---
title: "VeriScore — paper by Song et al. (2024)"
description: "The authors propose VeriScore, which pulls out only verifiable claims. 45 references, cited by 57 papers here."
authors: ["Yixiao Song", "Yekyung Kim", "Mohit Iyyer"]
year: 2024
venue: "Findings of the Association for Computational Linguistics: EMNLP 2024"
source: "https://aclanthology.org/2024.findings-emnlp.552/"
doi: "10.18653/v1/2024.findings-emnlp.552"
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/song-2024-veriscore-evaluating-factuality-verifiable-claims/"
---
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# VeriScore: Evaluating the factuality of verifiable claims in long-form text generation

**Authors:** [Yixiao Song](https://md.research.elamur.ai/free/en/authors/yixiao-song/), Yekyung Kim and [Mohit Iyyer](https://md.research.elamur.ai/free/en/authors/mohit-iyyer/) · **Year:** 2024 · **Venue:** [Findings of the Association for Computational Linguistics: EMNLP 2024](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2024](https://md.research.elamur.ai/free/en/venues/findings/2024/)) · **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/2024.findings-emnlp.552/) · [PDF](https://aclanthology.org/2024.findings-emnlp.552.pdf) · [DOI 10.18653/v1/2024.findings-emnlp.552](https://doi.org/10.18653/v1/2024.findings-emnlp.552) · [OpenAlex](https://openalex.org/W4404780948)

**Citations:** 12 citations (OpenAlex, 2026-10-14) · 57 in our library, of the 141,840 papers whose bibliographies we read

## Summary
Metrics for long-form factuality such as FactScore and SAFE split a text into atomic claims and check every one. They assume all content can be checked. In tasks like long-form QA (LFQA), responses mix facts with analogies, advice, opinions and hypotheticals, so these metrics unfairly penalise models.

The authors propose VeriScore, which pulls out only verifiable claims. A verifiable claim describes one event or state, together with the modifiers it needs. Extraction uses a sliding window over nearby sentences, so references like pronouns are resolved without a separate revision step. Each claim is used as a Google Search query, and an LLM judges whether the results support it. Scores combine precision and recall through F1@K.

The pipeline was first built with few-shot prompting of closed models. Open-weight models were then fine-tuned on GPT-4/GPT-4o outputs to give a cheaper version. Human annotators strongly preferred VeriScore's claims. Benchmarking 16 LMs across eight domains shows that factuality rankings shift between tasks. This argues for evaluating factuality beyond biography-style text.

## Key points
- In a human study, VeriScore's claim extraction was preferred over SAFE's in 93% of cases, including on biography generation.
- Evidence comes from Google Search through the Serper API, using up to 10 results per claim. Contradicted and inconclusive verdicts are merged into a single "unsupported" label, so verification becomes a binary task.
- The score is F1@K, where K is set per domain as the median number of extracted claims across model responses.
- Of the 16 evaluated models, GPT-4o scored best on average. Open-weight models such as Mixtral-8×22 are narrowing the gap, and a model's score on biographies does not predict its score on LFQA.
- Reported limitations:
  - Extraction is slow: about 4 hours for 400 GPT-4o responses on one RTX8000.
  - Complex, non-entity-centric claims are hard to verify from search snippets.
  - "Verifiable claim" is still only a working definition.

*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

- [University of Massachusetts Amherst](https://md.research.elamur.ai/free/en/universities/university-of-massachusetts-amherst/) — University, Amherst Center, United States · 67 works · 64,591 citations

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## Cite

Cite as: Yixiao Song, Yekyung Kim and Mohit Iyyer. 2024. VeriScore: Evaluating the factuality of verifiable claims in long-form text generation. Findings of the Association for Computational Linguistics: EMNLP 2024.

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

```bibtex
@inproceedings{song2024veriscore,
  title = {{VeriScore: Evaluating the factuality of verifiable claims in long-form text generation}},
  author = {Yixiao Song, Yekyung Kim and Mohit Iyyer},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2024},
  year = {2024},
  doi = {10.18653/v1/2024.findings-emnlp.552},
  url = {https://doi.org/10.18653/v1/2024.findings-emnlp.552}
}
```

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*Updated 24 October 2026*