Extracting Arguments, Not Just Classifying Them: Instruction-Tuned LLMs for Generative Component Detection

arXiv:2609.24855 · cs.AI, cs.CL · Submitted 2026-09-21 · Read on arXiv

cs.AI, cs.CL

Submitted: 2026-09-21

Updated: 2026-09-21

Journal ref: COLM 2026 - Third Annual Conference on Language Modeling, Oct 2026, San Francisco, CA, United States

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fidelity — 11× in this paper · explained in 2 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• F1 Score — 7× in this paper · explained in 3 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Argumentation Mining — 6× in this paper
• Argumentative Component Detection — 6× in this paper
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• Generative Models — 5× in this paper · explained in 2 episodes
• Core — 4× in this paper · explained in 2 episodes
• Hallucination — 4× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 4× in this paper · explained in 6 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Instruction Tuning — 3× in this paper · explained in 2 episodes
• Graph Construction — 2× in this paper · explained in 3 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Annotator Agreement — 1× in this paper · explained in 1 episode
• Conditional Random Field — 1× in this paper
• Consistency — 1× in this paper · explained in 3 episodes
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Neural End-to-End Learning for Computational Argumentation Mining
• Leveraging Small LLMs for Argument Mining in Education: Argument Component Identification, Classification, and Assessment
• The Llama 3 Herd of Models
• RoBERTa: A Robustly Optimized BERT Pretraining Approach
• OPT: Open Pre-trained Transformer Language Models

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