Can Language Models Recognize Convincing Arguments?

• URL: http://arxiv.org/abs/2404.00750v1
• Date: Sun, 31 Mar 2024 17:38:33 GMT
• Title: Can Language Models Recognize Convincing Arguments?
• Authors: Paula Rescala, Manoel Horta Ribeiro, Tiancheng Hu, Robert West,
• Abstract summary: Large Language Models (LLMs) have raised concerns about their potential misuse for creating personalized, convincing misinformation and propaganda. We study their performance on the related task of detecting convincing arguments. We show that LLMs perform on par with humans in these tasks and that combining predictions from different LLMs yields significant performance gains.
• Score: 12.458437450959416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The remarkable and ever-increasing capabilities of Large Language Models (LLMs) have raised concerns about their potential misuse for creating personalized, convincing misinformation and propaganda. To gain insights into LLMs' persuasive capabilities without directly engaging in experimentation with humans, we propose studying their performance on the related task of detecting convincing arguments. We extend a dataset by Durmus & Cardie (2018) with debates, votes, and user traits and propose tasks measuring LLMs' ability to (1) distinguish between strong and weak arguments, (2) predict stances based on beliefs and demographic characteristics, and (3) determine the appeal of an argument to an individual based on their traits. We show that LLMs perform on par with humans in these tasks and that combining predictions from different LLMs yields significant performance gains, even surpassing human performance. The data and code released with this paper contribute to the crucial ongoing effort of continuously evaluating and monitoring the rapidly evolving capabilities and potential impact of LLMs.

Related papers

• Persuasiveness of Generated Free-Text Rationales in Subjective Decisions: A Case Study on Pairwise Argument Ranking [4.1017420444369215]
  We analyze generated free-text rationales in tasks with subjective answers. We focus on pairwise argument ranking, a highly subjective task with significant potential for real-world applications. Our findings suggest that open-source LLMs, particularly Llama2-70B-chat, are capable of providing highly persuasive rationalizations.
  arXiv  Detail & Related papers  (2024-06-20T00:28:33Z)
• Can formal argumentative reasoning enhance LLMs performances? [0.3659498819753633]
  We present a pipeline (MQArgEng) to evaluate the effect of introducing computational argumentation semantics on the performance of Large Language Models (LLMs) Exploratory results indicate that MQArgEng provides a moderate performance gain in most of the examined topical categories and, as such, show promise and warrant further research.
  arXiv  Detail & Related papers  (2024-05-16T22:09:31Z)
• Evaluating Interventional Reasoning Capabilities of Large Language Models [58.52919374786108]
  Large language models (LLMs) can estimate causal effects under interventions on different parts of a system. We conduct empirical analyses to evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention. We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types, and enable a study of intervention-based reasoning.
  arXiv  Detail & Related papers  (2024-04-08T14:15:56Z)
• The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition [74.04775677110179]
  In-context Learning (ICL) has emerged as a powerful paradigm for performing natural language tasks with Large Language Models (LLM) We show that LLMs have strong yet inconsistent priors in emotion recognition that ossify their predictions. Our results suggest that caution is needed when using ICL with larger LLMs for affect-centered tasks outside their pre-training domain.
  arXiv  Detail & Related papers  (2024-03-25T19:07:32Z)
• Comparing Rationality Between Large Language Models and Humans: Insights and Open Questions [6.201550639431176]
  This paper focuses on the burgeoning prominence of large language models (LLMs) We underscore the pivotal role of Reinforcement Learning from Human Feedback (RLHF) in augmenting LLMs' rationality and decision-making prowess.
  arXiv  Detail & Related papers  (2024-03-14T18:36:04Z)
• CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
  We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark. In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship. We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
  arXiv  Detail & Related papers  (2023-11-29T08:29:54Z)
• Exploring the Potential of Large Language Models in Computational Argumentation [54.85665903448207]
  Large language models (LLMs) have demonstrated impressive capabilities in understanding context and generating natural language. This work aims to embark on an assessment of LLMs, such as ChatGPT, Flan models, and LLaMA2 models, in both zero-shot and few-shot settings.
  arXiv  Detail & Related papers  (2023-11-15T15:12:15Z)
• Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Models [61.28463542324576]
  Vision-language models (VLMs) have recently demonstrated strong efficacy as visual assistants that can generate human-like outputs. We evaluate existing state-of-the-art VLMs and find that even the best-performing model is unable to demonstrate strong visual reasoning capabilities and consistency. We propose a two-stage training framework aimed at improving both the reasoning performance and consistency of VLMs.
  arXiv  Detail & Related papers  (2023-09-08T17:49:44Z)
• Revisiting the Reliability of Psychological Scales on Large Language Models [66.31055885857062]
  This study aims to determine the reliability of applying personality assessments to Large Language Models (LLMs) By shedding light on the personalization of LLMs, our study endeavors to pave the way for future explorations in this field.
  arXiv  Detail & Related papers  (2023-05-31T15:03:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.