Can GPT-4 Sway Experts' and Amateurs' Financial Decisions?

Core Concepts

GPT-4 can generate persuasive analyses that sway the decisions of both amateur and professional investors, with amateurs being more susceptible to the influence of GPT-4-generated text.

Abstract

The paper explores the impact of GPT-4-generated text on the decision-making of both amateur and expert investors. The authors conducted experiments using earnings conference call (ECC) transcripts, where participants were first presented with a neutral summary and asked to make a decision, and then provided with an analysis with a specific investment stance (either generated by GPT-4 or written by professional analysts) and asked to reconsider their decision.
The key findings are:

GPT-4 can generate persuasive analyses that sway the decisions of both amateurs and professionals, but amateurs are more likely to change their decisions based on GPT-4-generated analysis, while more experienced investors are less influenced.

Investors are more sensitive to underweight (negative) analysis, and amateurs are particularly susceptible to this type of information, raising concerns about the potential risks of using LLMs to generate financial analyses for the general public.

The authors also evaluated the generated text from various aspects (grammar, convincingness, logical coherence, and usefulness) and found a high correlation between these metrics and the real-world evaluation through audience reactions, highlighting the potential of using readers' reactions as an evaluation method for generated text.

The paper emphasizes the need to consider the differences between amateur and expert decision-making when evaluating the impact of LLM-generated text, and the importance of developing responsible frameworks for the use of these models in decision-critical applications.

Stats

Ratio of changing decisions in the second stage:

All: 28.7%
Amateur: 31.3%
Expert: 24.7%
Veteran: 15.6%
Direction of the change:

Upward (Increase):

Amateur: 24.1%
Expert: 42.3%
Veteran: 44.4%

Downward (Decrease):

Amateur: 75.9%
Expert: 57.7%
Veteran: 55.6%
Accuracy of decisions:

1st stage:

Amateur: 61.2%
Expert: 61.3%
Veteran: 62.2%

2nd stage:

Amateur: 45.8%
Expert: 44.7%
Veteran: 51.1%

Quotes

"GPT-4 can generate persuasive analyses affecting the decisions of both amateurs and professionals."
"Amateurs are very sensitive to negative information. This raises a potential risk of using LLMs to generate analysis for the general public."
"Analysis with a strong tone sways experts' decisions more than pure analysis, regardless of the given stance."

Key Insights Distilled From

Beyond Turing Test: Can GPT-4 Sway Experts' Decisions?

by Takehiro Tak... at arxiv.org 09-26-2024

https://arxiv.org/pdf/2409.16710.pdf

Deeper Inquiries

How can we develop more objective and standardized evaluation frameworks for LLM-generated text in decision-critical applications?

To develop more objective and standardized evaluation frameworks for LLM-generated text, particularly in decision-critical applications such as finance, we can adopt a multi-faceted approach that incorporates both quantitative and qualitative metrics. First, we should establish clear criteria for evaluation that align with the specific needs of the domain. For instance, in finance, metrics could include accuracy, relevance, and clarity of the generated text, as well as its ability to influence decision-making effectively.
Utilizing automated scoring systems that assess grammatical correctness, coherence, and logical flow can provide a baseline for objective evaluation. However, given the subjective nature of text interpretation, it is crucial to complement these automated metrics with human evaluations. This can be achieved by creating standardized rubrics that define what constitutes high-quality text in various dimensions, such as convincingness, usefulness, and logical coherence.
Furthermore, incorporating feedback loops where both amateur and expert users assess the generated text can help refine the evaluation process. By analyzing user reactions and decision changes, as highlighted in the study, we can better understand the impact of LLM-generated content on real-world decision-making. This dual approach of combining objective metrics with user feedback will lead to a more comprehensive and standardized evaluation framework that can be adapted across different domains.

What regulatory frameworks or guidelines are needed to ensure the responsible use of LLMs in the financial sector and other domains where their influence can have significant societal impact?

To ensure the responsible use of LLMs in the financial sector and other domains with significant societal impact, regulatory frameworks must be established that prioritize transparency, accountability, and ethical considerations. First, guidelines should mandate that organizations disclose when content is generated by LLMs, allowing users to understand the source of the information they are consuming. This transparency is crucial in maintaining trust, especially in sectors like finance where decisions can lead to substantial financial consequences.
Additionally, regulatory bodies should develop standards for the training and deployment of LLMs, ensuring that these models are trained on diverse and representative datasets to mitigate biases that could lead to harmful outcomes. Regular audits of LLM outputs should be conducted to assess their accuracy and potential impact on decision-making processes.
Moreover, guidelines should address the ethical implications of using LLMs, particularly concerning the potential for market manipulation or misinformation. Establishing clear consequences for the misuse of LLM-generated content can deter unethical practices. Collaboration between industry stakeholders, regulatory agencies, and academic researchers is essential to create a robust framework that adapts to the evolving landscape of AI technologies while safeguarding public interests.

What other factors, beyond the content of the analysis, might influence the decision-making of amateur and expert investors, and how can these be incorporated into the evaluation of LLM-generated text?

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.