Large Language Models (LLMs) have wowed us with their ability to perform various tasks, seemingly out of thin air. But behind the curtain, a hidden bias lurks, preventing these impressive models from reaching their full potential. LLMs often rely on shortcuts, focusing on specific parts of the input rather than truly understanding the task. This 'preference bias' can lead to incorrect predictions, especially when faced with complex reasoning challenges. Imagine trying to solve a puzzle by only looking at half the pieces! That's essentially what LLMs sometimes do. Researchers have discovered that when presented with only part of the information needed (like the premise without the hypothesis in a logical statement), LLMs often make the same, sometimes incorrect, predictions as they do with the full information. This reveals a critical flaw in their reasoning process. To address this, researchers have developed a clever technique called 'Task Calibration' (TC). This method reframes the way LLMs approach inference, encouraging them to consider all pieces of the puzzle. Instead of relying on potentially misleading shortcuts, TC pushes the model to analyze the combined effect of all inputs, mitigating the impact of preference bias. The results are impressive. Experiments across various inference tasks, including natural language inference, stance detection, and paraphrasing, have shown that TC significantly improves the accuracy of LLM predictions, sometimes by over 40%! Even in few-shot learning scenarios, where the model has limited examples to learn from, TC boosts performance and helps LLMs better utilize provided demonstrations. Task Calibration also works across various language tasks beyond inference, like sentiment analysis and hate speech detection, proving it's a versatile tool for improving overall LLM accuracy. While TC requires additional computation, the gains in performance and robustness make it a promising development in the quest to unlock the true power of LLMs. As LLMs become increasingly integrated into our lives, ensuring they reason accurately and reliably is more crucial than ever. Task Calibration represents a significant step towards achieving this goal, paving the way for more dependable and capable AI systems.