mlsanityData to 14 Oct 2026

Boosting Neural Language Inference via Cascaded Interactive Reasoning

arXiv 2505.06607 ↗First posted 10 May 2025cs.CLcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Natural Language Inference (NLI) focuses on ascertaining the logical relationship (entailment, contradiction, or neutral) between a given premise and hypothesis. This task presents significant challenges due to inherent linguistic features such as diverse phrasing, semantic complexity, and contextual nuances. While Pre-trained Language Models (PLMs) built upon the Transformer architecture have yielded substantial advancements in NLI, prevailing methods predominantly utilize representations from the terminal layer. This reliance on final-layer outputs may overlook valuable information encoded in intermediate layers, potentially limiting the capacity to model intricate semantic interactions effectively. Addressing this gap, we introduce the Cascaded Interactive Reasoning Network (CIRN), a novel architecture designed for deeper semantic comprehension in NLI. CIRN implements a hierarchical feature extraction strategy across multiple network depths, operating within an interactive space where cross-sentence information is continuously integrated. This mechanism aims to mimic a process of progressive reasoning, transitioning from surface-level feature matching to uncovering more profound logical and semantic connections between the premise and hypothesis. By systematically mining latent semantic relationships at various representational levels, CIRN facilitates a more thorough understanding of the input pair. Comprehensive evaluations conducted on several standard NLI benchmark datasets reveal consistent performance gains achieved by CIRN over competitive baseline approaches, demonstrating the efficacy of leveraging multi-level interactive features for complex relational reasoning.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 28 of its 42 references that are in the map. First posted on arXiv 10 May 2025.

Builds on

28 of 42 references.

References · 28

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16 more
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  3. 2026Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLI
  4. 2026Semantic Abstraction for Natural Language Inference: a Methodological Framework for Discovering and Compensating Semantic Knowledge and Reasoning Gaps in Large Language Models
  5. 2024Reliable Reasoning Beyond Natural Language
  6. 2025Let's CONFER: A Dataset for Evaluating Natural Language Inference Models on CONditional InFERence and Presupposition
  7. 2022The KITMUS Test: Evaluating Knowledge Integration from Multiple Sources in Natural Language Understanding Systems
  8. 2025Cross-Lingual Consistency: A Novel Inference Framework for Advancing Reasoning in Large Language Models