ITA is a neurosymbolic framework that optimizes LLM argument generation and scoring via argumentation semantics to yield faithful ternary claim verifications on two datasets.
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A quantitative bipolar argumentation framework with five metrics is proposed to evaluate LLM debate summaries by comparing argument structures extracted from source debates and their summaries.
LLMs show structured attribute-driven decisions that a behavioral model can predict, but self-reports recover those drivers only partially, indicating superficial beliefs.
citing papers explorer
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Neurosymbolic Learning for Inference-Time Argumentation
ITA is a neurosymbolic framework that optimizes LLM argument generation and scoring via argumentation semantics to yield faithful ternary claim verifications on two datasets.
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Evaluating LLM-Driven Summarisation of Parliamentary Debates with Computational Argumentation
A quantitative bipolar argumentation framework with five metrics is proposed to evaluate LLM debate summaries by comparing argument structures extracted from source debates and their summaries.
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Superficial Beliefs in LLM Decision-Making
LLMs show structured attribute-driven decisions that a behavioral model can predict, but self-reports recover those drivers only partially, indicating superficial beliefs.