Argument Structure Prediction in Online Conversations: A Comparative Study of Modeling Paradigms and Task Architectures

arXiv:2609.39225 · cs.CL, cs.IR · Submitted 2026-09-30 · Read on arXiv

cs.CL, cs.IR

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/pydantic/pydantic

Terminology

• Argument Mining — 21× in this paper
• Fine-Tuning — 21× in this paper · explained in 5 episodes
• Generalization — 9× in this paper · explained in 13 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Grounding — 3× in this paper · explained in 3 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Contextual Understanding — 2× in this paper · explained in 2 episodes
• Empirical Methods in Natural Language Processing — 2× in this paper
• F1 Score — 2× in this paper · explained in 3 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Cross-lingual Transfer — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Hallucination — 1× in this paper · explained in 4 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Inference Anchoring Theory — 1× in this paper
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Retrieval Augmented Generation (RAG) — 1× in this paper · explained in 4 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Unsupervised Cross-lingual Representation Learning at Scale
• Qwen2.5 Technical Report
• Gemma 3 Technical Report
• Efficient Guided Generation for Large Language Models

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