arXiv AI
Argument-Aware Semantic Alignment of Normative Texts: A Toulmin-Based Neuro-Symbolic Approach
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NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models
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arXiv Computation and Language
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Embedding Models for Stance-Aware Argument Retrieval
The paper investigates how dense embedding models can be used for stance-aware argument retrieval, a task that requires both topic relevance and correct stance (support or attack) toward a claim. Experiments reveal that current models favor topical overlap and ignore stance, and that contrastive training to fix this bias leads to over-correction, where models focus too much on polarity keywords at the expense of topic relevance. To address this, the authors propose diagnostic word-ablation metrics and a data‑centric solution involving a balanced argument curriculum and LLM‑augmented stance‑inverted arguments, which helps powerful models learn deeper directional logic and improves stance‑aware retrieval performance.
arXiv AI
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CANDI: Contextual Alignment for Niche Domains Question Answering
arXiv:2607. 11891v1 Announce Type: cross Abstract: The deployment of large language models (LLMs) in specialized domains like medical diagnostics and financial advisory necessitates evaluating capabilities beyond general knowledge.
arXiv AI
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Logical Embeddings for Argument Analysis
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arXiv AI
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Neurosymbolic Learning for Inference-Time Argumentation
arXiv:2605. 20098v2 Announce Type: replace Abstract: Claim verification is an important problem in high-stakes settings, including health and finance.
arXiv AI
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Sound and Complete Neurosymbolic Reasoning with LLM-Grounded Interpretations
arXiv:2507. 09751v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but exhibit problems with logical consistency in their output.