Enhancing factual reasoning in large language models by combining Parameter-Efficient Fine-Tuning (PEFT) with Retrieval-Augmented Generation and Knowledge Graph injection. This hybrid architecture grounds LLM outputs in verified domain knowledge, enabling multi-hop reasoning with dramatically reduced hallucination rates across complex, domain-specific queries.
Large language models are powerful generators but unreliable narrators. They hallucinate facts, fabricate citations, and fail especially on domain-specific or multi-hop queries where factual precision is non-negotiable. In high-stakes domains like medical reasoning, legal analysis, and scientific research, a single hallucinated fact can propagate catastrophically through downstream decisions.
Pure Retrieval-Augmented Generation (RAG) introduces retrieval noise and struggles with implicit reasoning chains. Pure fine-tuning burns domain knowledge into weights but sacrifices the model's generality and can overfit to narrow distributions. Neither approach alone solves the core challenge: enabling a model to reason accurately over complex, interconnected facts.
Our hybrid approach combines the best of both worlds. PEFT adapters teach the model how to reason with structured evidence, while RAG and Knowledge Graph injection provide the evidence itself at inference time. The result is a system that maintains generality while grounding its outputs in verifiable, up-to-date domain knowledge.
The architecture routes each query through parallel retrieval paths before assembling a unified context for the fine-tuned LLM. Knowledge Graph sub-graphs provide structured relational facts, while the RAG pipeline surfaces relevant unstructured passages. Both are merged in a context assembler that feeds the PEFT-adapted model for chain-of-thought reasoning.
Natural language question, potentially multi-hop. The system decomposes complex queries into reasoning sub-steps for targeted retrieval.
Identifies intent, entities, and required reasoning hops. Generates structured sub-queries for both KG and RAG retrieval systems.
Domain-specific KG queried via entity linking. Sub-graph extraction retrieves relevant triples within 2-3 hops of query entities.
Dense passage retrieval via FAISS finds top-k relevant document chunks. Re-ranking model scores chunks for relevance and factual density.
Merges KG triples and RAG chunks into a structured context window. Prioritizes most relevant evidence and formats it for the LLM.
Decoder-only LLM with LoRA adapters fine-tuned on domain QA pairs. Preserves general capabilities while adding domain expertise.
Generates explicit reasoning steps, each referencing evidence from context. Creates a traceable reasoning path for multi-hop queries.
Final factual answer with cited sources and confidence score. Includes the reasoning chain for full explainability.
The system was developed through an iterative five-stage pipeline, each building on the outputs and learnings of the previous stage. From constructing the domain knowledge graph to final evaluation, every component was designed for modularity and reproducibility.
Built a comprehensive domain ontology and populated a Neo4j knowledge graph with entity-relation triples extracted from curated sources. Applied entity resolution and link prediction to fill gaps.
Fine-tuned a base LLM using Low-Rank Adaptation (LoRA) on domain-specific QA pairs, teaching the model to reason with structured evidence without catastrophic forgetting of general capabilities.
Implemented dense passage retrieval via FAISS, augmented with extracted sub-graphs from the knowledge graph. Context ranking ensures the most relevant evidence is prioritized for the LLM.
Developed structured prompting templates that guide the model through explicit reasoning steps, citing retrieved evidence at each hop. This improves both accuracy and interpretability.
Evaluated on multi-hop QA benchmarks with custom metrics for hallucination rate, reasoning faithfulness, and answer accuracy. Iterated on retrieval strategies and prompt templates.
Comprehensive evaluation across multiple dimensions demonstrates the advantage of the hybrid PEFT+RAG+KG approach over individual baselines. Explore the charts below to understand how each component contributes to the final system performance.
Exact-match accuracy on domain QA benchmark
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Percentage of factually incorrect claims
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Performance across reasoning hop counts
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Breakdown of system failure modes
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Side-by-side comparison of all evaluated approaches across key performance dimensions, demonstrating the consistent advantage of the full hybrid system.
| Approach | Single-hop Accuracy | Multi-hop Accuracy | Hallucination Rate | Latency |
|---|---|---|---|---|
| Base LLM | 61% | 23% | 38% | 0.4s |
| Fine-Tuned (PEFT) | 72% | 41% | 24% | 0.5s |
| RAG Only | 69% | 38% | 21% | 1.2s |
| Our System (PEFT+RAG+KG) | 86% | 64% | 8% | 1.8s |
Combining PEFT fine-tuning with RAG and Knowledge Graph injection yields synergistic improvements that exceed any individual approach. The whole is greater than the sum of its parts.
External knowledge grounding via sub-graph extraction and passage retrieval reduces hallucination by 78%, making the system viable for high-stakes factual reasoning tasks.
The modular design allows rapid adaptation to new domains by swapping the knowledge graph and document store, requiring only lightweight LoRA re-training rather than full model fine-tuning.
Standard LLM systems hallucinate on domain-specific reasoning tasks where factual precision is mandatory.
Knowledge-augmented architecture combining PEFT, RAG, and graph-aware reasoning for grounded answers.
Higher factual reliability, lower hallucination rate, and explainable multi-hop reasoning for expert users.
I can implement retrieval and knowledge grounding layers that make LLM outputs more trustworthy in production.
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