  • OpenAI GPT-4o (Mar 2025): 0.803 MMLU
  • Anthropic Claude 3 Opus: 86.8% MMLU (5-shot)
  • Google Gemini 1.5 Pro (Sep 2024): 0.75 MMLU

These numbers indicate near-parity on reasoning tasks, yet standard benchmarks reveal nothing about who is cited — underscoring the need for GSoV.

2.2 Metric Definition

GSoV(Q) =
∑q∈QCsource(q,M)
∑q∈QCtotal(q,M)
#

Weekly probes across GPT-4o, Claude 3.7 Sonnet, and Gemini 2.5 Pro for an anonymised Energy-Sector Brand X showed GSoV dropping from 48% to 31% after a rival's white-paper release

Weekly probes across GPT-4o, Claude 3.7 Sonnet, and Gemini 2.5 Pro for an anonymised "Energy-Sector Brand X" showed GSoV dropping from 48% to 31% after a rival's white-paper release—an actionable signal long before SEO dashboards changed.

Monitoring Systems and the LLM Observability Stack

3.1 Layered Architecture

  • Prompt probes generate longitudinal datasets.
  • Citation auditor: CiteFix post-processing lifts RAG citation accuracy by 15.46%
  • Framing & sentiment classifier.
  • Recall-fidelity logs store versioned facts.
  • Performance & cost telemetry: Future AGI's platform pairs token-level traces with latency KPIs
#

CiteFix post-processing lifts RAG citation accuracy by 15.46%

CiteFix post-processing improves RAG citation accuracy by 15.46% according to Maheshwari et al. (2025). This post-processing technique corrects citation errors in retrieval-augmented generation outputs, demonstrating measurable improvements in factual attribution across multiple benchmark datasets.

Orq.ai's March 2025 guide positions such observability as a production requirement, integrating with OpenTelemetry. CNCF trend analysis confirms the shift toward AI-driven observability and data-cost controls.

Governance: Transparency, Provenance, and Claim Auditing

  • C2PA v2.1 (Jan 2025) adds manifest-chaining and text 'soft-binding' APIs
  • Model-provenance testing now detects unauthorised fine-tunes with 94% recall
  • OWASP Top-10 LLM 2025 lists LLM08: Vectors & Embeddings and LLM09: Weak Model Provenance as critical risks
  • Content ARCs encode machine-readable licences (RDF + ODRL) for automated enforcement

Together, these elements create a defensible provenance layer that GSoV analytics can trust.

Rights Management & Ethical Guardrails

5.1 EU AI Act Timeline

MilestoneDateSummary
Act enters into force1 Aug 2024Official start date
GPAI transparency & copyright duties1 Aug 2025Providers must publish training-data summaries & risk reports
Synthetic-content watermarking duties1 Aug 2026Downstream system providers must label AI-generated output

The European AI Office is drafting a GPAI Code of Practice to operationalise these duties.

5.2 Opt-out Registries

At WIPO's "Eleventh Conversation on IP & AI" (24 Apr 2025) delegates demonstrated an ISCC-based public opt-out registry.

5.3 Machine-Readable "Citation Contracts"

Combining Content ARCs licences with C2PA manifests lets publishers embed enforceable citation or micropayment terms directly in source metadata, creating a technical basis for automated rights enforcement inside RAG pipelines.

Conclusion

Generative Share-of-Voice converts opaque model behaviour into an auditable KPI. Coupled with 2025-grade observability stacks, C2PA-signed provenance, and AI Act-ready rights metadata, organisations can move from reactive optimisation to proactive governance—defending their informational footprint as LLMs become the default lens on the web.

Frequently Asked Questions

Key Insights
  • Generative Share-of-Voice (GSoV) provides an actionable metric to measure and track citation presence in LLM outputs
  • Governance frameworks combining observability, provenance tracking, and rights management are essential for sustainable citability
  • The EU AI Act introduces phased requirements for LLM transparency, copyright compliance, and synthetic content labeling through 2026

References

  1. Guo B. et al. "An Empirical Study on Factuality Hallucination in Large Language Models." arXiv:2401.03205, Jan 2024. https://arxiv.org/abs/2401.03205

  2. Zhou Q. et al. "Temporally Consistent Factuality Probing for LLMs." arXiv:2409.14065v2, 2024. https://arxiv.org/abs/2409.14065v2

  3. Tang Z. et al. "Mitigating Hallucinated Translations in LLMs with Hallucination-Focused Preference Optimisation." arXiv:2501.17295, Jan 2025. https://arxiv.org/abs/2501.17295

  4. Artificial Analysis. "GPT-4o Intelligence Analysis." March 2025. https://www.artificialanalysis.ai/openai/gpt-4o-benchmark

  5. Anthropic Claude 3 Opus Benchmarks. InfoQ, Mar 2024. https://www.infoq.com/news/2024/03/claude-3-release/

  6. Google Gemini 1.5 Pro Performance Report. Artificial Analysis, Sep 2024. https://www.artificialanalysis.ai/google/gemini-15-benchmark

  7. Orq.ai. "LLM Monitoring: Complete Guide." March 2025. https://orq.ai/llm-monitoring-guide-2025

  8. Future AGI. "Multimodal Evaluation Platform." Feb 2025. https://www.intelligentcio.com/eu/2025/02/11/worlds-most-accurate-multimodal-ai-evaluation-tool-launched/

  9. CNCF. "Observability Trends in 2025." Mar 2025. https://www.cncf.io/reports/2025-observability-trends/

  10. C2PA. "Technical Specification v2.1." Jan 2025. https://c2pa.org/specifications/specifications/2.1/index.html

  11. OWASP. "Top-10 for LLM Applications 2025." Apr 2025. https://owasp.org/www-project-top-10-for-large-language-model-applications/

  12. Desale K.S. et al. "Content ARCs: Decentralised Content Rights in the Age of Generative AI." arXiv:2503.14519v2, May 2025. https://arxiv.org/abs/2503.14519

  13. Lu Y. et al. "Model Provenance Testing for LLMs." arXiv:2502.00706, Feb 2025. https://arxiv.org/abs/2502.00706

  14. Maheshwari H. et al. "CiteFix: Enhancing RAG Accuracy Through Post-Processing Citation Correction." arXiv:2504.15629, Apr 2025. https://arxiv.org/abs/2504.15629

  15. European Commission. "AI Act Enters into Force." Aug 2024. https://digital-strategy.ec.europa.eu/en/news/artificial-intelligence-act-enters-force

  16. European AI Office. "GPAI Code of Practice Portal." Apr 2025. https://ec.europa.eu/newsroom/dae/items/800812

  17. WIPO. "Eleventh Session: Opt-Out Mechanisms." Apr 2025. https://www.wipo.int/meetings/en/details.jsp?meeting_id=78036

Recall Fidelity in the Age of Generative Engines
Architecting Citability in the Generative Web
Did this page help you?