Argument Reconstruction as Supervision for Critical Thinking in LLMs
To think critically about arguments, human learners are trained to identify, reconstruct, and evaluate arguments. Argument reconstruction is especially important because it makes an argument's underlying inferences explicit. However, it remains unclear whether LLMs can similarly enhance their critical thinking ability by learning to reconstruct arguments. To address this question, we introduce a holistic framework with three contributions. We (1) propose an engine that automatically reconstructs arbitrary arguments (GAAR), (2) synthesize a new high-quality argument reconstruction dataset (Arguinas) using the GAAR engine, and (3) investigate whether learning argument reconstruction benefit...
| Authors | Hyun Ryu, Gyouk Chu, Gregor Betz et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 March 2026 |
| Last updated at source | 14 May 2026 |
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