# Argument Undermining: Counter-Argument Generation by Attacking Weak Premises

arXiv: https://arxiv.org/abs/2105.11752 · HTML: https://mlsanity.com/paper/2105.11752/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents](https://mlsanity.com/paper/1901.08149/) (2019)
- [Aspect-Controlled Neural Argument Generation](https://mlsanity.com/paper/2005.00084/) (2020)
- [AMPERSAND: Argument Mining for PERSuAsive oNline Discussions](https://mlsanity.com/paper/2004.14677/) (2020)
- [Learning-to-Rank with BERT in TF-Ranking](https://mlsanity.com/paper/2004.08476/) (2020)
- [Neural Argument Generation Augmented with Externally Retrieved Evidence](https://mlsanity.com/paper/1805.10254/) (2018)
- [TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank](https://mlsanity.com/paper/1812.00073/) (2018)
- [Sentence-Level Content Planning and Style Specification for Neural Text Generation](https://mlsanity.com/paper/1909.00734/) (2019)
- [Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation](https://mlsanity.com/paper/1805.08191/) (2018)
- [Out of the Echo Chamber: Detecting Countering Debate Speeches](https://mlsanity.com/paper/2005.01157/) (2020)
- [The workweek is the best time to start a family -- A Study of GPT-2 Based Claim Generation](https://mlsanity.com/paper/2010.06185/) (2020)
- [Detecting Attackable Sentences in Arguments](https://mlsanity.com/paper/2010.02660/) (2020)
- [Belief-based Generation of Argumentative Claims](https://mlsanity.com/paper/2101.09765/) (2021)
- [A Dataset of General-Purpose Rebuttal](https://mlsanity.com/paper/1909.00393/) (2019)

## Built on by (most cited first, 22 of 22)

- [Exploring the Potential of Large Language Models in Computational Argumentation](https://mlsanity.com/paper/2311.09022/) (2023)
- [Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation](https://mlsanity.com/paper/2303.06433/) (2023)
- [The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments](https://mlsanity.com/paper/2203.14563/) (2022)
- [NLP for Counterspeech against Hate: A Survey and How-To Guide](https://mlsanity.com/paper/2403.20103/) (2024)
- [Exploring Jiu-Jitsu Argumentation for Writing Peer Review Rebuttals](https://mlsanity.com/paper/2311.03998/) (2023)
- [Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation](https://mlsanity.com/paper/2312.13608/) (2023)
- [Is Safer Better? The Impact of Guardrails on the Argumentative Strength of LLMs in Hate Speech Countering](https://mlsanity.com/paper/2410.03466/) (2024)
- [To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support](https://mlsanity.com/paper/2305.16799/) (2023)
- [ArgU: A Controllable Factual Argument Generator](https://mlsanity.com/paper/2305.05334/) (2023)
- [HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicine](https://mlsanity.com/paper/2306.06029/) (2023)
- [NLAS-multi: A Multilingual Corpus of Automatically Generated Natural Language Argumentation Schemes](https://mlsanity.com/paper/2402.14458/) (2024)
- [Dynamic Knowledge Integration for Evidence-Driven Counter-Argument Generation with Large Language Models](https://mlsanity.com/paper/2503.05328/) (2025)
- [LPAttack: A Feasible Annotation Scheme for Capturing Logic Pattern of Attacks in Arguments](https://mlsanity.com/paper/2204.01512/) (2022)
- [Overview of AI-Debater 2023: The Challenges of Argument Generation Tasks](https://mlsanity.com/paper/2407.14829/) (2024)
- [Conclusion-based Counter-Argument Generation](https://mlsanity.com/paper/2301.09911/) (2023)
- [Revisiting the Role of Similarity and Dissimilarity in Best Counter Argument Retrieval](https://mlsanity.com/paper/2304.08807/) (2023)
- [ArgBench: Benchmarking LLMs on Computational Argumentation Tasks](https://mlsanity.com/paper/2604.17366/) (2026)
- [Teach Me How to Improve My Argumentation Skills: A Survey on Feedback in Argumentation](https://mlsanity.com/paper/2307.15341/) (2023)
- [XAM: Interactive Explainability for Authorship Attribution Models](https://mlsanity.com/paper/2512.06924/) (2025)
- [SAD: A Large-Scale Strategic Argumentative Dialogue Dataset](https://mlsanity.com/paper/2601.07423/) (2026)
- [PTCG: Persona-guided Tree-based Counterargument Generation](https://mlsanity.com/paper/2609.07120/) (2026)
- [A Multi-Stage Agentic Framework for Effective Counter-Narrative Generation and Refinement](https://mlsanity.com/paper/2609.14178/) (2026)

## Abstract (arXiv)

Text generation has received a lot of attention in computational argumentation research as of recent. A particularly challenging task is the generation of counter-arguments. So far, approaches primarily focus on rebutting a given conclusion, yet other ways to counter an argument exist. In this work, we go beyond previous research by exploring argument undermining, that is, countering an argument by attacking one of its premises. We hypothesize that identifying the argument's weak premises is key to effective countering. Accordingly, we propose a pipeline approach that first assesses the premises' strength and then generates a counter-argument targeting the weak ones. On the one hand, both manual and automatic evaluation proves the importance of identifying weak premises in counter-argument generation. On the other hand, when considering correctness and content richness, human annotators favored our approach over state-of-the-art counter-argument generation.