Building on these "base" ICL algorithms, intriguingly, we show that transformers

Deep learning models are emerging to summarize source code to comment for code documentation and program comprehension. We can achieve good performance by training the model on large training corpus.

VQA to overcome this limitation.

The scarcity of data presents a critical obstacle to the efficacy of medical vision-language pre-training (VLP).

Large language models (LLMs) have been recently leveraged as training data generators for various natural language processing (NLP) tasks. While previous research has explored different approaches to training models using generated data, they generally rely on simple class-conditional prompts, which may limit the diversity of the generated data and inherit systematic biases of LLM. Thus, we investigate training data generation with diversely attributed prompts (e.g.,

The contrastive vision-language pre-training, known as CLIP, demonstrates remarkable potential in perceiving open-world visual concepts, enabling effective zero-shot image recognition.

Moreover, the model tends to generalize well to unseen tasks that did not appear in the training data.