Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrieval
(2405.06545)Abstract
LLMs have demonstrated remarkable capabilities across various domains, although their susceptibility to hallucination poses significant challenges for their deployment in critical areas such as healthcare. To address this issue, retrieving relevant facts from knowledge graphs (KGs) is considered a promising method. Existing KG-augmented approaches tend to be resource-intensive, requiring multiple rounds of retrieval and verification for each factoid, which impedes their application in real-world scenarios. In this study, we propose Self-Refinement-Enhanced Knowledge Graph Retrieval (Re-KGR) to augment the factuality of LLMs' responses with less retrieval efforts in the medical field. Our approach leverages the attribution of next-token predictive probability distributions across different tokens, and various model layers to primarily identify tokens with a high potential for hallucination, reducing verification rounds by refining knowledge triples associated with these tokens. Moreover, we rectify inaccurate content using retrieved knowledge in the post-processing stage, which improves the truthfulness of generated responses. Experimental results on a medical dataset demonstrate that our approach can enhance the factual capability of LLMs across various foundational models as evidenced by the highest scores on truthfulness.
Overview
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The paper introduces a method called Self-Refinement-Enhanced Knowledge Graph Retrieval (Re-KGR) to improve the factuality of LLMs by refining the information retrieval process. It targets reducing non-factual content or 'hallucinations', especially in critical fields like healthcare.
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Re-KGR method involves entity detection, triple extraction for potential inaccuracies, smart knowledge retrieval from a curated graph, and subsequent verification and rectification to enhance the truthfulness of LLM outputs.
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The effectiveness of Re-KGR was tested, showing improved truthfulness scores in medical datasets and suggesting its applicability in other high-stakes domains like law or finance.
Mitigating Hallucinations in LLMs with Enhanced Knowledge Retrieval
Introduction to Hallucinations in LLMs
LLMs, while excelling in producing human-like text, often suffer from generating non-factual content, also known as hallucinations. This issue is particularly pronounced in domains where accuracy is critical, such as healthcare. Traditional methods to combat this through knowledge retrieval are often cumbersome and resource-heavy. The paper we're exploring proposes an innovative approach called Self-Refinement-Enhanced Knowledge Graph Retrieval (Re-KGR), aimed at enhancing the factuality of LLMs by refining the process of information retrieval and integration.
Understanding the Re-KGR Approach
The Re-KGR method enhances the traditional process of using knowledge graphs for verifying the factual accuracy of the content generated by LLMs. Here's how it works:
- Entity Detection and Triple Extraction: First, the model identifies potential inaccuracies in the tokens generated by LLMs. It then extracts these tokens and forms 'triples', which are sets of data points that include a subject, predicate, and object, to be checked for accuracy.
- Knowledge Retrieval: Instead of retrieving all possible triples, Re-KGR smartly retrieves only those that are likely to be erroneous by cross-referencing a curated knowledge graph. This reduces unnecessary computational overhead.
- Verification and Rectification: Retrieved triples are then verified, and any incorrect information is rectified in the LLM's outputs, enhancing the overall truthfulness of the content.
Practical Implications
The usage of Re-KGR, particularly in the medical domain, can significantly diminish the risk of disseminating incorrect information, which is crucial for patient care and medical advisories. By refining the retrieval process, the model not only improves efficiency but also the reliability of the responses generated, making it a promising tool for deployments in high-stake environments.
Performance and Results
The effectiveness of Re-KGR is underscored by its performance metrics. It achieved notably higher truthfulness scores on a medical dataset when compared to baseline methods. For instance, integrated with a LLaMA model and a contrastive decoding technique (DoLa), Re-KGR method showed a substantial increase in the factual accuracy of responses.
Future Directions
While the current research primarily focuses on medical QA tasks, the potential for Re-KGR to be adapted to other domains is significant. Future work could explore its applicability in fields like law or finance, where the veracity of information is equally critical. Furthermore, integrating knowledge retrieval during the generation phase might optimize the response time and efficiency, paving the way for real-time applications.
Conclusion
Re-KGR presents a viable solution to the persistent issue of hallucinations in LLMs. By leveraging sophisticated entity detection techniques linked with strategic knowledge graph retrievals and subsequent verification, it ensures that the expanded capabilities of LLMs are not undermined by inaccuracies. This research not only contributes to the more reliable deployment of LLMs but also opens pathways for further innovations in handling hallucinations across various AI-driven applications.
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