Probing neural language models for understanding of words of estimative probability (2023)

Abstract: Words of Estimative Probability (WEP) are phrases used to express the plausibility of a statement. Examples include terms like \textit{probably, maybe, likely, doubt, unlikely}, and \textit{impossible}. Surveys have shown that human evaluators tend to agree when assigning numerical probability levels to these WEPs. For instance, the term \textit{highly likely} equates to a median probability of $0.90{\pm}0.08$ according to a survey by \citet{fagen-ulmschneider}.In this study, our focus is to gauge the competency of neural language processing models in accurately capturing the consensual probability level associated with each WEP. Our first approach is utilizing the UNLI dataset \cite{chen-etal-2020-uncertain}, which links premises and hypotheses with their perceived joint probability $p$. From this, we craft prompts in the form: "[\textsc{Premise}]. [\textsc{Wep}], [\textsc{Hypothesis}].” This allows us to evaluate whether language models can predict if the consensual probability level of a WEP aligns closely with $p$.In our second approach, we develop a dataset based on WEP-focused probabilistic reasoning to assess if language models can logically process WEP compositions. For example, given the prompt "[\textsc{EventA}] \textit{is likely}. [\textsc{EventB}] \textit{is impossible}.”, a well-functioning language model should not conclude that [\textsc{EventA$\&amp;$B}] is likely. Through our study, we observe that both tasks present challenges to out-of-the-box English language models. However, we also demonstrate that fine-tuning these models can lead to significant and transferable improvements.

## introduction expression of uncertainty is an important part of communication. formal statistics are the rigorous way to quantify uncertainty but do not fit all communication styles. words of estimative probability (wep) such as maybe and believe are adverbs or verbs that are informal alternatives. @xcite noted the importance of clarifying wep meaning for intelligence analysis in the central intelligence agency, and provided guidelines for mapping wep to numerical probabilities. several studies then measured the human perceptions of probability words and discovered some agreement with @xcite 's guidelines. in this work, we use the scale derived from a survey (fagen-ulmschneider, 2015), which is the largest and most recent wep perception survey available. 123 participants were asked to label wep with numerical probabilities. we use the median of the participant answers to assign a consensual value to each wep. associated probabilities for the 19 wep we use are available in appendix a, here, we assess whether neural language models learn the consensual probability judgment of wep from language modeling pretraining. we develop datasets and a methodology to probe neural language model understanding of wep. the first dataset leverages previously annotated probability scores between a premise and a hypothesis, in order to measure a language model's ability to capture the agreement between numerical probabilities and wep-expressed probabilities. the second dataset is based on compositions of facts with wep-expressed probabilities, and measures verbal probabilistic reasoning in language models. our contributions are as follows: (i) two datasets and methods to measure understanding of wep; and (ii) evaluation of the ability of neural language models (gpt2, roberta-trained on mnli) to tackle wep-related problems, showing that offthe-shelf models are very little influenced by them, even though fine-tuning on our constructed datasets quickly leads to high accuracies. the code and generated datasets are publicly available 1 2 related work our work probes a particular aspect of language understanding. we do not analyze the inside of the models @xcite . we focus on the models' ability to perform controlled tasks @xcite involving wep. wep were studied in the context of intelligence analysis and linguistics, our work is the first to look at them through natural language processing (nlp) models. our study also pertains to nlp analyses of logical reasoning and probability problems, and to uncertainty in natural language inference tasks. linguistics study of @xcite 's seminal work was the first to link wep and numerical probability estimates, with intelligence analysis motivations (dhami and mandel, 2021) and a prescriptivist approach. this inspired further quantifications of human perceptions of wep, in the context of medical reports (o @xcite and weather reports @xcite . fagen-ulmschneider (2015) proposed the largest survey up to date with 123 participants about generaldomain wep perception. logical and probabilistic reasoning another strand of work probes nlp text encoders capabilities, notably reasoning abilities. weston et al. (2015) probed understanding of specific problems like negation, spatial and temporal reasoning with the babi dataset. richardson et al. (2020) probe understanding of first-order logic reasoning, sileo and lernould (2023) probe epistemic logic reasoning. our work is the first to address probabilistic logic, alongside dries et al. (2017); suster et al. @xcite who construct a dataset of natural language probability problems, e.g., "a bag has 4 white and 8 blue marbles. you pull out one marble and it is blue. you pull out another marble, what is the probability of it being white?". they also rely on the problog solver @xcite , but focus on numeric probability problems. by contrast, our work targets wep, and textual probabilistic logical reasoning. natural language inference, uncertainty, modality, evidentiality uncertainty was also studied in the context of natural language inference tasks. @xcite study the disagreement across annotators when labeling entailment relationships. @xcite annotate graded entailment with 5 probability levels, and the unli dataset @xcite ) go further by annotating numerical probabilities. our work also pertains to the study of modality @xcite and more particularly evidentiality @xcite , but where previous work focused on wep. ## experiments we conduct verbalization validity prediction (binary classification task of wep correctness detection between two candidates) under two settings. ## conclusion we investigated wep understanding in neural language models with new datasets and experiments, showing that wep processing is challenging but helped by supervision which leads to transferable improvement. future work could extract wep probability scales from the unli dataset as an alternative to human perception surveys, but our work suggests that this requires language modeling progress.