When helpfulness backfires: LLMs and the risk of false medical information due to sycophantic behavior
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
Large language models (LLMs) exhibit a vulnerability arising from being trained to be helpful: a tendency to comply with illogical requests that would generate false information, even when they have the knowledge to identify the request as illogical. This study investigated this vulnerability in the medical domain, evaluating five frontier LLMs using prompts that misrepresent equivalent drug relationships. We tested baseline sycophancy, the impact of prompts allowing rejection and emphasizing factual recall, and the effects of fine-tuning on a dataset of illogical requests, including out-of-distribution generalization. Results showed high initial compliance (up to 100%) across all models, prioritizing helpfulness over logical consistency. Prompt engineering and fine-tuning improved performance, improving rejection rates on illogical requests while maintaining general benchmark performance. This demonstrates that prioritizing logical consistency through targeted training and prompting is crucial for mitigating the risk of generating false medical information and ensuring the safe deployment of LLMs in healthcare.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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