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Found 10 papers, 8 papers with code
Best Practices and Lessons Learned on Synthetic Data for Language Models
The success of AI models relies on the availability of large, diverse, and high-quality datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and high costs.
Design2Code: How Far Are We From Automating Front-End Engineering?
In this work, we formalize this as a Design2Code task and conduct comprehensive benchmarking.
Dynamic LLM-Agent Network: An LLM-agent Collaboration Framework with Agent Team Optimization
We further design an automatic agent team optimization algorithm based on an unsupervised metric termed $\textit{Agent Importance Score}$, enabling the selection of best agents based on the contribution each agent makes.
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding
Instruction tuning unlocks the superior capability of Large Language Models (LLM) to interact with humans.
Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints
The limits of open-ended generative models are unclear, yet increasingly important.
Auditing Gender Presentation Differences in Text-to-Image Models
Text-to-image models, which can generate high-quality images based on textual input, have recently enabled various content-creation tools.
Robustness of Demonstration-based Learning Under Limited Data Scenario
Demonstration-based learning has shown great potential in stimulating pretrained language models' ability under limited data scenario.
Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition
We found that state-of-the-art NER systems trained on CoNLL 2003 training data drop performance dramatically on our challenging set.
Continual Sequence Generation with Adaptive Compositional Modules
Continual learning is essential for real-world deployment when there is a need to quickly adapt the model to new tasks without forgetting knowledge of old tasks.
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.
