Verified claim · AI-ML · 100% confidence
InstructGPT methodology introduced in paper: Training language models to follow instructions with human feedback (Ouyang et al., 2022).
Last verified 2026-05-16 · Methodology veritas-v0.1 · 5da8f8dffc038b8e
SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.
Structured fields
- Subject
- InstructGPT methodology
- Predicate
introduced_in_paper- Object
- Training language models to follow instructions with human feedback (Ouyang et al., 2022)
- Confidence
- 100%
- Tags
- instructgpt · alignment · openai · 2022 · ouyang · rlhf
Sources (2)
[1] preprint · arXiv (Ouyang et al., OpenAI) · 2022-03-04
Training language models to follow instructions with human feedback“We show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. … The resulting InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets.”
[2] official blog · OpenAI · 2022-01-27
Aligning language models to follow instructionsOpenAI is rated by SourceScore — see its reliability →
Cite this claim
Ready-to-paste citation (Markdown / plain text):
InstructGPT methodology introduced in paper: Training language models to follow instructions with human feedback (Ouyang et al., 2022). — SourceScore Claim 5da8f8dffc038b8e (verified 2026-05-16). https://sourcescore.org/api/v1/claims/5da8f8dffc038b8e.jsonEmbed this claim
Drop this iframe into any blog post, docs page, or knowledge base. The widget renders the signed claim + primary source + click-through to this canonical page. CC-BY 4.0; attribution included.
<iframe src="https://sourcescore.org/embed/claim/5da8f8dffc038b8e/" width="100%" height="360" frameborder="0" loading="lazy" title="InstructGPT methodology introduced in paper: Training language models to follow instructions with human feedback (Ouyang et al., 2022)."></iframe>Preview: open in new tab
Related claims
Other verified claims sharing tags with this one — useful for LLM retrieval graphs and citation discovery.
InstructGPT introduced in: Ouyang et al. 2022 — RLHF-tuned GPT-3, direct ancestor of ChatGPT.
590b9de765b8126e · 100% confidence · shares 5 tags (instructgpt, openai, rlhf…)
Reinforcement Learning from Human Feedback (RLHF) introduced in paper: Deep Reinforcement Learning from Human Preferences (Christiano et al., 2017).
67866330cd60e54d · 100% confidence · shares 2 tags (rlhf, alignment)
Constitutional AI (CAI) introduced in paper: Constitutional AI: Harmlessness from AI Feedback (Bai et al., 2022).
ba1eb83c14795107 · 100% confidence · shares 2 tags (alignment, 2022)
ChatGPT released on: 2022-11-30.
8d653880c519a8ef · 100% confidence · shares 2 tags (openai, 2022)
Whisper released on: 2022-09-21.
a3ebbaed14bd83d0 · 100% confidence · shares 2 tags (openai, 2022)
Frequently asked questions
Is the claim "InstructGPT methodology introduced in paper: Training language models to follow instructions with human feedback (Ouyang et al., 2022)." verified?
Yes — SourceScore verified this claim with 100% confidence as of 2026-05-16. The verification uses 2 primary sources cross-referenced against the SourceScore methodology (version veritas-v0.1). Full source list + signed JSON envelope linked below.
What is the evidence for "InstructGPT methodology introduced in paper: Training language models to follow instructions with human feedback (Ouyang et al., 2022)."?
Evidence comes from 2 primary sources: arXiv (Ouyang et al., OpenAI), OpenAI. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/5da8f8dffc038b8e.json includes an HMAC-SHA256 signature for audit verification.
When was this claim last verified by SourceScore?
Last verified 2026-05-16 under methodology version veritas-v0.1. The signed JSON envelope is dated and cryptographically signed for audit trail. Re-verification cadence depends on the claim type and source freshness.
How can I cite this SourceScore claim in my code or article?
Fetch the signed JSON envelope from https://sourcescore.org/api/v1/claims/5da8f8dffc038b8e.json which includes the verbatim claim, primary sources, confidence, methodology version, last-verified date, and HMAC-SHA256 signature for audit. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
Use this claim in your code
Fetch this signed envelope from your application. The response includes the verbatim excerpt, primary source URLs, and an HMAC-SHA256 signature you can verify locally for audit trails.
cURL
curl https://sourcescore.org/api/v1/claims/5da8f8dffc038b8e.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/5da8f8dffc038b8e.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "InstructGPT methodology introduced in paper: Training language models to follow instructions with human feedback (Ouyang et al., 2022)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/5da8f8dffc038b8e.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "InstructGPT methodology introduced in paper: Training language models to follow instructions with human feedback (Ouyang et al., 2022)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_instructgpt_methodology_fact() -> dict:
"""Fetch the verified SourceScore claim for InstructGPT methodology."""
r = httpx.get("https://sourcescore.org/api/v1/claims/5da8f8dffc038b8e.json")
return r.json()