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cs.CL2024
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao +1
This paper presents a systematic overview of parameter-efficient fine-tuning methods, covering over 50 papers published between early 2019 and mid-2024. These methods aim to addres…
cs.CL2024
Deconstructing In-Context Learning: Understanding Prompts via Corruption
Namrata Shivagunde, Vladislav Lialin, Sherin Muckatira +1
The ability of large language models (LLMs) to learn in context based on the provided prompt has led to an explosive growth in their use, culminating in the proliferation of…
cs.CL2024
Emergent Abilities in Reduced-Scale Generative Language Models
Sherin Muckatira, Vijeta Deshpande, Vladislav Lialin +1
Large language models can solve new tasks without task-specific fine-tuning. This ability, also known as in-context learning (ICL), is considered an emergent ability and is primari…