# Finding Convincing Arguments Using Scalable Bayesian Preference Learning

arXiv: https://arxiv.org/abs/1806.02418 · HTML: https://mlsanity.com/paper/1806.02418/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2018.

## Built on by (most cited first, 17 of 17)

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- [Persuasiveness of Generated Free-Text Rationales in Subjective Decisions: A Case Study on Pairwise Argument Ranking](https://mlsanity.com/paper/2406.13905/) (2024)
- [Argument Quality Assessment in the Age of Instruction-Following Large Language Models](https://mlsanity.com/paper/2403.16084/) (2024)
- [Assisting Decision Making in Scholarly Peer Review: A Preference Learning Perspective](https://mlsanity.com/paper/2109.01190/) (2021)
- [Active Learning for Argument Strength Estimation](https://mlsanity.com/paper/2109.11319/) (2021)
- [Investigating Subjective Factors of Argument Strength: Storytelling, Emotions, and Hedging](https://mlsanity.com/paper/2507.17409/) (2025)
- [A Generalizable Rhetorical Strategy Annotation Model Using LLM-based Debate Simulation and Labelling](https://mlsanity.com/paper/2510.15081/) (2025)
- [Transfer Learning of Lexical Semantic Families for Argumentative Discourse Units Identification](https://mlsanity.com/paper/2209.02495/) (2022)
- [Persona Knowledge-Aligned Prompt Tuning Method for Online Debate](https://mlsanity.com/paper/2410.04239/) (2024)
- [Argument Reconstruction as Supervision for Critical Thinking in LLMs](https://mlsanity.com/paper/2603.17432/) (2026)
- [Argument Quality Assessment with Large Language Models: A Pairwise Bradley-Terry Approach](https://mlsanity.com/paper/2605.28313/) (2026)

## Abstract (arXiv)

We introduce a scalable Bayesian preference learning method for identifying convincing arguments in the absence of gold-standard rat- ings or rankings. In contrast to previous work, we avoid the need for separate methods to perform quality control on training data, predict rankings and perform pairwise classification. Bayesian approaches are an effective solution when faced with sparse or noisy training data, but have not previously been used to identify convincing arguments. One issue is scalability, which we address by developing a stochastic variational inference method for Gaussian process (GP) preference learning. We show how our method can be applied to predict argument convincingness from crowdsourced data, outperforming the previous state-of-the-art, particularly when trained with small amounts of unreliable data. We demonstrate how the Bayesian approach enables more effective active learning, thereby reducing the amount of data required to identify convincing arguments for new users and domains. While word embeddings are principally used with neural networks, our results show that word embeddings in combination with linguistic features also benefit GPs when predicting argument convincingness.