Few-shot in-context learning (ICL) enables pre-trained language models to perform a
previously-unseen task without any gradient-based training by feeding a small number of …

Large-scale language models, like ChatGPT, have garnered significant media attention and
stunned the public with their remarkable capacity for generating coherent text from short …

Soft prompt tuning achieves superior performances across a wide range of few-shot tasks.
However, the performances of prompt tuning can be highly sensitive to the initialization of …

The high annotation costs and diverse demands of various summarization tasks motivate the
development of few-shot summarization. However, despite the emergence of many …

Large language models (LLMs) have been shown to perform well in answering questions
and in producing long-form texts, both in few-shot closed-book settings. While the former can …

Generative transformer-based models have achieved state-of-the-art performance in text
summarization. Nevertheless, they still struggle in real-world scenarios with long documents …

Sequence-to-sequence deep neural models fine-tuned for abstractive summarization can
achieve great performance on datasets with enough human annotations. Yet, it has been …

Training language models to learn from human instructions for zero-shot cross-task
generalization has attracted much attention in NLP communities. Recently, instruction tuning …

Unsupervised summarization is a powerful technique that enables training summarizing
models without requiring labeled datasets. This survey covers different recent techniques …

Recent advances in prompt tuning have proven effective as a new language modeling
paradigm for various natural language understanding tasks. However, it is challenging to …