Search Results for author:
Found 8 papers, 2 papers with code
Graph-based Aspect Representation Learning for Entity Resolution
Entity Resolution (ER) identifies records that refer to the same real-world entity.
Is Next Token Prediction Sufficient for GPT? Exploration on Code Logic Comprehension
Our experimental results reveal that following this pretraining, both Code Llama and StarCoder, the prevalent code domain pretraining models, display significant improvements on our logically equivalent code selection task and the code completion task.
Rethinking the Instruction Quality: LIFT is What You Need
Instruction tuning, a specialized technique to enhance large language model (LLM) performance via instruction datasets, relies heavily on the quality of employed data.
SUT: Active Defects Probing for Transcompiler Models
In this paper we introduce a new metrics for programming language translation and these metrics address these basic syntax errors.
Program Translation via Code Distillation
Distilled code serves as a translation pivot for any programming language, leading by construction to parallel corpora which scale to all available source code by simply applying the distillation compiler.
A flexible PageRank-based graph embedding framework closely related to spectral eigenvector embeddings
We study a simple embedding technique based on a matrix of personalized PageRank vectors seeded on a random set of nodes.
Continual Learning for Text Classification with Information Disentanglement Based Regularization
Continual learning has become increasingly important as it enables NLP models to constantly learn and gain knowledge over time.
Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees
We present Stochastic-Sign SGD which utilizes novel stochastic-sign based gradient compressors enabling the aforementioned properties in a unified framework.
