Pith Number

pith:FBRGX74K

pith:2026:FBRGX74KI7XTPC2EOP5MYSK7QM

not attested not anchored not stored refs pending

Do We Still Need Humans in the Loop? Comparing Human and LLM Annotation in Active Learning for Hostility Detection

Ahmad Dawar Hakimi, Hinrich Sch\"utze, Isabelle Augenstein, Lea Hirlimann

LLM-generated labels train hostility detectors to the same F1-Macro level as human labels but at far lower cost.

arxiv:2604.13899 v4 · 2026-04-15 · cs.CL · cs.AI

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\usepackage{pith}
\pithnumber{FBRGX74KI7XTPC2EOP5MYSK7QM}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ 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.

Claims

C1strongest claim

A classifier trained on 25,974 GPT-5.2 labels ($43) achieves comparable F1-Macro to one trained on 3,800 human annotations ($316).

C2weakest assumption

The assumption that the human-annotated subset serves as an unbiased gold standard and that the pre-enriched pool does not limit the potential benefits of active learning.

C3one line summary

LLM annotation can replace human labels for hostility detection with comparable F1 at much lower cost, but active learning adds little value and error structures differ systematically.

Receipt and verification

First computed	2026-06-19T16:11:23.065263Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

28626bff8a47ef378b4473facc495f832c2164cbb1bf19dced36bdebf3408918

Aliases

arxiv: 2604.13899 · arxiv_version: 2604.13899v4 · doi: 10.48550/arxiv.2604.13899 · pith_short_12: FBRGX74KI7XT · pith_short_16: FBRGX74KI7XTPC2E · pith_short_8: FBRGX74K

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/FBRGX74KI7XTPC2EOP5MYSK7QM \
  | 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: 28626bff8a47ef378b4473facc495f832c2164cbb1bf19dced36bdebf3408918

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "a942c3fe4bc6663e8cbb867771f0d42b0c38bf9047b8303f827f8149d08101ea",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2026-04-15T14:10:58Z",
    "title_canon_sha256": "887e0c248498c4c9de2aed8f5b3b5c092ebaa22477170bc92f6d85faf3ecadcd"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.13899",
    "kind": "arxiv",
    "version": 4
  }
}