---
title: "ZoFia — paper by Wu et al. (2026) — summary and ideas"
description: "ZoFia targets zero-shot fake news detection, where LLMs struggle for two reasons. 69.71 accuracy. 55 references."
authors: ["Lvhua Wu", "Xuefeng Jiang", "Sheng Sun", "Yan Lei", "Tian Wen", "Yuwei Wang", "Min Liu"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1083/"
doi: "10.18653/v1/2026.findings-acl.1083"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wu-2026-zofia-zero-shot-fake-news/"
---
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# ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction

**Authors:** Lvhua Wu, Xuefeng Jiang, [Sheng Sun](https://md.research.elamur.ai/free/en/authors/sheng-sun/), Yan Lei, Tian Wen, [Yuwei Wang](https://md.research.elamur.ai/free/en/authors/yuwei-wang/) and [Min Liu](https://md.research.elamur.ai/free/en/authors/min-liu/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1083/) · [PDF](https://aclanthology.org/2026.findings-acl.1083.pdf) · [DOI 10.18653/v1/2026.findings-acl.1083](https://doi.org/10.18653/v1/2026.findings-acl.1083) · [OpenAlex](https://openalex.org/W4416435464)

**Citations:** 3 citations (OpenAlex, 2026-10-14)

## Summary
ZoFia targets zero-shot fake news detection, where LLMs struggle for two reasons. Their knowledge cutoff leads to hallucinated facts about recent events. A single model also tends to commit to a verdict early and then justify it, so it handles content analysis and fact checking poorly at the same time.

The framework has two stages. In the first, a BERT-NER model proposes candidate entities. These are ranked with a new Hierarchical Salience score, which combines entity–context and context–document SBERT similarities. A Salience-Calibrated MMR (SC-MMR) step then picks a compact keyword set, which forms a conjunctive query for Wikipedia and Open Web retrieval. In the second stage, several LLM agents work in parallel: a linguistic analyst, a domain expert, and a claim extractor/verifier. Opposing debaters then argue "real" and "fake," and a judge issues the verdict.

On two public datasets, ZoFia beats zero-shot baselines and most few-shot methods. This matters because it shows that separating reasoning from evidence checking makes training-free detection more reliable and explainable.

## Key points
- Agent roles are Linguist, Expert, Claim Extractor/Verifier, Debater-R, Debater-F and Judge, tested with backbones including Qwen3-32B and DeepSeek-v3. Code is released at github.com/SakiRinn/ZoFia.
- On GossipCop with Qwen3-32B, ZoFia reaches 69.71 accuracy and 59.07 F1. Without retrieval, these fall to 62.65 and 50.85.
- Swapping the entity extraction for TF-IDF, PromptNER or LLM-only NER lowers F1 by 5.69, 3.94 and 6.33 points. Sentence-level queries (Sentence Salience, Core Claim) or OR-based matching lower it by 12.92, 10.26 and 6.81 points.
- Performance holds steady up to about 6 keywords and drops sharply beyond that. This motivated an annealed SC-MMR weight with α = 0.3 and β = 2.5.
- Under output-token limits, CoT and FactAgent level off around 600 tokens. ZoFia needs at least 400 tokens and keeps improving up to 1200.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) — Government body, Beijing, China · 194 works · 23,906 citations
- [University of Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/universities/university-of-chinese-academy-of-sciences/) — University, Beijing, China · 147 works · 8,480 citations
- [Institute of Computing Technology](https://md.research.elamur.ai/free/en/labs/institute-of-computing-technology/) — Research institute, Beijing, China · 51 works · 3,411 citations

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## Cite

Cite as: Lvhua Wu, Xuefeng Jiang, Sheng Sun, Yan Lei, Tian Wen, Yuwei Wang and Min Liu. 2026. ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wu2026zofia,
  title = {{ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction}},
  author = {Lvhua Wu, Xuefeng Jiang, Sheng Sun, Yan Lei, Tian Wen, Yuwei Wang and Min Liu},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1083},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1083}
}
```

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*Updated 1 November 2026*