Pith Number
pith:YRHVAUTX
pith:2024:YRHVAUTXZ3I5BDOP63GBTS5DD5
not attested
not anchored
not stored
refs pending
Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary Empirical Study
arxiv:2406.11629 v6 · 2024-06-17 · cs.CL
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{YRHVAUTXZ3I5BDOP63GBTS5DD5}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
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4
Citations
5
Replications
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Portable graph bundle live · download bundle · merged
state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same
current state with the deterministic merge algorithm.
Cited by
Receipt and verification
| First computed | 2026-07-05T10:09:44.823555Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
c44f505277ced1d08dcff6cc19cba31f562aefc9e0f47e87c0e502b03d16e6c1
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/YRHVAUTXZ3I5BDOP63GBTS5DD5 \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: c44f505277ced1d08dcff6cc19cba31f562aefc9e0f47e87c0e502b03d16e6c1
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "917dffb0a5115d5e62e7ce40e73bfebfc76de37677c2b5a723c0e245cdbec166",
"cross_cats_sorted": [],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.CL",
"submitted_at": "2024-06-17T15:11:58Z",
"title_canon_sha256": "84d960fef51f102d7b02278277cecaeea1e54b211c75c6438d5c66ae03c41d1f"
},
"schema_version": "1.0",
"source": {
"id": "2406.11629",
"kind": "arxiv",
"version": 6
}
}