Practical Solutions for Efficient Hallucination Detection
Addressing Challenges with Large Language Models (LLMs)
Large Language Models (LLMs) have shown remarkable capabilities in natural language processing tasks but face challenges such as hallucinations. These hallucinations undermine reliability and require effective detection methods.
Robust Workflow for Hallucination Detection
Microsoft Responsible AI researchers present a workflow that balances latency and interpretability by combining a small language model (SLM) with a downstream LLM module called a “constrained reasoner.” This approach mitigates the challenges of using LLMs for real-time applications.
Components of the Framework
The framework consists of an SLM for initial detection and a constrained reasoner based on an LLM for explanation. The SLM efficiently screens input to reduce computational load, while the reasoner provides detailed explanations of detected hallucinations.
Enhancing Alignment and Consistency
The framework incorporates mechanisms to enhance alignment between SLM decisions and LLM explanations, including careful prompt engineering for the LLM and potential feedback loops for refining the SLM’s detection criteria over time.
Experimental Results
The experimental results demonstrate the effectiveness of the proposed hallucination detection framework, particularly the Categorized approach, in handling inconsistencies between SLM decisions and LLM explanations.
Practical Framework for Hallucination Detection
This study presents a practical framework for efficient and interpretable hallucination detection by integrating an SLM for detection with an LLM for constrained reasoning. The proposed categorized prompting and filtering strategy effectively aligns LLM explanations with SLM decisions, demonstrating empirical success across four datasets.
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