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Found 9 papers, 4 papers with code
Testing the Limits of Unified Sequence to Sequence LLM Pretraining on Diverse Table Data Tasks
Tables stored in databases and tables which are present in web pages and articles account for a large part of semi-structured data that is available on the internet.
HYTREL: Hypergraph-enhanced Tabular Data Representation Learning
Language models pretrained on large collections of tabular data have demonstrated their effectiveness in several downstream tasks.
Large Language Models of Code Fail at Completing Code with Potential Bugs
We find that the presence of potential bugs significantly degrades the generation performance of the high-performing Code-LLMs.
Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion
To better solve this task, we query a program analyzer for information relevant to a given function call, and consider ways to provide the analyzer results to different code completion models during inference and training.
Parameter and Data Efficient Continual Pre-training for Robustness to Dialectal Variance in Arabic
While prior research studies have tried to adapt these multilingual models for dialectal variants of Arabic, it still remains a challenging problem owing to the lack of sufficient monolingual dialectal data and parallel translation data of such dialectal variants.
Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning
Recent work has found that multi-task training with a large number of diverse tasks can uniformly improve downstream performance on unseen target tasks.
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating).
NSML: A Machine Learning Platform That Enables You to Focus on Your Models
However, researchers are still required to perform a non-trivial amount of manual tasks such as GPU allocation, training status tracking, and comparison of models with different hyperparameter settings.
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
To address these problems, we propose both a new model and a benchmark for precipitation nowcasting.
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