Awesome Instruction Learning: 2024 Index of LLM Papers and Resources

RenzeLou curated this repository of research papers and datasets focused on instruction tuning and following for Large Language Models, last updated in April 2024. It functions as a centralized bibliography and resource index for researchers studying prompt engineering and in-context learning.

Use Cases

• Literature review using the categorized paper-list for instruction tuning
• Identifying training data by browsing the curated dataset links
• Analyzing research trends in prompt engineering via the chronological survey entries

Strengths

• MIT licensed for open redistribution
• Categorized by specific LLM sub-topics like in-context learning
• Updated as recently as April 2024

Limitations

• Contains links to external resources rather than hosting raw data files
• Lacks structured metadata like row counts or file sizes for the listed datasets
• Subject to manual curation bias

Provenance

Source

RenzeLou via GitHub

Collection Method

Manual curation

Freshness

Last updated April 2024.

Geography

Global