Large language models (LLMs) are impressive, but they sometimes "hallucinate," meaning they generate incorrect or inconsistent information. This poses a significant challenge for real-world applications where reliability is crucial. Researchers are tackling this problem by developing methods to detect these hallucinations. One promising approach involves calculating a "confidence score" to assess the likelihood of an LLM-generated answer being a hallucination. This score is then calibrated based on the input and the generated response, allowing for risk-aware decision-making. Interestingly, no single scoring method works best in all situations. Some methods excel at detecting factual inconsistencies, while others are better at identifying logical contradictions or ungrounded claims. Researchers have found that combining multiple scoring methods into a "multi-scoring" framework significantly improves overall performance. This approach leverages the strengths of individual methods, creating a more robust and accurate hallucination detection system. Even better, a "cost-effective multi-scoring" approach can achieve comparable results while minimizing computational overhead. This is particularly relevant for real-world applications where cost considerations are important. By strategically combining scores, developers can achieve a high level of accuracy without excessive computational burden. This research is crucial for building trust and reliability in LLMs. As AI models become increasingly integrated into our lives, ensuring they produce accurate and consistent information is paramount. By developing cost-effective methods to detect and mitigate hallucinations, we pave the way for wider and safer adoption of LLMs across various industries and applications.