A new research paper explores argument structure prediction (ASP) in online conversations, comparing various modeling paradigms and task architectures. The study systematically evaluates supervised fine-tuning and prompt-based large language models (LLMs) across different architectures, focusing on performance, generalization, schema compliance, and efficiency. The findings indicate that identifying argumentative relations is a significant challenge in dialogical settings due to the implicit and context-dependent nature of argumentation. The researchers have released their data processing pipeline and modeling framework to support future work in this area. AI
IMPACT This research could improve how AI systems understand and generate arguments in conversational contexts.
RANK_REASON The cluster contains an academic paper detailing a comparative study of modeling paradigms for argument structure prediction.
Read on arXiv cs.IR (Information Retrieval) →
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- Inference Anchoring Theory
- large language models (LLMs)
- ScienceCast
- Siddharth Bhargava
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