LLM Research Insights: Instruction Masking and New LoRA Finetuning Experiments?
Read OriginalThis article examines three recent research papers on instruction finetuning and parameter-efficient finetuning with LoRA in large language models. It focuses particularly on a study questioning the common practice of instruction masking during loss calculation, comparing performance differences between masked and unmasked approaches. The author provides practical context from working with these methods in LitGPT and discusses implications for LLM development.
Comments
No comments yet
Be the first to share your thoughts!
Browser Extension
Get instant access to AllDevBlogs from your browser
Top of the Week
1
Introducing GPT-5.1 for developers
Simon Willison
•
6 votes
2
A simple explanation of the big idea behind public key cryptography
Richard Gendal Brown
•
1 votes
3
Google Antigravity Exfiltrates Data
Simon Willison
•
1 votes
4
5
Fix “This video format is not supported” on YouTube TV
David Walsh
•
1 votes
6
Tooltip Components Should Not Exist
TkDodo Dominik Dorfmeister
•
1 votes
7
llm-anthropic 0.22
Simon Willison
•
1 votes
8
GPT-5.1 Instant and GPT-5.1 Thinking System Card Addendum
Simon Willison
•
1 votes
9
Nano Banana can be prompt engineered for extremely nuanced AI image generation
Simon Willison
•
1 votes
10
Hire Me in Japan
Dan Abramov
•
1 votes