mlsanityData to 13 Oct 2026

Good Parenting is all you need -- Multi-agentic LLM Hallucination Mitigation

arXiv 2410.14262 ↗First posted 18 Oct 2024cs.CR cs.CLcited by 7

On the map: LLM agents and tool use → Medical and scientific domain agents

Abstract (arXiv)

This study explores the ability of Large Language Model (LLM) agents to detect and correct hallucinations in AI-generated content. A primary agent was tasked with creating a blog about a fictional Danish artist named Flipfloppidy, which was then reviewed by another agent for factual inaccuracies. Most LLMs hallucinated the existence of this artist. Across 4,900 test runs involving various combinations of primary and reviewing agents, advanced AI models such as Llama3-70b and GPT-4 variants demonstrated near-perfect accuracy in identifying hallucinations and successfully revised outputs in 85% to 100% of cases following feedback. These findings underscore the potential of advanced AI models to significantly enhance the accuracy and reliability of generated content, providing a promising approach to improving AI workflow orchestration.

Key facts. Cited by 7 papers in the map (data through 13 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 6 of its 7 references that are in the map. First posted on arXiv 18 Oct 2024.

Builds on

6 of 7 references.

References · 6

  1. 2022Survey of Hallucination in Natural Language Generationcited by 2,068
  2. 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 268
  3. 2023Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedbackcited by 205
  4. 2023The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediationscited by 130
  5. 2023Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approachcited by 38
  6. 2024AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systemscited by 20

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 6; 2026: 1.

7 papers.

  1. 2025A Survey on Trustworthy LLM Agents: Threats and Countermeasurescited by 94
  2. 2025Fully Autonomous AI Agents Should Not be Developedcited by 43
  3. 2025Security of Internet of Agents: Attacks and Countermeasurescited by 17
  4. 2026Agentic Artificial Intelligence (AI): Architectures, Taxonomies, and Evaluation of Large Language Model Agentscited by 14
  5. 2025Hallucination Mitigation using Agentic AI Natural Language-Based Frameworkscited by 12
  6. 2025Layered Chain-of-Thought Prompting for Multi-Agent LLM Systems: A Comprehensive Approach to Explainable Large Language Modelscited by 10
  7. 2025HalluDetect: Detecting, Mitigating, and Benchmarking Hallucinations in Conversational Systems in the Legal Domaincited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions
  2. 2024Towards Detecting LLMs Hallucination via Markov Chain-based Multi-agent Debate Framework
  3. 2026AgentHallu: Benchmarking Automated Hallucination Attribution of LLM-based Agents
  4. 2026MARCH: Multi-Agent Reinforced Self-Check for LLM Hallucination
  5. 2024Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models
  6. 2026Hallucination Detection and Mitigation in Large Language Models
  7. 2026Hallucination Cascade: Analyzing Error Propagation in Multi-Agent LLM Systems
  8. 2025MIRAGE-Bench: LLM Agent is Hallucinating and Where to Find Them