Fact-Aware RAG and NLI Verification: A New Approach to Reducing Hallucinations in Large Language Models

This article presents an AI system proposed by an open-source project that integrates Retrieval-Augmented Generation (RAG) and Natural Language Inference (NLI) verification. Using a three-layer architecture—semantic retrieval, context generation, and fact consistency verification—it effectively reduces hallucinations in Large Language Models (LLMs), offering a new direction for building more reliable AI systems.

LLMs are essentially probabilistic text generators that predict the next word based on learned language patterns but do not truly understand content. When training data coverage is insufficient or information is ambiguous, they tend to fabricate incorrect answers. Traditional mitigation methods (stronger models, expanded data, prompt engineering) cannot fundamentally solve the issue due to the lack of a fact-checking mechanism.

The core of Retrieval-Augmented Generation (RAG) is retrieving relevant information from trusted knowledge bases before generation. The project uses the FAISS vector database for semantic retrieval: convert the question into a vector, search for semantically similar document fragments in the FAISS index, and provide them as context to the generation model—ensuring answers have clear sources.

RAG alone is insufficient; models may misinterpret retrieved information. A Natural Language Inference (NLI) verification layer is introduced: NLI judges logical relationships between sentences (entailment/contradiction/neutral). By comparing generated answers with retrieved fragments, contradictions are flagged as hallucination risks, adding an extra fact-checking layer.

Tech stack includes:

• Llama 3: Main generation model (open-source and easy to deploy)
• LangChain: Orchestrates the RAG workflow
• Sentence Transformers: Converts text to vectors
• Streamlit: Web interface Workflow: User query → FAISS retrieval → Llama3 generation → NLI verification → Output final answer

Application scenarios: AI research, fact-checking, educational assistance, legal/medical fields, etc. Future improvements: Support PDF uploads, real-time web retrieval, SHAP interpretability analysis, hallucination confidence scoring, multi-document reasoning, etc.

LLM hallucination is an urgent challenge in AI. The RAG + NLI solution provides a promising path to building trustworthy AI through external knowledge retrieval and fact verification. We hope future AI can honestly acknowledge its knowledge boundaries and say 'I don’t know' when uncertain.