Instruction-Tuning-Papers
Reading list of Instruction-tuning papers.
GraphCanon updated Oct 11, 2026 · GitHub synced Oct 11, 2026
34views this month
Decision brief
Instruction-Tuning-Papers is a curated reading list focused on the instruction-tuning domain for language models.
Good fit when
- When you're looking to enhance your understanding of how natural language instructions can empower language models in diverse tasks.
- If your focus is on studying advancements like Natural Instruction, FLAN, and T0 from notable conferences such as ACL and ICLR.
Avoid when
- Avoid this resource if you are looking for tools or frameworks to implement instruction tuning rather than theoretical understanding.
- Not suitable for users in need of a broader overview beyond specific academic papers on language model training methodologies.
Observed Jul 11, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Dormant (1179d since push)
- As of Oct 11, 2026
- Provenance
- Not a fork · Personal account
- As of Oct 11, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/SinclairCoder/Instruction-Tuning-PapersSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
This repository contains a curated set of academic papers focused on the concept of instruction tuning for language models, aiming to teach these models to follow instructions effectively and improve their capabilities in both training tasks and unseen, generalized tasks. Papers span research from key conferences such as ACL and ICLR and cover notable works like Natrural-Instruction, FLAN, and T0.
Capability facts
No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).
Categories
Tags
README
Instruction Tuning Papers A trend starts from (ACL 2022), (ICLR 2022) and (ICLR 2022). What's the instruction tuning? It aims to teach language models to follow natural language (including prompt, positive or negative examples, and constraints etc.), to perform better multi task...
For agents
This page has a .md twin and JSON over the API.