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REVIEW 3 major objections 5 minor 33 references

OpenFActScore: Open-Source Atomic Evaluation of Factuality in Text Generation

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read OpenFActScore is an open-source reimplementation of FActScore that claims fully open language models can reproduce the original metric's model ranking, with a Pearson correlation over 0.99 across 10 language models.

desk verdict Useful open-source FActScore with a plausible but over-sold 0.99 correlation; worth reviewing with statistical fixes. read the letter →

arxiv 2507.05965 v1 pith:XXLPEMOZ submitted 2025-07-08 cs.CL cs.AI

Lucas Fonseca Lage , Simon Ostermann This is my paper
classification cs.CLcs.AI
keywords factualityevaluationatomicfactgenerationvalidationopen-sourcelanguagemodelsScorehallucinationdetectionlong-formtextmodel-based
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

OpenFActScore is an open-source reimplementation of the FActScore pipeline for measuring whether long-form text generated by language models is factual. The paper claims that fully open models can stand in for the proprietary InstructGPT and ChatGPT components of the original, and that the resulting estimates are not only close but rank models identically: across 10 language models, OpenFActScore's scores correlate with the original FActScore at $r > 0.99$. The authors select Olmo for atomic fact generation and Gemma for atomic fact validation, documenting design changes such as chat templates and system prompts. A sympathetic reader would care because factuality evaluation is otherwise tied to closed, paid APIs, which limits reproducibility and cost-effective auditing.

What carries the argument

The load-bearing mechanism is the two-stage FActScore pipeline itself, made model-agnostic. Atomic Fact Generation (AFG) splits each sentence into atomic facts, short statements each carrying one piece of information, using a prompt with a BM25-selected demo; Atomic Fact Validation (AFV) retrieves the top five Wikipedia passages with a GTR retriever, prepends them to a 'True or False?' prompt, and parses the validator's first token. OpenFActScore's engineering contribution is an HFModel class that loads any openly available causal language model with its chat template and system prompt, letting the user swap AFG and AFV models freely. The chosen pair, Olmo for AFG and Gemma for AFV, is what produces the $r > 0.99$ correlation with the original scores.

What would settle it

Have human annotators score the same 10 model outputs used in Table 3 and compare their ranking with OpenFActScore's ranking; if the human-model rank correlation is much lower than 0.99, then the high correlation shows agreement with the closed-source estimator rather than with human factuality. Alternatively, compute OpenFActScore on a fresh set of model outputs from a different domain and check whether the model ranking still matches human judgment.

Watch

Extended reading notes

Core claim

The central discovery is that a fully open model pair can approximate the original closed-source FActScore estimator. Using Olmo to decompose model outputs into atomic facts and Gemma to verify each fact against retrieved Wikipedia passages yields FActScore estimates that correlate with the original scores from both FActScore settings at Pearson $r > 0.99$ across 10 language models, preserving the model ranking even though the absolute OpenFActScore values are lower. This is presented as an extension of FActScore rather than a new metric, with the open pipeline intended to make factuality evaluation reproducible and independent of commercial APIs.

Load-bearing premise

The load-bearing assumption is that the original FActScore benchmark data, specifically the human-annotated atomic facts and the 10 model outputs used in Table 3, is an accurate and representative testbed for factuality evaluation; if these outputs are easy to rank or the human labels are noisy, a 0.99 correlation may only show agreement with the original automated pipeline, not with genuine human factuality judgments.

Editorial extensions

If this is right

  • Factuality scores for a new language model can be computed with no closed-source API calls, as long as a Wikipedia dump and an open validator are available.
  • Model rankings from the original FActScore benchmark are preserved by the open pipeline, so comparative conclusions from that benchmark carry over to OpenFActScore.
  • Gemma and Llama 3.1 are more reliable validators than Olmo and Qwen, indicating that strong atomic fact generation does not automatically imply strong atomic fact validation.
  • The published system prompts and chat-template handling make the exact scoring procedure reproducible by other researchers.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the correlation generalizes beyond the biography benchmark, OpenFActScore could serve as a free, reproducible audit tool for hallucination rates in both open and commercial models.
  • The lower absolute scores suggest OpenFActScore is calibrated differently from the original; users who care about absolute factuality thresholds rather than rankings should recalibrate before comparing scores across papers.
  • A natural test is to run the same two-stage pipeline on other domains with factual ground truth, such as news summarization or non-English biography writing, to see whether the $r > 0.99$ transfers.
  • Because the paper chooses Olmo over Gemma for AFG based on a roughly 0.35 percent score difference plus openness, swapping in a slightly better open generator could change the correlation and should be checked before relying on the specific model pair.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper introduces OpenFActScore, an open-source reimplementation of the FActScore factuality evaluation framework. It replaces the original closed-source components (InstructGPT for atomic fact generation, ChatGPT/InstLlama for validation) with Hugging Face-compatible models, and describes the implementation, prompts, and design choices. The authors evaluate four open models: Llama-3.1, Gemma, Qwen, and Olmo, using BERTScore-F1 against human-corrected atomic facts for AFG and error rates against human FActScores for AFV. Based on these results, they select Olmo for AFG and Gemma for AFV, apply this pipeline to 10 model outputs from the original FActScore repository, and report that OpenFActScore scores correlate with the original FActScore settings at Pearson r > 0.99 with the same model ranking.

