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Found 8 papers, 4 papers with code
Prompting a Pretrained Transformer Can Be a Universal Approximator
Despite the widespread adoption of prompting, prompt tuning and prefix-tuning of transformer models, our theoretical understanding of these fine-tuning methods remains limited.
When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations
Context-based fine-tuning methods, including prompting, in-context learning, soft prompting (also known as prompt tuning), and prefix-tuning, have gained popularity due to their ability to often match the performance of full fine-tuning with a fraction of the parameters.
Language Models as a Service: Overview of a New Paradigm and its Challenges
This paper has two goals: on the one hand, we delineate how the aforementioned challenges act as impediments to the accessibility, replicability, reliability, and trustworthiness of LMaaS.
Language Model Tokenizers Introduce Unfairness Between Languages
Recent language models have shown impressive multilingual performance, even when not explicitly trained for it.
Certifying Ensembles: A General Certification Theory with S-Lipschitzness
Improving and guaranteeing the robustness of deep learning models has been a topic of intense research.
Robustness of Unsupervised Representation Learning without Labels
When used in adversarial training, they improve most unsupervised robustness measures, including certified robustness.
Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents
As robotics matures and increases in complexity, it is more necessary than ever that robot autonomy research be reproducible.
Learning Camera Miscalibration Detection
Self-diagnosis and self-repair are some of the key challenges in deploying robotic platforms for long-term real-world applications.
