Learning to fake it: limited responses and fabricated references provided by ChatGPT for medical questions
Bibliographic record
Abstract
Abstract Background ChatGPT have gained public notoriety and recently supported manuscript preparation. Our objective was to evaluate the quality of the answers and the references provided by ChatGPT for medical questions. Methods Three researchers asked ChatGPT a total of 20 medical questions and prompted it to provide the corresponding references. The responses were evaluated for quality of content by medical experts using a verbal numeric scale going from 0 to 100%. These experts were the corresponding author of the 20 articles from where the medical questions were derived. We planned to evaluate three references per response for their pertinence, but this was amended based on preliminary results showing that most references provided by ChatGPT were fabricated. Results ChatGPT provided responses varying between 53 and 244 words long and reported two to seven references per answer. Seventeen of the 20 invited raters provided feedback. The raters reported limited quality of the responses with a median score of 60% (1 st and 3 rd quartile: 50% and 85%). Additionally, they identified major (n=5) and minor (n=7) factual errors among the 17 evaluated responses. Of the 59 references evaluated, 41 (69%) were fabricated, though they appeared real. Most fabricated citations used names of authors with previous relevant publications, a title that seemed pertinent and a credible journal format. Interpretation When asked multiple medical questions, ChatGPT provided answers of limited quality for scientific publication. More importantly, ChatGPT provided deceptively real references. Users of ChatGPT should pay particular attention to the references provided before integration into medical manuscripts.
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 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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".