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In this paper, we explore the challenges inherent to Large Language Models(LLMs) like GPT-4, particularly their propensity for hallucinations, logicmistakes, and incorrect conclusions when tasked with answering complexquestions. The capacity of LLMs to present erroneous answers in a coherent andsemantically rigorous manner further complicates the detection of factualinaccuracies. This issue is especially pronounced in fields that requirespecialized expertise. Our work delves into these challenges, aiming to enhancethe understanding and mitigation of such errors, thereby contributing to theimprovement of LLM accuracy and reliability in scientific and other specializeddomains. Our findings reveal a non-linear relationship between the context'srelevancy and the answers' measured quality. In addition, we demonstrate thatwith 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 toself-examine the quality of their own performance. Finally, we describe anexperimental platform that can be seen as a proof-of-concept of the techniquesdescribed in this work.

Context Matters: Data-Efficient Augmentation of Large Language Models for Scientific Applications