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Found 5 papers, 2 papers with code
Two-stage LLM Fine-tuning with Less Specialization and More Generalization
Pretrained large language models (LLMs) are general purpose problem solvers applicable to a diverse set of tasks with prompts.
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces
A popular approach for dealing with the large label space is to arrange the labels into a shallow tree-based index and then learn an ML model to efficiently search this index via beam search.
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
We also provide a theoretical analysis that justifies the use of XMC over link prediction and motivates integrating XR-Transformers, a powerful method for solving XMC problems, into the GIANT framework.
Ranked #2 on
Node Property Prediction
on ogbn-papers100M
Theoretical Analysis of Consistency Regularization with Limited Augmented Data
Data augmentation is popular in the training of large neural networks; currently, however, there is no clear theoretical comparison between different algorithmic choices on how to use augmented data.
Data-aware Low-Rank Compression for Large NLP Models
In this paper, we observe that the learned representation of each layer lies in a low-dimensional space.
