Probing neural language models for understanding of words of estimative probability

Abstract: Words of estimative probability (WEP) are expressions of a statement's plausibility (probably, maybe, likely, doubt, likely, unlikely, impossible...). Multiple surveys demonstrate the agreement of human evaluators when assigning numerical probability levels to WEP. For example, highly likely corresponds to a median chance of 0.90+-0.08 in Fagen-Ulmschneider (2015)'s survey. In this work, we measure the ability of neural language processing models to capture the consensual probability level associated to each WEP. Firstly, we use the UNLI dataset (Chen et al., 2020) which associates premises and hypotheses with their perceived joint probability p, to construct prompts, e.g. "[PREMISE]. [WEP], [HYPOTHESIS]." and assess whether LLMs can predict whether the WEP consensual probability level is close to p. Secondly, we construct a dataset of WEP-based probabilistic reasoning, to test whether LLMs can reason with WEP compositions. When prompted "[EVENTA] is likely. [EVENTB] is impossible.", a causal LLM should not express that [EVENTA&B] is likely. We show that both tasks are unsolved by off-the-shelf English LLMs, but that fine-tuning leads to transferable improvement.