Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Financial literacy is a well-established area of research. The incorporation of Large Language Models (LLMs) into FinTech solutions has opened up a new avenue of research to determine how LLMs can be used to interact with users and improve financial literacy. Following previous research that focused purely on the GPT models, we have extended this work to investigate how Gemini, Copilot, and DeepSeek respond to basic accounting and finance questions from users, ranging from financially unsophisticated to expert. To investigate this, we use Cosine Similarity and the Flesch Reading Ease Score. The Cosine Similarity results show that the LLMs struggle with distinguishing between users, often defaulting to communicating as an expert. We also conduct a post-hoc analysis where the generated texts are analyzed by an accounting expert. We find that some LLM generated answers are misleading, which could place LLM users with little to no financial literacy at a significant disadvantage, and could lead to them making disastrous financial decisions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it