Reducing Hallucinations in Large Language Models Through Integrated Self-Verification and Retrieval-Augmented Generation
cs.SE, cs.AI
Submitted: 2026-08-12
Updated: 2026-08-12
Journal ref: Proceedings of the ASME 2025 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE2025), Anaheim, CA, USA, August 17-20, 2025, Paper No. DETC2025-169730, V02BT02A032
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
- On Faithfulness and Factuality in Abstractive Summarization
- A Survey of Hallucination in Large Foundation Models
- How Can Large Language Models Help Humans in Design and Manufacturing?
- Chain-of-Verification Reduces Hallucination in Large Language Models
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models
- A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
- Training language models to follow instructions with human feedback
- Deep Bidirectional Language-Knowledge Graph Pretraining
- Billion-scale similarity search with GPUs
- Mistral 7B
- Llama 2: Open Foundation and Fine-Tuned Chat Models
- LoRA: Low-Rank Adaptation of Large Language Models
- Decoupled Weight Decay Regularization
- FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
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