-
Scaling Laws for Forgetting When Fine-Tuning Large Language Models
Paper • 2401.05605 • Published -
Scaling Laws for Downstream Task Performance of Large Language Models
Paper • 2402.04177 • Published • 20 -
A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Software Engineering Tasks
Paper • 2312.15614 • Published
Johanna Mannisto
mannisjo
AI & ML interests
None yet
Organizations
None yet
Synthetic Data
-
DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data
Paper • 2405.14333 • Published • 48 -
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
Paper • 2405.15613 • Published • 17 -
DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
Paper • 2402.10379 • Published • 31 -
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Paper • 2402.13064 • Published • 52
to-read
-
Scaling Laws for Forgetting When Fine-Tuning Large Language Models
Paper • 2401.05605 • Published -
Scaling Laws for Downstream Task Performance of Large Language Models
Paper • 2402.04177 • Published • 20 -
A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Software Engineering Tasks
Paper • 2312.15614 • Published
Synthetic Data
-
DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data
Paper • 2405.14333 • Published • 48 -
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
Paper • 2405.15613 • Published • 17 -
DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
Paper • 2402.10379 • Published • 31 -
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Paper • 2402.13064 • Published • 52
models 0
None public yet
datasets 0
None public yet