pith:Z4XHBC6M
Bridging the Semantic Gap for Categorical Data Clustering via Large Language Models
LLM-generated semantic descriptions of categorical values, when fused into embeddings, measurably improve clustering quality over standard methods.
arxiv:2601.01162 v3 · 2026-01-03 · cs.LG · cs.AI · cs.CL
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\usepackage{pith}
\pithnumber{Z4XHBC6MV64DVJTY3ARM5MU5UA}
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Record completeness
Claims
Experiments on eight benchmark datasets demonstrate consistent improvements over seven representative counterparts, with gains of 19-27%.
That LLM-generated descriptions of attribute values provide reliable, unbiased semantic knowledge that meaningfully complements the original categorical metric space without introducing hallucinations or domain mismatches.
ARISE integrates LLM-generated semantic embeddings with categorical data representations to bridge the similarity gap, achieving 19-27% gains over seven baselines on eight benchmark datasets.
Receipt and verification
| First computed | 2026-05-29T01:05:01.000829Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
cf2e708bccafb83aa678d822ceb29da009baef00a24ca785070d7d0e0e3ce8ab
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/Z4XHBC6MV64DVJTY3ARM5MU5UA \
| 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: cf2e708bccafb83aa678d822ceb29da009baef00a24ca785070d7d0e0e3ce8ab
Canonical record JSON
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"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
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"submitted_at": "2026-01-03T11:37:46Z",
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