CASA : Causality-driven Argument Sufficiency Assessment (2024)

Abstract: The argument sufficiency assessment task aims to determine if the premises of a given argument support its conclusion.To tackle this task, existing works often train a classifier on data annotated by humans. However, annotating data is laborious, and annotations are often inconsistent due to subjective criteria. Motivated by the definition of probability of sufficiency (PS) in the causal literature, we proposeCASA, a zero-shot causality-driven argument sufficiency assessment framework. PS measures how likely introducing the premise event would lead to the conclusion when both the premise and conclusion events are absent. To estimate this probability, we propose to use large language models (LLMs) to generate contexts that are inconsistent with the premise and conclusion and revise them by injecting the premise event.Experiments on two logical fallacy detection datasets demonstrate that CASA accurately identifies insufficient arguments. We further deploy CASA in a writing assistance application, and find that suggestions generated by CASA enhance the sufficiency of student-written arguments. Code and data are available at https://github.com/xxxiaol/CASA.

## introduction argumentation is an integral part of our daily verbal communication @xcite ). an argument is a series of statements consisting of premises and a conclusion. take the argument shown in figure 1 as an example: you shouldn't trust donald's views about politics. he's an alcoholic. the first sentence, which serves as the conclusion, is supported by the second sentence, acting as the premise. if we have techniques to assess the quality of arguments precisely, we can identify weaknesses in arguments and further improve them. previous works on argument sufficiency assessment train classifiers based on human annotations @xcite . however, the sufficiency criteria are vague and subjective among annotators. for example, @xcite collected annotations from seven annotators, but even the three annotators with the highest consensus only achieved an agreement of 0.28. 1 this inconsistency poses a challenge in learning an accurate model. in this paper, we propose casa, a zeroshot causality-driven argument sufficiency assessment framework by formulating the task with a concept borrowed from causality: the probability of sufficiency (ps) @xcite . intuitively, if x is a sufficient cause of y , the presence of x implies the subsequent occurrence of y . ps quantifies the probability that introducing x would produce y in the case where x and y are in fact absent: to measure ps of a given argument, there presents the following challenges: 1) how to measure the probabilities without observational data, i.e., how to estimate p (@xmath0, @xmath1. our approach tackles the challenges by leveraging the commonsense knowledge and reasoning abilities of large language models (llms) @xcite . specifically, we ask llms to sample data that are inconsistent with the premises and the conclusion, such as donald, being clear-minded, supports his political views with extensive research; and then revise the data to contain the premises, such as despite being an alcoholic, donald's views on politics are supported by extensive research. ## the casa framework notations. we define x as the occurrence of a premise event foot_2 : @xmath2, and y as the occurrence of a conclusion event: @xmath3. here 1(•) is the indicator function. both x and y are binary variables. y u indicates the value of y in the unit u. assumptions. our framework is based on two common assumptions in causal inference @xcite : w z x y argument: you shouldn't trust donald's views about politics. he's an alcoholic. premise: he's an alcoholic. conclusion: you shouldn't trust donald's views about politics. x: 1(premise) y: 1(conclusion) sample contexts c when @xmath4, @xmath5, which have been shaped by his sober perspective. …… claim extraction context sampling revision under intervention probability estimation revise c to r under intervention @xmath6: confounder z: mediator is a realization of extract premises and conclusion from the argument despite being an alcoholic, donald's political views are based on his own personal experiences and observations. …… w z x y estimate if the conclusion is supported under estimate if the conclusion is supported under is a realization of …… sufficient insufficient … figure 2: overall architecture of casa. given an argument, we first extract its premises and conclusion, sample contexts that are inconsistent with the premises and conclusion, revise the contexts to meet the premises, and finally estimate the probability of the conclusion. edge @xmath7. overview. as shown in figure 2 , we first extract the premise and conclusion from a given argument, then sample contexts that meet the conditions, make interventions on the contexts, and finally estimate the probability of the conclusion for each unit. ## analysis 4.1 ablation study to further investigate the effectiveness of casa components, we study several variants of casa: w/o intervention. in this ablation, we simply estimate p (@xmath8. concretely, we sample contexts for premise, and estimate the probability of conclusion based on contexts and premise. w/o condition on @xmath9. this variant estimates p (y (@xmath10, where the term @xmath11. in the context sampling step, we ask llms to generate contexts only consistent with ¬conclusion, and other steps are kept the same. w/o condition on @xmath12. this variant estimates p (y (@xmath13, and llms are asked to generate contexts consistent with ¬premise. intervention: concatenation. we study if the intervention step can be replaced by simply concatenating the context with premise. in this setting, the mediator z in the context remains unchanged. ## application: writing assistance we apply casa to a realistic scenario: providing writing suggestions for essays written by students. if casa identifies that an argument in an essay is insufficient, we extract explainable reasons from casa's reasoning process, and provide them as suggestions for revision. specifically, we generate objection situations (situations that challenge the sufficiency of the argument) out of intervened situations r that contradict the conclusion, by removing the premise from r. as shown in the example of figure 4 , the revised situation r 1 is converted to the objection situation s 1 by removing the premise co-education helps both genders to behave and cooperate and work together. the removal is automatically done we investigate (q1): whether casa is capable of generating rational and feasible objection situations to the essays, and (q2): whether revising based on the generated objection situations improves the sufficiency of the essays. ## conclusion we propose casa, a zero-shot argument sufficiency assessment framework driven by the causal concept of sufficiency. in the absence of observational data and intervention data, we sample contexts and make interventions with llms. casa is capable of identifying insufficient arguments on two logical fallacy detection datasets, and providing writing suggestions to further improve the sufficiency of human-written arguments.