mlsanityData to 14 Oct 2026

Large Language Model Sourcing: A Survey

arXiv 2510.10161 ↗First posted 11 Oct 2025cs.CL cs.AIcited by 5

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Abstract (arXiv)

Due to the black-box nature of large language models (LLMs) and the realism of their generated content, issues such as hallucinations, bias, unfairness, and copyright infringement have become significant. In this context, sourcing information from multiple perspectives is essential. This survey presents a systematic investigation organized around four interrelated dimensions: Model Sourcing, Model Structure Sourcing, Training Data Sourcing, and External Data Sourcing. Moreover, a unified dual-paradigm taxonomy is proposed that classifies existing sourcing methods into prior-based (proactive traceability embedding) and posterior-based (retrospective inference) approaches. Traceability across these dimensions enhances the transparency, accountability, and trustworthiness of LLMs deployment in real-world applications.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 147 of its 190 references that are in the map. First posted on arXiv 11 Oct 2025.

Builds on

147 of 190 references.

References · 147

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50 most cited of 147 All 147 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026SkillAttack: Automated Red Teaming of Agent Skills through Attack Path Refinementcited by 30
  2. 2026Circular Reasoning: Understanding Self-Reinforcing Loops in Large Reasoning Modelscited by 5
  3. 2026Tracing the Data Trail: A Survey of Data Provenance, Transparency and Traceability in LLMscited by 3
  4. 2026PaperTrail: A Claim-Evidence Interface for Grounding Provenance in LLM-based Scholarly Q&Acited by 2
  5. 2026Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMscited by 2