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Found 7 papers, 4 papers with code
AAAR-1.0: Assessing AI's Potential to Assist Research
Numerous studies have assessed the proficiency of AI systems, particularly large language models (LLMs), in facilitating everyday tasks such as email writing, question answering, and creative content generation.
LLMs' Classification Performance is Overclaimed
We define this task as Classify-w/o-Gold and propose it as a new testbed for LLMs.
X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification
In recent years, few-shot and zero-shot learning, which learn to predict labels with limited annotated instances, have garnered significant attention.
MUFFIN: Curating Multi-Faceted Instructions for Improving Instruction-Following
In the realm of large language models (LLMs), enhancing instruction-following capability often involves curating expansive training data.
Robustness of Learning from Task Instructions
To our knowledge, this is the first work that systematically studies how robust a PLM is when it is supervised by instructions with different factors of variability.
OpenStance: Real-world Zero-shot Stance Detection
To our knowledge, this is the first work that studies stance detection under the open-domain zero-shot setting.
Bone marrow sparing for cervical cancer radiotherapy on multimodality medical images
Experiments on patient dataset reveal that our proposed method can enhance the multimodal image registration accuracy and efficiency for medical practitioners in sparing BM of cervical cancer radiotherapy.
