Papers with argument generation
Aspect-Controlled Neural Argument Generation (2021.naacl-main)
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| Challenge: | Current argument generation models produce lengthy texts and allow the user little control over the aspect the argument should address. |
| Approach: | They propose a language model that can be controlled to generate sentence-level arguments for a given topic, stance, and aspect. |
| Outcome: | The proposed model generates high-quality arguments for argumentation and counter-arguments. |
Exploring the Potential of Large Language Models in Computational Argumentation (2024.acl-long)
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| Challenge: | Argumentation is an essential tool in various domains, including law, public policy, and artificial intelligence. |
| Approach: | They propose to evaluate LLMs on various computational argumentation tasks . they organize existing tasks into six main categories and standardize the format of 14 datasets . |
| Outcome: | The proposed model performs well on argument mining and argument generation tasks. |
RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators (2022.naacl-main)
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| Challenge: | Using a framework based on Rhetorical Structure Theory, we aim to improve the cohesion and coherence of long-form text generated by language models. |
| Approach: | They propose a framework that utilises Rhetorical Structure Theory to control the discourse structure, semantics and topics of generated text. |
| Outcome: | The proposed framework performs competitively against existing models while offering significantly more controls over generated text than alternative methods. |
Fixed That for You: Generating Contrastive Claims with Semantic Edits (N19-1)
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| Challenge: | Understanding contrastive opinions is a key component of argument generation. |
| Approach: | They create a corpus of Reddit comment pairs and train neural models to edit the original claim and produce a new claim with a different view. |
| Outcome: | The proposed model improves on a sequence-to-sequence baseline and compared to a human evaluation for fluency, coherence, and contrast. |
A Logical Fallacy-Informed Framework for Argument Generation (2025.naacl-long)
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| Challenge: | Argument generation is crucial in daily life and has numerous online and offline applications. |
| Approach: | They propose a fallacy-informed preference optimization that includes a classification loss to capture the fine-grained information on fallacy types to help LLMs generate logically sound arguments. |
| Outcome: | The proposed method reduces fallacy errors by 17.5% on argument generation tasks and outperforms fine-tuned baselines and other preference optimization methods, such as DPO. |
Uncovering Argumentative Flow: A Question-Focus Discourse Structuring Framework (2025.emnlp-main)
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| Challenge: | Existing structure modeling approaches fail to capture the author’s rhetorical intent and reasoning process. |
| Approach: | They propose a Question-Focus discourse structuring framework that explicitly models the underlying argumentative flow by anchoring each argumentative unit to a guiding question and a set of attentional foci. |
| Outcome: | The proposed framework outperforms baseline models and curated models on an argument reconstruction task in Chinese think-tank articles and claims coverage. |
ArgU: A Controllable Factual Argument Generator (2023.acl-long)
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| Challenge: | Effective argumentation is essential towards a purposeful conversation with a satisfactory outcome. |
| Approach: | They propose a controllable neural argument generator capable of producing factual arguments from input facts and real-world concepts that can be explicitly controlled for stance and argument structure. |
| Outcome: | The proposed model produces factual arguments from input facts and real-world concepts that can be explicitly controlled for stance and argument structure using Walton’s argument scheme-based control codes. |
MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective (2022.emnlp-main)
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| Challenge: | Recent pre-trained language models have produced impressive results, but there is still a gap between human written texts and machine-generated outputs. |
| Approach: | They propose a multi-task training strategy for long text generation grounded on the cognitive theory of writing. |
| Outcome: | The proposed model achieves better results on three open-ended generation tasks than baselines. |
Decomposing Argumentative Essay Generation via Dialectical Planning of Complex Reasoning (2024.findings-acl)
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| Challenge: | Argumentative Essay Generation (AEG) is a challenging task in computational argumentation, where detailed logical reasoning and effective rhetorical skills are essential. |
| Approach: | They propose an argumentative planning strategy for prompting large language models to generate high-quality essays by sketch planning and dialectical planning. |
| Outcome: | The proposed method generates more dialectical and persuasive essays with higher diversity compared to baselines. |
From Values to Opinions: Predicting Human Behaviors and Stances Using Value-Injected Large Language Models (2023.emnlp-main)
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| Challenge: | Existing large-scale surveys soliciting opinions on issues can be costly and laborious. |
| Approach: | They propose to use value-injected large language models to inject a target value distribution into large language model (LLM) and have them predict opinions and behaviors of people with similar values. |
| Outcome: | The proposed method significantly outperforms baseline methods on four tasks and the results suggest opinions and behaviors can be better predicted using value-injected LLMs. |
Perspective-driven Preference Optimization with Entropy Maximization for Diverse Argument Generation (2025.findings-emnlp)
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| Challenge: | Argument generation with diverse perspectives is essential for fostering balanced discourse and mitigating bias. |
| Approach: | They propose a Perspective-aware Preference Optimization with Entropy Maximization framework for diverse argument generation. |
| Outcome: | The proposed framework enhances perspective diversity through preference optimization based on the constructed preference dataset . |
From Atomic to Complex tasks: Cross-Tasking Improves Zero-Shot Argument Generation and Retrieval (2026.findings-acl)
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| Challenge: | Argument retrieval and argument generation (AR) have attracted significant attention in recent years . argllms are a powerful tool for analyzing argument quality and extracting argument units . |
| Approach: | They propose that argument generation and argument retrieval could leverage cross-tasking atomic argument mining and argument quality assessment tasks even if there is no supervision. |
| Outcome: | The proposed framework outperforms base models in argument generation and retrieval tasks even without supervision. |