Home AI News Improving Large Language Models’ Reasoning Abilities through Active Prompting and Uncertainty

Improving Large Language Models’ Reasoning Abilities through Active Prompting and Uncertainty

Improving Large Language Models’ Reasoning Abilities through Active Prompting and Uncertainty

Introducing Large Language Models (LLMs) and Their Reasoning Abilities

Large language models (LLMs) like ChatGPT have become essential tools in our daily lives, helping us with information retrieval, chat assistance, writing assistance, and more. These models have strong reasoning capabilities, allowing them to use logical deduction to solve problems based on given information. They can draw conclusions, make inferences, and connect different pieces of information.

Challenges of Complex Reasoning Tasks for LLMs

While LLMs excel at reasoning tasks, they face difficulties when it comes to complex reasoning tasks. These tasks require a higher level of comprehension and reasoning ability. To guide LLMs in such tasks, we can use in-context learning. This means providing the model with a set of example questions and answers before asking the main question. By doing this, the model can learn the context and adapt accordingly.

The Importance of Question-Answer Chains

The question-answer chain is crucial in guiding LLMs. Selecting informative questions and annotating them with chain-of-thought (CoT) and answers requires human engineering. However, given the diverse difficulty, scope, and domain of reasoning tasks, it’s unclear which questions should be prioritized for annotation.

Active Prompting: A Solution to Question Selection

Active Prompting offers a solution to this problem by leveraging uncertainty and involving minimal human efforts. The proposed method introduces uncertainty metrics to rank the most uncertain questions, which are then selected for annotation. Uncertainty is estimated using various strategies such as disagreement and entropy. Higher entropy indicates more uncertainty, making questions with higher entropy preferable for annotation.

Evaluation of Active Prompting and Its Benefits

The proposed solution is evaluated on multiple reasoning tasks and found to outperform baseline methods in terms of accuracy and efficiency. The uncertainty metrics also contribute to improving the model’s performance. Overall, Active Prompting is a valuable approach to determining important questions for annotation in CoT prompting.


Active Prompting is a solution that leverages uncertainty to select important questions for annotation in CoT prompting. It minimizes human efforts and enhances the performance of LLMs in reasoning tasks. The results from this approach are promising and can further improve the capabilities of LLMs in various applications.

About the Author

Ekrem Çetinkaya received his B.Sc. and M.Sc. from Ozyegin University, Istanbul, Türkiye. His research interests include deep learning, computer vision, video encoding, and multimedia networking.

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