Asking an LLM 'could you be wrong?' after its answer surfaces its own biases, omitted evidence, and alternative perspectives in qualitative demonstrations on three tasks.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Could you be wrong: Debiasing LLMs using a metacognitive prompt for improving human decision making
Asking an LLM 'could you be wrong?' after its answer surfaces its own biases, omitted evidence, and alternative perspectives in qualitative demonstrations on three tasks.