Significance. If the statistical claims are properly supported, this is a useful and reproducible engineering contribution: it shows that a fully open pipeline can approximate the ordinal behavior of a closed-source factuality metric on a standard benchmark. The released codebase, the use of the original human annotations, and the explicit choice of a fully open model (Olmo) are genuine strengths. The scientific novelty is modest, however, since the paper is primarily a reimplementation and evaluation study rather than a new metric. The main limitation is that the headline 0.99 correlation rests on only 10 aggregate model-level points with no uncertainty quantification, so the strength of the conclusion is currently overstated.

major comments (3)
  1. [Section 4.3, Table 3] The claim that 'the Pearson correlation between FActScore and OpenFActScore in both setting (A) and (B) is over 0.99' is computed from exactly 10 model-level scores with no confidence interval, p-value, or per-entity breakdown. The table does show that the 10 aggregate scores are identically ordered across the three columns, so the ranking claim is supported for this specific sample. However, a high Pearson r is expected when the 10 points span a very wide range (FS(A) from 17.3 to 73.1), and this does not establish that the ranking is reliable for models that are close in factuality (e.g., Alpaca 13B vs Vicuna 7B differ by only about 1 point in FS(OFS)). Please report Fisher-z or bootstrap confidence intervals, Spearman/Kendall rank correlations, and per-entity agreement, and release the per-entity scores so the 0.99 can be independently verified.
  2. [Section 4.2, Table 2] The 'Cumulative ER' column is the sum of the absolute error rates across the three subject models (e.g., Llama-3.1: 2.8 + 1.6 + 9.0 = 13.4), but it is labeled and discussed as a cumulative error rate. Because signed errors cancel, this metric can conceal large but opposing errors and is not an appropriate loss for model selection. Please report signed error rates and mean absolute error separately, and confirm that the choice of Gemma for AFV and the overall conclusion remain justified under the corrected metric.
  3. [Section 4.3 and Data Availability] The paper does not provide the OpenFActScore atomic facts, validation labels, or per-entity scores used to compute the FS(OFS) column in Table 3, so the central quantitative claim cannot be recomputed from the manuscript alone. Given that the stated contribution is reproducibility, please release these artifacts alongside the code, including exact model versions, decoding parameters, and the retrieval setting (e.g., Wikipedia dump and GTR model version) used for the final evaluation.
minor comments (5)
  1. [Conclusion and Table 3] The conclusion states that 'we evaluated productions from 11 different language models,' but Table 3 lists 10 models; please clarify whether the count includes the three subject models in Tables 1-2 or whether a row is missing from Table 3.
  2. [Abstract] The abstract contains a typo ('setupt' for 'setup') and should be corrected.
  3. [Throughout] There are several spelling errors, including 'oringial', 'implementatoin', 'differense', 'Althought', and 'architechture'; please proofread the manuscript.
  4. [Section 4.3] There is an inconsistent capitalization 'OpenFactScore' in the surrounding text, which should be 'OpenFActScore'.
  5. [Section 4.1] The AFG evaluation uses BERTScore-F1 with a best-match assignment per sentence, but the paper does not report standard deviations across entities or across the three subject models; adding such variation would help interpret the 0.35% difference that motivates choosing Olmo over Gemma.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the 0.99 correlation is measured, not derived; the open pipeline's ranking of 8 models unseen during component selection gives the claim independent content.

