Mitigating LLM Hallucinations through Domain-Grounded Tiered Retrieval

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References (35)

[1]
Large Language Models Hallucination: A Comprehensive Survey
2025Aisha Alansari, H. Luqman
[2]
Why Language Models Hallucinate
2025A. Kalai, Ofir Nachum et al.
[3]
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation
2025Jiashuo Sun, Xianrui Zhong et al.
[4]
The Distracting Effect: Understanding Irrelevant Passages in RAG
2025Chen Amiraz, Florin Cuconasu et al.
[5]
Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning
2025Yiqun Chen, Ling Yan et al.
[6]
Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective
2024Ernests Lavrinovics, Russa Biswas et al.
[7]
LLMs: A Game-Changer for Software Engineers?
2024Md. Asraful Haque
[8]
Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models
2024Liam Barkley, Brink van der Merwe
[9]
LLMs Will Always Hallucinate, and We Need to Live With This
2024Sourav Banerjee, Ayushi Agarwal et al.
[10]
Detecting hallucinations in large language models using semantic entropy
2024Sebastian Farquhar, Jannik Kossen et al.
[11]
MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents
2024Liyan Tang, Philippe Laban et al.
[12]
Reducing hallucination in structured outputs via Retrieval-Augmented Generation
2024Patrice Bechard, Orlando Marquez Ayala
[13]
Exploring ChatGPT and its impact on society
2024Md. Asraful Haque, Shuai Li
[14]
Corrective Retrieval Augmented Generation
2024Shi-Qi Yan, Jia-Chen Gu et al.
[15]
Hallucination is Inevitable: An Innate Limitation of Large Language Models
2024Ziwei Xu, Sanjay Jain et al.
[16]
RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
2023Yuanhao Wu, Juno Zhu et al.
[17]
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
2023Lei Huang, Weijiang Yu et al.
[18]
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
2023Akari Asai, Zeqiu Wu et al.
[19]
The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations
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Showing 20 of 35 references

Founder's Pitch

"A domain-grounded retrieval system that enhances the reliability of LLMs by mitigating hallucinations through a structured verification process."

LLM ReliabilityScore: 7View PDF ↗

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0-10 scale

High Potential

2/4 signals

5

Quick Build

3/4 signals

7.5

Series A Potential

1/4 signals

2.5

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