mlsanityData to 14 Oct 2026

Will It Blend? Mixing Training Paradigms & Prompting for Argument Quality Prediction

arXiv 2209.08966 ↗First posted 19 Sep 2022cs.CL cs.AIcited by 7

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Abstract (arXiv)

This paper describes our contributions to the Shared Task of the 9th Workshop on Argument Mining (2022). Our approach uses Large Language Models for the task of Argument Quality Prediction. We perform prompt engineering using GPT-3, and also investigate the training paradigms multi-task learning, contrastive learning, and intermediate-task training. We find that a mixed prediction setup outperforms single models. Prompting GPT-3 works best for predicting argument validity, and argument novelty is best estimated by a model trained using all three training paradigms.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 19 of its 38 references that are in the map. First posted on arXiv 19 Sep 2022.

Builds on

19 of 38 references.

References · 19

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7 more
  1. 2021Overview of the 2021 Key Point Analysis Shared Taskcited by 23
  2. 2020Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluationcited by 20
  3. 2020Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processingcited by 17
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Cited by year

Citing papers per year: 2023: 4; 2024: 3.

7 papers.

  1. 2023Fine-grained Affective Processing Capabilities Emerging from Large Language Modelscited by 24
  2. 2024Can Large Language Models perform Relation-based Argument Mining?cited by 23
  3. 2023Do Differences in Values Influence Disagreements in Online Discussions?cited by 15
  4. 2023Similarity-weighted Construction of Contextualized Commonsense Knowledge Graphs for Knowledge-intense Argumentation Taskscited by 6
  5. 2023"I'd Like to Have an Argument, Please": Argumentative Reasoning in Large Language Modelscited by 4
  6. 2024Argument Quality Assessment in the Age of Instruction-Following Large Language Modelscited by 4
  7. 2024Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Studycited by 1