Opinion dynamics (OD) studies how individual opinions evolve and generate collective patterns such as consensus and polarization. While recent work explores OD using populations of LLM-based agents focusing on opinion exchange, it typically does not incorporate individuals’ lived experiences, such as economic outcomes of past decisions, which play a critical role in shaping opinions. We propose a novel OD simulation framework that grounds LLM-based agents in an economic environment, allowing them to act and receive environmental feedback. Our simulations exhibit coherent OD at both individual and population levels: individual opinions follow structured trajectories shaped by economic experiences, with adverse conditions inducing opinion rigidity, while at the population level, collective opinions co-move with economic conditions, with inequality amplifying polarization and price instability driving larger distributional shifts. These results highlight the importance of grounding LLM-based agents in environments to capture collective OD.

In the post-Turing era, evaluating large language models (LLMs) involves assessing generated text based on readers’ decisions rather than merely its indistinguishability from human-produced content. This paper explores how LLM-generated text impacts readers’ decisions, focusing on both amateur and expert audiences. Our findings indicate that GPT-4 can generate persuasive analyses affecting the decisions of both amateurs and professionals. Furthermore, we evaluate the generated text from the aspects of grammar, convincingness, logical coherence, and usefulness. The results highlight a high correlation between real-world evaluation through audience decisions and the current multi-dimensional evaluators commonly used for generative models. Overall, this paper shows the potential and risk of using generated text to sway human decisions and also points out a new direction for evaluating generated text, i.e., leveraging the decisions of readers. We release our dataset to assist future research.

In the dynamic marketplace, vendors continuously seek innovative ideas for new products and ways to improve existing ones. These ideas can be uncovered by analyzing text data, such as product descriptions and customer reviews. However, the ever-increasing volume of text data poses a challenge in extracting meaningful insights. Therefore, this study addresses the challenge of extracting actionable insights from the growing volume of text data, with a specific focus on product descriptions. To this end, we investigate two primary research questions: the predictive power of product descriptions for product success, and the capability of style transfer to highlight the successful factors of these descriptions. In response to the first question, our findings validate that product descriptions are indeed reliable indicators of product success. Addressing our second question, we propose a Successful Style Transfer Variational Autoencoder (SST-VAE), a VAE-based language model designed for effective successful style transfer. Qualitative analysis indicates that the SST-VAE effectively enables successful style transfer conditional on a given label. In addition, case studies suggest that the proposed approach could be useful in gaining insights about product success, by highlighting key factors that may contribute to their success. On the other hand, our approach confronts issues such as hallucinations and the need for factual accuracy. These challenges underscore the necessity for continued research in the field of e-commerce natural language processing.