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Found 7 papers, 0 papers with code
Exploring Label Hierarchy in a Generative Way for Hierarchical Text Classification
Hierarchical Text Classification (HTC), which aims to predict text labels organized in hierarchical space, is a significant task lacking in investigation in natural language processing.
Locating and Mitigating Gender Bias in Large Language Models
Prior research has typically tackled the issue of bias through a one-dimensional perspective, concentrating either on locating or mitigating it.
Editing Knowledge Representation of Language Model via Rephrased Prefix Prompts
The results indicate that PSPEM can serve as an alternative to original prompts, supporting the model in effective editing.
Hierarchy-Aware T5 with Path-Adaptive Mask Mechanism for Hierarchical Text Classification
Hierarchical Text Classification (HTC), which aims to predict text labels organized in hierarchical space, is a significant task lacking in investigation in natural language processing.
Denoising User-aware Memory Network for Recommendation
Specifically, the framework: (i) proposes a feature purification module based on orthogonal mapping, which use the representation of explicit feedback to purify the representation of implicit feedback, and effectively denoise the implicit feedback; (ii) designs a user memory network to model the long-term interests in a fine-grained way by improving the memory network, which is ignored by the existing methods; and (iii) develops a preference-aware interactive representation component to fuse the long-term and short-term interests of users based on gating to understand the evolution of unbiased preferences of users.
LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding
In this work, we propose a knowledge distillation method LRC-BERT based on contrastive learning to fit the output of the intermediate layer from the angular distance aspect, which is not considered by the existing distillation methods.
Privacy Preserving PCA for Multiparty Modeling
In this paper, we present a general multiparty modeling paradigm with Privacy Preserving Principal Component Analysis (PPPCA) for horizontally partitioned data.
