On the Importance of Data Size in Probing Fine-tuned Models

arXiv:2203.09627 · cs.CL, cs.LG · Submitted 2022-03-17 · Read on arXiv

cs.CL, cs.LG

Submitted: 2022-03-17

Updated: 2022-03-17

Comments: To appear in ACL Findings 2022

DOI: 10.18653/v1/2022.findings-acl.20

Code: https://github.com/hmehrafarin/data-size-analysis

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fine-Tuning — 60× in this paper · explained in 5 episodes
• Accuracy — 12× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Downstream Tasks — 4× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Natural Language Inference (NLI) — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Related papers

• Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
• Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
• Subliminal Steering: Stronger Encoding of Hidden Signals
• MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
• The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
• Untangling the Mechanisms of Misleading Context in Medical Question Answering