In this paper, we explore the challenges inherent to Large Language Models (LLMs) like GPT-4, particularly their propensity for hallucinations, logic mistakes, and incorrect conclusions when tasked with answering complex questions. The capacity of LLMs to present erroneous answers in a coherent and semantically rigorous manner further complicates the detection of factual inaccuracies. This issue is especially pronounced in fields that require specialized expertise. Our work delves into these challenges, aiming to enhance the understanding and mitigation of such errors, thereby contributing to the improvement of LLM accuracy and reliability in scientific and other specialized domains. Our findings reveal a non-linear relationship between the context's relevancy and the answers' measured quality. In addition, we demonstrate that with the correct calibration, it is possible to automate the grading procedure -- a finding suggesting that, at least to some degree, the LLMs can be used to self-examine the quality of their own performance. Finally, we describe an experimental platform that can be seen as a proof-of-concept of the techniques described in this work.

翻译：本文探讨了大型语言模型（如GPT-4）在回答复杂问题时固有的挑战，特别是其产生幻觉、逻辑错误和错误结论的倾向。大型语言模型能以连贯且语义严谨的方式呈现错误答案，这进一步增加了识别事实不准确的难度。这一问题在需要专业知识的领域尤为突出。本研究深入分析了这些挑战，旨在增进对这类错误的理解并加以缓解，从而提升大型语言模型在科学及其他专业领域的准确性和可靠性。我们的研究结果显示，语境相关性与答案质量之间存在非线性关系。此外，我们证明，通过正确的校准，可以实现评分过程的自动化——这一发现表明，至少在一定程度上，大型语言模型可用于自我检测其自身性能的质量。最后，我们描述了一个可视为本文所述技术概念验证的实验平台。