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Linguistic Calibration of Language Models
April 2, 2024, 7:42 p.m. | Neil Band, Xuechen Li, Tengyu Ma, Tatsunori Hashimoto
cs.LG updates on arXiv.org arxiv.org
Abstract: Language models (LMs) may lead their users to make suboptimal downstream decisions when they confidently hallucinate. This issue can be mitigated by having the LM verbally convey the probability that its claims are correct, but existing models cannot produce text with calibrated confidence statements. Through the lens of decision-making, we formalize linguistic calibration for long-form generations: an LM is linguistically calibrated if its generations enable its users to make calibrated probabilistic predictions. This definition enables …
abstract arxiv confidence cs.ai cs.cl cs.lg decision decisions issue language language models lms making probability stat.ml text through type
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