Verified claim · AI-ML · 100% confidence
AlpacaEval introduced in: Li et al. 2023 — LLM-as-judge evaluation benchmark.
Last verified 2026-05-16 · Methodology veritas-v0.1 · 2f14f3078741c0ad
SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.
Related verified claims
More verified claims related to this one — keep exploring.
Chatbot Arena introduced in: Zheng et al. 2023 — LMSYS open platform for evaluating LLMs by human preference.
100% confidence · shares 3 tags (evaluation, 2023, introduced_in)
Stanford Alpaca publicly released on: 2023-03-13 — instruction-tuned LLaMA 7B from Stanford CRFM.
100% confidence · shares 3 tags (alpaca, stanford, 2023)
SWE-bench introduced in: Jimenez et al. 2024 — software engineering benchmark from GitHub issues.
100% confidence · shares 3 tags (evaluation, introduced_in, 2023)
Direct Preference Optimization (DPO) introduced in paper: Direct Preference Optimization: Your Language Model is Secretly a Reward Model (Rafailov et al., 2023).
100% confidence · shares 2 tags (2023, stanford)
Toolformer introduced in: Schick et al. 2023 — self-supervised LLM tool-use.
100% confidence · shares 2 tags (2023, introduced_in)
vLLM introduced in: Kwon et al. 2023 — high-throughput LLM serving via PagedAttention.
100% confidence · shares 2 tags (2023, introduced_in)
Self-RAG introduced in: Asai et al. 2023 — self-reflective retrieval-augmented generation.
100% confidence · shares 2 tags (2023, introduced_in)
LangSmith publicly released on: 2023-07-18 by LangChain — LLM observability + evaluation platform.
100% confidence · shares 2 tags (evaluation, 2023)
Structured fields
- Subject
- AlpacaEval
- Predicate
introduced_in- Object
- Li et al. 2023 — LLM-as-judge evaluation benchmark
- Confidence
- 100%
- Tags
- alpacaeval · alpaca · stanford · evaluation · llm-as-judge · 2023 · introduced_in
Sources (2)
[1] github release · Tatsu Lab / Stanford · 2023-05-25
AlpacaEval — automatic evaluator for instruction-following models“An Automatic Evaluator for Instruction-following Language Models. AlpacaEval, an LLM-based automatic evaluator that is based on the AlpacaFarm evaluation set, which tests the ability of models to follow general user instructions.”
[2] official blog · Tatsu Lab / Stanford · 2023-05-25
AlpacaEval Leaderboard
Cite this claim
Ready-to-paste citation (Markdown / plain text):
AlpacaEval introduced in: Li et al. 2023 — LLM-as-judge evaluation benchmark. — SourceScore Claim 2f14f3078741c0ad (verified 2026-05-16). https://sourcescore.org/api/v1/claims/2f14f3078741c0ad.jsonEmbed this claim
Drop this iframe into any blog post, docs page, or knowledge base. The widget renders the claim record + top cited source + click-through to this canonical page. CC-BY 4.0; attribution included.
<iframe src="https://sourcescore.org/embed/claim/2f14f3078741c0ad/" width="100%" height="360" frameborder="0" loading="lazy" title="AlpacaEval introduced in: Li et al. 2023 — LLM-as-judge evaluation benchmark."></iframe>Preview: open in new tab
Frequently asked questions
How has SourceScore reviewed the claim "AlpacaEval introduced in: Li et al. 2023 — LLM-as-judge evaluation benchmark."?
SourceScore records this assertion with 100% legacy editorial confidence as of 2026-05-16, under methodology veritas-v0.1. That metadata is not a truth guarantee. Inspect the 2 cited source record(s), excerpts, and live evidence below.
What is the evidence for "AlpacaEval introduced in: Li et al. 2023 — LLM-as-judge evaluation benchmark."?
The record lists 2 cited source(s): Tatsu Lab / Stanford, Tatsu Lab / Stanford. Each is shown below with a short excerpt and URL. The JSON record at https://sourcescore.org/api/v1/claims/2f14f3078741c0ad.json includes SourceScore-issued HMAC integrity metadata, not a public verification proof.
When was this claim record last reviewed by SourceScore?
Last reviewed 2026-05-16 under methodology version veritas-v0.1. The dated JSON record includes SourceScore-issued HMAC metadata, which is not publicly recomputable. Refetch the record and inspect current evidence before relying on it.
How can I cite this SourceScore claim in my code or article?
Fetch the JSON record from https://sourcescore.org/api/v1/claims/2f14f3078741c0ad.json, including the verbatim claim, cited evidence, confidence, methodology version, and last-verified date. Refetch the canonical HTTPS record and inspect cited evidence; the HMAC tag is not publicly independently verifiable. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
Use this claim in your code
Fetch this record from your application. The response includes verbatim excerpts, primary-source URLs, and SourceScore-issued HMAC integrity metadata. Refetch the canonical HTTPS record and inspect cited evidence; public users cannot independently verify the HMAC tag.
cURL
curl https://sourcescore.org/api/v1/claims/2f14f3078741c0ad.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/2f14f3078741c0ad.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "AlpacaEval introduced in: Li et al. 2023 — LLM-as-judge evaluation benchmark."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/2f14f3078741c0ad.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "AlpacaEval introduced in: Li et al. 2023 — LLM-as-judge evaluation benchmark."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_alpacaeval_fact() -> dict:
"""Fetch the verified SourceScore claim for AlpacaEval."""
r = httpx.get("https://sourcescore.org/api/v1/claims/2f14f3078741c0ad.json")
return r.json()