Beyond the content of the analysis, several factors can influence the decision-making of amateur and expert investors. These include emotional responses, cognitive biases, prior experiences, and the social context in which decisions are made. For instance, amateur investors may be more susceptible to emotional reactions, such as fear or excitement, which can lead to impulsive decisions. In contrast, expert investors may rely more on analytical reasoning but can still be influenced by biases such as overconfidence or anchoring.
To incorporate these factors into the evaluation of LLM-generated text, we can implement a multi-dimensional assessment framework that includes psychological and behavioral metrics. Surveys or interviews could be conducted to gauge investor sentiment and emotional responses to LLM-generated analyses. Additionally, tracking decision-making patterns over time can reveal how external factors, such as market trends or news events, interact with LLM outputs to influence investor behavior.
By integrating these psychological and contextual elements into the evaluation process, we can gain a more holistic understanding of how LLM-generated text impacts decision-making. This approach not only enhances the evaluation of LLM outputs but also informs the development of more effective and responsible AI systems tailored to the needs of diverse investor groups.