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Found 11 papers, 4 papers with code
To be or not to be an Integer? Encoding Variables for Mathematical Text
The application of Natural Language Inference (NLI) methods over large textual corpora can facilitate scientific discovery, reducing the gap between current research and the available large-scale scientific knowledge.
Estimating the Causal Effects of Natural Logic Features in Transformer-Based NLI Models
Rigorous evaluation of the causal effects of semantic features on language model predictions can be hard to achieve for natural language reasoning problems.
Estimating the Causal Effects of Natural Logic Features in Neural NLI Models
Rigorous evaluation of the causal effects of semantic features on language model predictions can be hard to achieve for natural language reasoning problems.
Interventional Probing in High Dimensions: An NLI Case Study
Probing strategies have been shown to detect the presence of various linguistic features in large language models; in particular, semantic features intermediate to the "natural logic" fragment of the Natural Language Inference task (NLI).
Natural Language Inference
Vocal Bursts Intensity Prediction
Montague semantics and modifier consistency measurement in neural language models
At the same time, the need for interpretability has elicited questions on their intrinsic properties and capabilities.
Systematicity, Compositionality and Transitivity of Deep NLP Models: a Metamorphic Testing Perspective
Metamorphic testing has recently been used to check the safety of neural NLP models.
Decomposing Natural Logic Inferences in Neural NLI
In the interest of interpreting neural NLI models and their reasoning strategies, we carry out a systematic probing study which investigates whether these models capture the crucial semantic features central to natural logic: monotonicity and concept inclusion.
Grounding Natural Language Instructions: Can Large Language Models Capture Spatial Information?
Even though BERT and similar pre-trained language models have excelled in several NLP tasks, their use has not been widely explored for the UI grounding domain.
Supporting Context Monotonicity Abstractions in Neural NLI Models
Natural language contexts display logical regularities with respect to substitutions of related concepts: these are captured in a functional order-theoretic property called monotonicity.
Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference
This paper presents Diff-Explainer, the first hybrid framework for explainable multi-hop inference that integrates explicit constraints with neural architectures through differentiable convex optimization.
Does My Representation Capture X? Probe-Ably
Probing (or diagnostic classification) has become a popular strategy for investigating whether a given set of intermediate features is present in the representations of neural models.
