Causality-driven Argument Sufficiency Assessment: A Zero-Shot Framework for Identifying Insufficient Arguments

Core Concepts

The probability of sufficiency (PS) can be used to determine if the premises of an argument sufficiently support its conclusion, without relying on subjective human annotations.

Abstract

This paper proposes CASA, a zero-shot causality-driven argument sufficiency assessment framework. The key idea is to formulate the task using the concept of probability of sufficiency (PS) from causal inference literature. PS measures how likely introducing the premise event would lead to the conclusion when both the premise and conclusion events are absent.
To estimate PS, CASA leverages large language models (LLMs) to:

Sample contexts that are inconsistent with the premise and conclusion.
Revise the contexts by injecting the premise event and evaluate if the conclusion is still supported.

The step-wise evaluation shows that LLMs are capable of generating textual data that conform to certain conditions and making interventions on situations in the form of natural language.
Experiments on two logical fallacy detection datasets demonstrate that CASA accurately identifies insufficient arguments, outperforming zero-shot and one-shot baselines. CASA is further applied in a writing assistance application, where the objections generated by CASA help improve the sufficiency of student-written arguments.

Stats

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Quotes

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Key Insights Distilled From

CASA

by Xiao Liu,Yan... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2401.05249.pdf

Deeper Inquiries

How can the causality-driven framework of CASA be extended to assess the sufficiency of arguments with multiple premises?

In order to assess the sufficiency of arguments with multiple premises, the causality-driven framework of CASA can be extended by considering each premise individually in the context sampling and intervention steps. Instead of focusing on a single premise, the framework can sample contexts that are inconsistent with each premise and the conclusion. By revising these contexts under interventions for each premise, the framework can estimate the probability of sufficiency by evaluating the impact of each premise on the conclusion separately. This approach allows for a more comprehensive analysis of the sufficiency of arguments with multiple premises.

What are the potential limitations of using LLMs to sample contexts and make interventions in the CASA framework, and how can these limitations be addressed?

One potential limitation of using LLMs to sample contexts and make interventions in the CASA framework is the generation of low-quality or irrelevant contexts. LLMs may not always produce contexts that accurately reflect the conditions of ¬Premise and ¬Conclusion, leading to biased estimations of sufficiency. To address this limitation, it is essential to fine-tune the LLMs on relevant data and provide clear instructions to generate contexts that align with the premises and conclusion of the argument. Additionally, incorporating human oversight and validation can help ensure the quality of the generated contexts and interventions.

How might the insights from CASA's causality-driven approach to argument sufficiency assessment be applied to other areas of natural language processing, such as dialogue systems or automated essay scoring?

The insights from CASA's causality-driven approach can be applied to other areas of natural language processing, such as dialogue systems or automated essay scoring, in the following ways:

Dialogue Systems: By incorporating causal reasoning into dialogue systems, the systems can better understand the implications of different responses and make more informed decisions during conversations. This can lead to more coherent and contextually relevant interactions with users.
Automated Essay Scoring: Applying causality-driven reasoning to automated essay scoring can help in evaluating the logical coherence and sufficiency of arguments presented in essays. By considering the causal relationships between premises and conclusions, the scoring systems can provide more nuanced feedback on the strength of arguments and the overall structure of essays.