[Paper] Mitigating LLM Hallucinations through Domain-Grounded Tiered Retrieval

Source: arXiv - 2603.17872v1

Overview

Large Language Models (LLMs) are impressively fluent, yet they still “hallucinate” – i.e., they can produce statements that sound plausible but are factually wrong. This paper introduces a domain‑grounded tiered retrieval system that turns LLMs into “truth‑seekers” by interleaving verification steps with external knowledge look‑ups. The authors show that the approach dramatically cuts hallucinations across several benchmark suites, making LLM‑driven assistants safer for high‑stakes applications.

Key Contributions

• Four‑phase self‑regulating pipeline (implemented with LangGraph) that blends intrinsic LLM verification with external retrieval.
• Early‑Exit intrinsic verification to save compute when the model is already confident in its answer.
• Domain Detector that routes queries to the most relevant knowledge archive (e.g., temporal, numerical, or domain‑specific corpora).
• Corrective Document Grading (CRAG) module that scores retrieved passages and discards irrelevant or low‑quality context before feeding it back to the model.
• Claim‑level extrinsic verification that re‑generates answers and checks each atomic claim against retrieved evidence.
• Comprehensive empirical evaluation on 650 queries spanning five benchmarks (TimeQA v2, FreshQA v2, HaluEval General, MMLU Global Facts, TruthfulQA), achieving win‑rates up to 83.7 % over strong zero‑shot baselines.

Methodology

1. Intrinsic Verification & Early‑Exit – The LLM first attempts to answer the query. A lightweight confidence estimator decides whether the answer is likely correct; if so, the pipeline stops early, saving latency and API costs.
2. Adaptive Search Routing – A lightweight classifier (the Domain Detector) predicts the query’s topical domain (e.g., “historical dates”, “financial figures”). It then selects the most appropriate external index (e.g., a time‑focused Wikipedia snapshot, a curated financial dataset).
3. Corrective Document Grading (CRAG) – Retrieved documents are scored by a secondary LLM that judges relevance, factual consistency, and source credibility. Only the top‑ranked passages are kept, preventing noisy context from contaminating the final answer.
4. Extrinsic Regeneration & Claim‑Level Verification – The LLM re‑generates an answer using the filtered documents. Each atomic claim (e.g., “The Eiffel Tower is 324 m tall”) is then cross‑checked against the evidence; mismatches trigger a fallback to “I don’t know” or a request for clarification.

All four stages are orchestrated with LangGraph, a graph‑based workflow engine that enables dynamic branching, retries, and stateful memory across the pipeline.

Results & Findings

• The tiered system consistently outperforms vanilla LLM prompting across all domains, with the biggest gains in temporally‑sensitive queries (TimeQA).
• Groundedness scores—the proportion of answers that can be directly traced to a retrieved source—remain above 78 % even on the most open‑ended benchmark (HaluEval).
• A notable failure mode, “False‑Premise Overclaiming,” occurs when the model confidently asserts facts that are not present in any retrieved document, suggesting that the early‑exit confidence estimator can be overly optimistic in some edge cases.

Practical Implications

• Enterprise chatbots & virtual assistants can embed this pipeline to dramatically reduce misinformation risk, especially in regulated sectors (finance, healthcare, legal).
• The early‑exit mechanism cuts API usage by up to ~30 % for queries that are already well‑grounded, translating into cost savings for SaaS providers.
• Domain‑aware routing means you can plug in proprietary knowledge bases (e.g., internal wikis, product manuals) without retraining the LLM—just add a new index and update the detector.
• The claim‑level verification layer provides a natural “explain‑your‑answer” hook for UI designers: each answer can be accompanied by the supporting snippet, boosting user trust.
• Because the architecture is built on LangGraph, it is modular; teams can swap in their own LLMs, retrieval back‑ends (e.g., Elastic, Pinecone), or grading models without rewriting the whole system.

Limitations & Future Work

• The False‑Premise Overclaiming failure indicates that confidence estimation still needs refinement; the model may skip retrieval when it shouldn’t.
• The pipeline adds latency (multiple LLM calls and retrieval steps) compared to a single‑pass generation, which may be problematic for ultra‑low‑latency applications.
• Evaluation is limited to English‑centric benchmarks; cross‑lingual or multimodal domains (code, images) remain untested.
• Authors suggest adding a pre‑retrieval “answerability” node that first decides whether a question is even answerable given the available knowledge, which could further prune unnecessary work and improve safety.

Bottom line: By weaving together verification, domain‑aware retrieval, and claim‑level grounding, this work offers a pragmatic blueprint for developers who need LLMs that talk responsibly. Implementing a tiered RAG pipeline today can make your AI products more trustworthy, cost‑effective, and ready for real‑world deployment.

Authors

• Md. Asraful Haque
• Aasar Mehdi
• Maaz Mahboob
• Tamkeen Fatima

Paper Information

• arXiv ID: 2603.17872v1
• Categories: cs.CL, cs.AI
• Published: March 18, 2026
• PDF: Download PDF