Hey ChatGPT—Is a Louis Vuitton Bag an Investment? Evaluating LLM Readiness for Use in Financial Literacy and Education
Bibliographic record
Abstract
ABSTRACT The prevalence of large language models (LLMs) such as ChatGPT has wowed the world with its ability to generate text in a human-like manner. While educators evaluate how AI will impact the future of learning, we identify mistakes ChatGPT has made. We further extend this concern to nonfinancially sophisticated users seeking to improve their financial literacy who may not possess the financial acumen to determine when the AI is hallucinating. Using a longitudinal study, our analysis frames the prompts and subsequent findings within the four stages of the Dunning-Kruger effect to explore how users of varying expertise receive output from the LLMs. We find that ChatGPT cannot always fully distinguish between three different user groups. Our findings have important implications for accountants, educators, and students using LLMs as a tool in work and education and for the general population looking to bypass financial experts for their personal finance needs. Data Availability: Data will be made available upon request. JEL Classifications: M41.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".