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Found 6 papers, 3 papers with code
SINC: Self-Supervised In-Context Learning for Vision-Language Tasks
To this end, we raise a question: ``How can we enable in-context learning without relying on the intrinsic in-context ability of large language models?".
Shilling Black-box Review-based Recommender Systems through Fake Review Generation
Review-Based Recommender Systems (RBRS) have attracted increasing research interest due to their ability to alleviate well-known cold-start problems.
SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization
The generated summaries could therefore be constrained by the preference bias in the training set, especially under low-resource settings.
Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness
A notable challenge in Multi-Document Summarization (MDS) is the extremely-long length of the input.
Meta-Transfer Learning for Low-Resource Abstractive Summarization
Neural abstractive summarization has been studied in many pieces of literature and achieves great success with the aid of large corpora.
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