full rationale

OpenFActScore is an engineering reimplementation of the FActScore pipeline using open models, and its derivation chain contains no fitted parameters, no equations that reduce to their own inputs, and no load-bearing self-citations: the paper relies on the external FActScore benchmark of Min et al. (2023a), not on prior work by the present authors. The central claim, Pearson r > 0.99 between FS(OFS) (Olmo for AFG, Gemma for AFV) and the original FS(A)/FS(B) scores on 10 models (Section 4.3), is an empirical comparison with independent content: eight of the ten model outputs (GPT-4, Alpaca 65B, Alpaca 13B, Vicuna 7B, MPT Chat 7B, Pythia 12B, Dolly 12B, StableLM 7B) played no role in selecting the components, and the comparison target (the original closed-model pipeline) is distinct from the human-annotation agreement criterion used to pick Olmo and Gemma in Sections 4.1-4.2, so nothing forces the resulting ranking or correlation. The only mild in-sample aspect is that Olmo and Gemma were chosen for best agreement with the same human-annotated data that anchors the benchmark, and two of the ten evaluated models (ChatGPT, InstructGPT) overlap with that annotation set; this can slightly inflate apparent agreement, but it is a methodological limitation rather than a circular reduction. Remaining issues — the conclusion says '11 different language models' while Table 3 lists 10, and no confidence interval is given for r at n=10 — are reporting and rigor concerns, not circularity.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

No free parameters are fitted; the only design choices are model selection (Olmo for AFG, Gemma for AFV) based on the same benchmark data, which is a mild selection-bias concern rather than a fitted parameter. The paper relies on existing benchmark data and standard similarity metrics, and invents no new entities.

assumptions (5)
  • domain assumption Human-annotated atomic facts from Min et al. (2023a) are an accurate gold standard for Atomic Fact Generation quality.
    Used as ground truth in Section 4.1 to compute BERTScore-F1; if these annotations are noisy, AFG rankings may not reflect true generation quality.
  • domain assumption BERTScore-F1 with best-match selection is a valid proxy for the human-judged semantic equivalence of atomic facts.
    The paper uses BERTScore instead of human judges for AFG evaluation (Section 4.1); this is a modeling choice that may not capture all semantic nuances.
  • domain assumption The 10 model outputs in the original FActScore repository are representative of LLM outputs for the biography-writing task.
    Table 3 computes the headline 0.99 correlation on these outputs; if they are not diverse, the correlation may overstate general agreement.
  • domain assumption FActScore's three assumptions (atomic facts are undebatable, equally weighted, and non-overlapping in the knowledge source) hold for the biography task.
    These assumptions are inherited from Min et al. (2023a), Section 2.1, and are not revalidated in this paper.
  • standard math A Pearson correlation computed over 10 paired values is a meaningful measure of agreement between two evaluation methods.
    The paper reports a Pearson correlation of 0.99 on 10 models without confidence intervals; this is statistically thin but not mathematically invalid.

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Cite this review

Pith. "Pith review of OpenFActScore: Open-Source Atomic Evaluation of Factuality in Text Generation." pith.science (2026). https://pith.science/paper/XXLPEMOZ

@misc{pith2026250705965,
  author       = {Pith},
  title        = {Pith review of: OpenFActScore: Open-Source Atomic Evaluation of Factuality in Text Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XXLPEMOZ}},
  note         = {Machine review of arXiv:2507.05965}
}
read the original abstract

We introduce OpenFActScore, an open-source implementation of the FActScore framework for evaluating the factuality of text generated by large language models (LLMs). FActScore evaluates the factual accuracy of long-form text by using Atomic Fact Generation (AFG) to extract individual factual claims and Atomic Fact Validation (AFV) to verify each claim against a trusted knowledge source. While the original FActScore relies on closed-source and commercial models such as InstructGPT and ChatGPT, OpenFActScore enables the use of any Hugging Face-compatible model for both AFG and AFV. We provide a detailed technical overview of our implementation, highlighting design choices and modifications made to support open models. We evaluate multiple open-source LLMs on both AFG and AFV using the original FActScore benchmark, reporting BERTScore-F1 for AFG and Error Rate relative to human annotations for AFV. Our results show that open models can approximate the performance of closed-source systems, with Gemma achieving the best overall performance, and our final setup obtains a 0.99 Pearson correlation with the original FActScore experiments. OpenFActScore promotes transparency, reproducibility, and cost-effective evaluation, and is available at: https://github.com/lflage/OpenFActScore.

Figures

Figures reproduced from arXiv: 2507.05965 by the authors.

Figure 1
Figure 1. Sample from human annotated data. 2. Atomic Fact Validation (AFV): Verify for each claim whether it is supported by a refer￾ence knowledge source. After obtaining the Atomic Facts and validating them against a knowledge source, the FActScore for a model is calculated as the number of Atomic Facts supported by the knowledge source divided by the total number of generated Atomic Facts.1 2.2 Human-Annotated Data In the… view at source ↗

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