Papers by Tamás Ficsor

2 papers
SUE: Sparsity-based Uncertainty Estimation via Sparse Dictionary Learning (2025.emnlp-main)

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Challenge: Existing methods to estimate uncertainty use predictive confidence, structural characteristics of representation space, or stochastic variation in model outputs.
Approach: They propose a new uncertainty estimation framework based on sparse dictionary learning by identifying dictionary atoms associated with misclassified samples.
Outcome: The proposed framework outperforms or matches existing methods on several NLU benchmarks and sentiment analysis benchmarks.
Changing the Basis of Contextual Representations with Explicit Semantics (2021.acl-srw)

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Challenge: Existing transformer-based contextual representations are opaque as their latent dimensions are not directly interpretable.
Approach: They propose an algorithm where the output representation expresses human-interpretable information of each dimension.
Outcome: The proposed transformations are able to predict supersense category of a word by looking for its transformed coordinate with the largest coefficient.

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