Exploring ChatGPT’s capabilities in solving accounting standards problems: the case of IAS 37
Bibliographic record
Abstract
Using a quasi-experimental method and content analysis as a technique, this study tests ChatGPT, in its version 4, by assessing its textual characteristics and overall understanding regarding the recognition criteria of provisions under International Accounting Standards (IAS) 37, as issued by the International Accounting Standards Board (IASB). For this purpose, it uses a set of questions (input) from the IASB's illustrative examples to compare the answers (output) from IASB and ChatGPT in two distinct strategies: with and without prompting. The findings indicate that ChatGPT’s answers are wordier, have higher magnitude levels, and are more predominantly inserted in Business and Finance. The no-prompting strategy is globally more negative and subjective, while the prompting one improves the answers’ focus and readability, also presenting more diverse tones in its textual characteristics, similar to what was found in the IASB's answers. However, some answers were not globally accurate in both strategies. These findings provide insights into how ChatGPT, as one of the most disseminated artificial intelligence tools, can be used by accounting professionals and educators, being aware of the potential risks and benefits from both strategies underlying this experiment. Then, by considering those aspects, practitioners, including accountants and managers, but also investors can use it to understand the matters and issues under assessment in a given situation, as well as the sources to consider when preparing financial statements or making a decision. Academics can also use it to open up discussions and promote students’ critical thinking skills in a classroom environment.
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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 | 0.027 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".