Evaluation of an LLM in Identifying Logical Fallacies: A Call for Rigor When Adopting LLMs in HCI Research (2404.05213v1)

Abstract: There is increasing interest in the adoption of LLMs in HCI research. However, LLMs may often be regarded as a panacea because of their powerful capabilities with an accompanying oversight on whether they are suitable for their intended tasks. We contend that LLMs should be adopted in a critical manner following rigorous evaluation. Accordingly, we present the evaluation of an LLM in identifying logical fallacies that will form part of a digital misinformation intervention. By comparing to a labeled dataset, we found that GPT-4 achieves an accuracy of 0.79, and for our intended use case that excludes invalid or unidentified instances, an accuracy of 0.90. This gives us the confidence to proceed with the application of the LLM while keeping in mind the areas where it still falls short. The paper describes our evaluation approach, results and reflections on the use of the LLM for our intended task.

• The paper presents a methodological framework for evaluating GPT-4's fallacy detection, reporting 79% overall accuracy and 90% after data refinement.
• It emphasizes rigorous evaluation in HCI research to ensure LLMs address digital misinformation effectively.
• The findings urge researchers to balance the strengths of LLMs with critical assessment to mitigate limitations in logical reasoning detection.

The paper "Evaluation of an LLM in Identifying Logical Fallacies: A Call for Rigor When Adopting LLMs in HCI Research" explores the application of a LLM in identifying logical fallacies within the context of Human-Computer Interaction (HCI) research. Given the burgeoning interest in integrating LLMs into various research domains, including HCI, the authors advocate for a critical and rigorous evaluation approach to ensure that LLMs are fit for their intended tasks.

The primary focus of the paper is to assess the effectiveness of GPT-4 in identifying logical fallacies, which are common errors in reasoning that can undermine the validity of an argument. The ability to detect these fallacies is particularly pertinent as part of a broader strategy to combat digital misinformation, a significant issue in today's information landscape.

The researchers developed a methodological framework for evaluating GPT-4's performance, utilizing a labeled dataset as the benchmark. In their analysis, GPT-4 achieved an accuracy rate of 0.79 in identifying logical fallacies. However, for the specific use case excluding instances that were invalid or misidentified, the accuracy rose to 0.90. These findings indicate that while GPT-4 shows promise, it does have some limitations that necessitate caution.

Significantly, the paper underscores the importance of not overly relying on the perceived omnipotence of LLMs. The authors reflect on the areas where GPT-4 performs well and where it falls short, suggesting that researchers and practitioners must maintain a critical perspective. This balanced view aims to harness the strengths of LLMs while being mindful of their imperfections.

In conclusion, this paper contributes to the discourse on the responsible adoption of LLMs in HCI research, urging the community to prioritize rigorous evaluation to ensure these powerful tools are leveraged effectively and appropriately. The findings provide a foundation for proceeding with the use of LLMs in tasks such as identifying logical fallacies, all the while acknowledging and addressing their constraints.