Danilo Carvalho
15 papers · 2022–2026 · 6 venues · across top CS/AI venues
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🌉 Interdisciplinary Bridge · Bronze 🗺️ Taxonomy Completionist (25) 🌍 Conference Polyglot · Silver 🐣 Hot Topic Early Bird 🤝 Dynamic Duo (15)
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Century Club · Bronze
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Keywords
variational autoencoder
(5)
representation learning
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large language model
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latent space
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natural language inference
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language model
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mechanistic interpretability
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disentangled representation
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semantic disentanglement
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semantic consistency
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model analysis
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explanation generation
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logical reasoning
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linguistic knowledge
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zero-shot learning
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language modeling
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latent representation
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syntactic parsing
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symbolic reasoning
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layer-wise analysis
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Papers
Bridging Linguistic Structure and Mechanistic Interpretability for Conceptual Interpretation in Language Models
CoNLL 2026
Learning to Disentangle Latent Reasoning Rules with Language VAEs: A Systematic Study
AAAI 2026
Where Do LLMs Compose Meaning? A Layerwise Analysis of Compositional Robustness
EACL 2026
CARMA: Enhanced Compositionality in LLMs via Advanced Regularisation and Mutual Information Alignment
EMNLP 2025
Inductive Learning of Logical Theories with LLMs: A Expressivity-graded Analysis
AAAI 2025
LangVAE and LangSpace: Building and Probing for Language Model VAEs
EMNLP 2025
PEIRCE: Unifying Material and Formal Reasoning via LLM-Driven Neuro-Symbolic Refinement
ACL 2025
Quasi-symbolic Semantic Geometry over Transformer-based Variational AutoEncoder
CoNLL 2025
SylloBio-NLI: Evaluating Large Language Models on Biomedical Syllogistic Reasoning
NAACL 2025
TRACE: Training and Inference-Time Interpretability Analysis for Language Models
EMNLP 2025
Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncoders
NAACL 2024
Improving Semantic Control in Discrete Latent Spaces with Transformer Quantized Variational Autoencoders
EACL 2024
Learning Disentangled Semantic Spaces of Explanations via Invertible Neural Networks
ACL 2024
Multi-Relational Hyperbolic Word Embeddings from Natural Language Definitions
EACL 2024
Systematicity, Compositionality and Transitivity of Deep NLP Models: a Metamorphic Testing Perspective
ACL 2022