Assessing the Response Strategies of Large Language Models Under Uncertainty: A Comparative Study Using Prompt Engineering

The ability of artificial intelligence to understand and generate human language has transformed various applications, enhancing interactions and decision-making processes. Evaluating the fallback behaviors of language models under uncertainty introduces a novel approach to understanding and improving their performance in ambiguous or conflicting scenarios. The research focused on systematically analyzing ChatGPT and Claude through a series of carefully designed prompts to introduce different types of uncertainty, including ambiguous questions, vague instructions, conflicting information, and insufficient context. Automated scripts were employed to ensure consistency in data collection, and the responses were evaluated using metrics such as accuracy, consistency, fallback mechanisms, response length, and complexity. The results highlighted significant differences in how ChatGPT and Claude handle uncertainty, with ChatGPT demonstrating superior accuracy and stability, and a more frequent use of proactive strategies to manage ambiguous inputs. The study's findings provide valuable insights for the ongoing development and refinement of language models, emphasizing the importance of integrating advanced fallback mechanisms and adaptive response strategies to enhance their robustness and reliability.