arXiv AI
Jul 31
Making Implicit Premises Explicit in Logical Understanding of Enthymemes
arXiv:2603. 06114v2 Announce Type: replace-cross Abstract: Real-world arguments in text and dialogues are normally enthymemes (i.
Identification of Entailment and Contradiction Relations for Natural Language Argumentation: A Neurosymbolic Approach
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arXiv AI
Aug 20
Identifying Implicit Premises for Logical Reconstruction of Argument Graphs
The paper tackles the challenge of reconstructing argument graphs from natural language by addressing enthymemes—arguments with implicit premises. It proposes a neuro‑symbolic pipeline that employs large language models to generate intermediate implicit premises, translates them into logical formulas, and combines them with explicit premises and claims to determine entailment, contradiction, or neutrality. The method is evaluated on the Microtext Argumentative Corpus.
arXiv Computation and Language
Sep 23
Semantic Abstraction for Natural Language Inference: a Methodological Framework for Discovering and Compensating Semantic Knowledge and Reasoning Gaps in Large Language Models
arXiv:2609.26610v1 Announce Type: new Abstract: Despite their outstanding performance on many NLP tasks, LLMs face serious challenges related to semantic abstraction. In this study, we are interested...
arXiv Computation and Language
Oct 15
Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLI
arXiv:2610.15158v2 Announce Type: replace Abstract: We study neuro-symbolic natural language inference (NLI) with independently translated propositional representations of the premise and hypothesis....
arXiv AI
Jun 9
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.
arXiv AI
Jul 15
A Neurosymbolic Approach to Natural Language Formalization and Verification
arXiv:2511. 09008v2 Announce Type: replace-cross Abstract: Large Language Models perform well at natural language interpretation and reasoning, but their lack of formal correctness guarantees limits their adoption in regulated industries like finance and health-care that operate under strict policies.
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 Computation and Language
Sep 1
REER-PT: Reverse-Engineered Reasoning for Perplexity-Guided Pre-training Data Augmentation
arXiv:2608.30627v1 Announce Type: new Abstract: As language-model compute continues to scale, high-quality training data is becoming an increasingly important bottleneck. Conventional next-token pred...
arXiv AI
Aug 18
Logical Embeddings for Argument Analysis
arXiv:2608. 15325v1 Announce Type: cross Abstract: We propose a new framework for machine-learning-oriented argument analysis tasks.
arXiv AI
Jun 3
Fixing FOLIO and MALLS: Verified Annotations and an LLM-assisted Framework to Focus Human Relabeling
arXiv:2606. 02837v1 Announce Type: cross Abstract: Accurate translation from Natural Language to First-Order Logic (NL-to-FOL) underpins neurosymbolic AI systems and Natural Language Inference (NLI), making the quality of NL-to-FOL benchmarks essential -- yet these datasets have never been rigorously audited.
arXiv Computation and Language
Oct 16
Explanation Generation for Contradiction Reconciliation with LLMs
The paper introduces a new task called reconciliatory explanation generation, where large language models (LLMs) must produce explanations that make contradictory statements compatible. It proposes reusing natural language inference datasets and new quality metrics for scalable evaluation. Experiments with 13 LLMs show limited success, especially when longer reasoning chains are involved, but reinforcement‑learning training improves performance on two narrative reasoning benchmarks.
arXiv AI
Jun 16
VeriGraph: Towards Verifiable Data-Analytic Agents
arXiv:2606. 16603v1 Announce Type: cross Abstract: LLM-based agents have demonstrated strong capabilities in data-intensive analytical tasks, yet their outputs are rarely verifiable: a reliance on linear text trajectories makes their reasoning difficult to audit.