mlsanityData to 7 Oct 2026

Loki: An Open-Source Tool for Fact Verification

arXiv 2410.01794 ↗First posted 2 Oct 2024cs.CLcited by 16

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Abstract (arXiv)

We introduce Loki, an open-source tool designed to address the growing problem of misinformation. Loki adopts a human-centered approach, striking a balance between the quality of fact-checking and the cost of human involvement. It decomposes the fact-checking task into a five-step pipeline: breaking down long texts into individual claims, assessing their check-worthiness, generating queries, retrieving evidence, and verifying the claims. Instead of fully automating the claim verification process, Loki provides essential information at each step to assist human judgment, especially for general users such as journalists and content moderators. Moreover, it has been optimized for latency, robustness, and cost efficiency at a commercially usable level. Loki is released under an MIT license and is available on GitHub. We also provide a video presenting the system and its capabilities.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 8 of its 19 references that are in the map. First posted on arXiv 2 Oct 2024.

Builds on

8 of 19 references.

References · 8

  1. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
  2. 2023FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarioscited by 247
  3. 2024Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantificationcited by 159
  4. 2024Long-form factuality in large language modelscited by 146
  5. 2023On the Risk of Misinformation Pollution with Large Language Modelscited by 124
  6. 2023Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkerscited by 86
  7. 2024Factuality of Large Language Models: A Surveycited by 64
  8. 2024OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMscited by 28

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 8; 2026: 7.

16 papers.

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  1. 2024FIRE: Fact-checking with Iterative Retrieval and Verificationcited by 39
  2. 2025ADMIT: Few-shot Knowledge Poisoning Attacks on RAG-based Fact Checkingcited by 9
  3. 2026DeepResearch-9K: A Challenging Benchmark Dataset of Deep-Research Agentcited by 8
  4. 2026VeriTaS: The First Dynamic Benchmark for Multimodal Automated Fact-Checkingcited by 5
  5. 2025UrduFactCheck: An Agentic Fact-Checking Framework for Urdu with Evidence Boosting and Benchmarkingcited by 4
  6. 2025Fact2Fiction: Targeted Poisoning Attack to Agentic Fact-checking Systemcited by 4
  7. 2026Proof-of-Guardrail in AI Agents and What (Not) to Trust from Itcited by 4
  8. 2025Fact in Fragments: Deconstructing Complex Claims via LLM-based Atomic Fact Extraction and Verificationcited by 2
  9. 2025ShortCheck: Checkworthiness Detection of Multilingual Short-Form Videoscited by 1
  10. 2025Can LLMs extract human-like fine-grained evidence for evidence-based fact-checking?cited by 1
  11. 2025Examining the Metrics for Document-Level Claim Extraction in Czech and Slovakcited by 0
  12. 2025TrackList: Tracing Back Query Linguistic Diversity for Head and Tail Knowledge in Open Large Language Modelscited by 0
4 more
  1. 2026TAIGR: Towards Modeling Influencer Content on Social Media via Structured, Pragmatic Inferencecited by 0
  2. 2026ViX-Ray: A Vietnamese Chest X-Ray Dataset for Vision-Language Modelscited by 0
  3. 2026CAAFC: Chronological Actionable Automated Fact-Checker for misinformation / non-factual hallucination detection and correctioncited by 0
  4. 2026WildSEEK: Evaluating Language Models for Information-Seekingcited by 0