Pith Number

pith:54ZFYSZE

pith:2026:54ZFYSZEEWRNYZ5IGQVM5IILMO

not attested not anchored not stored refs resolved

Fact4ac at the Financial Misinformation Detection Challenge Task: Reference-Free Financial Misinformation Detection via Fine-Tuning and Few-Shot Prompting of Large Language Models

Cuong Hoang, Le-Minh Nguyen

Fine-tuned LLMs detect financial misinformation at 95-96 percent accuracy using only internal context and no external references.

arxiv:2604.14640 v1 · 2026-04-16 · cs.CL · cs.AI

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\usepackage{pith}
\pithnumber{54ZFYSZEEWRNYZ5IGQVM5IILMO}

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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

Our proposed system demonstrated superior efficacy, successfully securing the first-place ranking on both official leaderboards. Specifically, we achieved an accuracy of 95.4% on the public test set and 96.3% on the private test set.

C2weakest assumption

That the internal semantic understanding and contextual consistency of the fine-tuned LLMs are sufficient to determine the veracity of financial claims without any external evidence or references.

C3one line summary

LLM system with LoRA fine-tuning and few-shot prompting wins reference-free financial misinformation detection task at 95.4% public and 96.3% private accuracy.

References

5 extracted · 5 resolved · 2 Pith anchors

[1] Language Models are Few-Shot Learners 2005 · arXiv:2005.14165

[2] Meta-learning via language model in-context tuning.ArXiv, abs/2110.07814

[3] InProceedings of the 33rd ACM Interna- tional Conference on Multimedia, MM ’25, 13874–13880 2025

[4] All that glisters is not gold: A bench- mark for reference-free counterfactual financial misinformation detection

[5] Qwen2.5 Technical Report · arXiv:2412.15115

Receipt and verification

First computed	2026-05-27T01:05:54.641947Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

ef325c4b2425a2dc67a8342acea10b63a1fb54fdbd64614993f88545516ac3d6

Aliases

arxiv: 2604.14640 · arxiv_version: 2604.14640v1 · doi: 10.48550/arxiv.2604.14640 · pith_short_12: 54ZFYSZEEWRN · pith_short_16: 54ZFYSZEEWRNYZ5I · pith_short_8: 54ZFYSZE

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/54ZFYSZEEWRNYZ5IGQVM5IILMO \
  | 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: ef325c4b2425a2dc67a8342acea10b63a1fb54fdbd64614993f88545516ac3d6

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "f96fdb2fc0f5303e53f48ed0b4def29bcf40805eb3cd7f32132499c34d8fac62",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2026-04-16T05:38:58Z",
    "title_canon_sha256": "e8ff4f0348b0152cec126fecedb1367307b57138bad682f02f0e7c65842e84f2"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.14640",
    "kind": "arxiv",
    "version": 1
  }
}