Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning (2504.06438v1)

Abstract: 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 premise using factual sources. Finally, we incorporate the verification results into the LLM's prompt to maintain factual consistency in the final output. Experiments show that this approach effectively reduces hallucinations, improves factual accuracy, and does not require access to model logits or large-scale fine-tuning.

Don’t Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning

The paper "Don’t Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning" addresses a critical challenge faced by LLMs—the phenomenon of hallucination, particularly when questions posed to them are based on false premises. Hallucination, in this context, refers to the LLM's tendency to produce fabricated, inaccurate, or misleading information when responding to queries that contain false premises contradictory to established facts. This issue is significant as it can lead to incorrect responses that affect decision-making processes, especially in sensitive domains such as healthcare and finance.

Methodology

The authors propose a novel framework that emphasizes proactive prevention of hallucinations rather than post-hoc mitigation. The framework incorporates retrieval-augmented generation (RAG) combined with logical reasoning techniques to verify the factual consistency of the premises in user queries. The process includes three core stages:

1. Logical Form Extraction: This initial step involves transforming user queries into a logical representation to facilitate systematic analysis. The logical form helps in identifying critical components, such as entities and their relationships, which are central to the query's semantics.
2. Structured Retrieval and Verification: Using the logical representation, the model retrieves relevant information from a knowledge graph to verify the accuracy of identified premises. The retrieval process is augmented to ensure the extracted evidence aligns accurately with the logical form of the query.
3. Factual Consistency Enforcement: Recognizing potential contradictions, the model flags queries with false premises, thereby guiding the LLM to either correct or disregard the erroneous assumptions before generating a response.

Experimental Results

The authors evaluate their approach on a dataset containing True and False Premise Questions (TPQs and FPQs) using a structured knowledge graph. They demonstrate that this framework enhances the accuracy of LLMs in differentiating between valid and false premises, effectively reducing hallucination rates. The method yields significant improvements, achieving a high true positive rate (TPR) and F1 score, particularly with multi-hop questions where reasoning over multiple steps is crucial.

Results show that utilizing logical forms during both retrieval and verification stages substantially increases the detection of false premises. This finding is supported by comparisons across several retrieval methods, including embedding-based, non-parametric, and LLM-based retrievers, where logical forms contribute to higher performance metrics, especially the true positive rate.

Implications and Future Directions

The proposed strategy highlights the necessity of integrating external knowledge sources and logical reasoning in enhancing the factual precision of LLMs. This approach does not require access to model logits or extensive fine-tuning, making it applicable to various existing LLM frameworks.

From a theoretical standpoint, the paper provides insights into the dynamics between LLMs and structured reasoning models, paving the way for future advancements in aligning LLM outputs with objective truth, particularly in complex, multi-step reasoning scenarios. Practically, this enhances the reliability of AI systems in critical applications, minimizing the risk of misinformation.

Future research could focus on expanding this framework to a broader range of question types and domains, incorporating real-time updates to knowledge graphs to maintain alignment with current facts. Furthermore, exploring the balance between model performance and computational efficiency remains an open area for continued investigation.

In summary, this paper offers a significant contribution toward mitigating hallucinations in LLMs through a structured, retrieval-augmented logical reasoning approach, increasing both the reliability and accuracy of AI-generated responses in various substantial applications.