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Linguistic Features for Interpretable Textual Entailment
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arXiv Computation and Language
Sep 22
Linguistic Features for Interpretable Textual Entailment
arXiv:2609.24932v1 Announce Type: new Abstract: Despite the success of neural models in natural language processing, their black-box nature limits interpretability and conceals the linguistic phenome...
arXiv Computation and Language
Oct 19
Inductive Learning of Concept Embeddings from Cross-Linguistic Colexification Patterns with Textual Attributes
The paper introduces an inductive method for learning concept embeddings from cross‑linguistic colexification networks combined with short textual descriptions. Unlike previous transductive approaches, this framework can embed unseen concepts using only a brief definition. The resulting embeddings correlate with cross‑linguistic similarity measures and generalize well to new concepts.
arXiv AI
Sep 1
Argument-Aware Semantic Alignment of Normative Texts: A Toulmin-Based Neuro-Symbolic Approach
arXiv:2608.29529v1 Announce Type: cross Abstract: Semantic alignment between specialized normative texts is challenging when equivalent requirements use different terms, syntax, and levels of abstrac...
arXiv Machine Learning
Oct 3
Beyond Linear Concepts: Discovering and Aligning Non-Linear Concept Manifolds in Large Language Models
The paper extends mechanistic interpretability of large language models by modeling concepts as low‑dimensional non‑linear manifolds rather than linear subspaces. It introduces a concept‑based alignment (CBA) score to compare these manifolds across layers and models, revealing block structures in intermediate layers, a shift from syntax‑dominated to mixed syntactic‑semantic concepts, and training‑dependent multilingual sharing. The study also shows that alignment patterns differ across model families and training stages, with adjacent stages aligning more closely than distant ones.
arXiv AI
Oct 2
A Comparative Explainability Framework for DeBERTa-v3 in Zero-Shot Medical Abstract Classification
The paper introduces a comparative explainability framework for auditing DeBERTa‑v3 in zero‑shot medical abstract classification. It evaluates five explanation methods—SHAP, LIME, occlusion, Input × Gradient, and Attention × Gradient—using a natural language inference engine on a balanced corpus of 1,000 abstracts per diagnostic category. The study finds that explanatory stability aligns with predictive certainty, identifies three systemic failure mechanisms, and recommends combining multiple explanation methods and quantitative agreement metrics for transformer‑based medical text classifiers.
arXiv AI
Jun 9
Explaining Black-Box Language Models: Learning to Optimize Linguistically-Structured Word Subsets
arXiv:2606. 08497v1 Announce Type: new Abstract: As deep language models (DLMs) are increasingly deployed in high-stakes domains such as healthcare, understanding their decision rationale becomes paramount for ensuring trust, safety, and accountability.