![[2504.06438] Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2504.06438] Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning
Summary
The paper presents a novel framework for premise verification in large language models (LLMs) to reduce hallucinations by using retrieval-augmented logical reasoning.
Why It Matters
As LLMs become increasingly integrated into applications, ensuring their factual accuracy is crucial. This research addresses the prevalent issue of hallucinations, which can lead to misinformation, enhancing the reliability of AI-generated content.
Key Takeaways
- Proposes a retrieval-based framework to verify premises before LLM generation.
- Transforms user queries into logical representations for better accuracy.
- Reduces hallucinations and improves factual consistency without extensive training.
- Demonstrates effectiveness in real-time applications.
- Addresses a critical challenge in the deployment of LLMs.
Computer Science > Computation and Language arXiv:2504.06438 (cs) [Submitted on 8 Apr 2025 (v1), last revised 16 Feb 2026 (this version, v2)] Title:Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning Authors:Yuehan Qin, Shawn Li, Yi Nian, Xinyan Velocity Yu, Yue Zhao, Xuezhe Ma View a PDF of the paper titled Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning, by Yuehan Qin and 5 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) have shown substantial capacity for generating fluent, contextually appropriate responses. However, they can produce hallucinated outputs, especially when a user query includes one or more false premises-claims that contradict established facts. Such premises can mislead LLMs into offering fabricated or misleading details. Existing approaches include pretraining, fine-tuning, and inference-time techniques that often rely on access to logits or address hallucinations after they occur. These methods tend to be computationally expensive, require extensive training data, or lack proactive mechanisms to prevent hallucination before generation, limiting their efficiency in real-time applications. We propose a retrieval-based framework that identifies and addresses false premises before generation. Our method first transforms a user's query into a logical representation, then applies retrieval-augmented generation (RAG) to assess the validity of each p...
