ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification

Hiep Nguyen; Le-Minh Nguyen; Son T. Luu; Trung Vo

REVIEW 4 major objections 6 minor 16 references

ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read ZeFaV claims that adding claim-anchored relation triples and reorganized evidence to an LLM prompt improves zero-shot fact verification enough to beat earlier LLM methods on HoVer and FEVEROUS-S.

desk verdict ZeFaV's own ablation shows its novel relation-extraction module hurts more than it helps, so the central claim that ZeFaV boosts zero-shot fact verification does not survive its data. read the letter →

arxiv 2411.11247 v1 pith:U32KIYXN submitted 2024-11-18 cs.CL cs.AI

Son T. Luu , Hiep Nguyen , Trung Vo , Le-Minh Nguyen This is my paper · ORCID

classification cs.CLcs.AI

keywords factverificationzero-shotlearninglargelanguagemodelsrelationextractionin-contextmulti-hopreasoningevidencereorganizationchain-of-thoughtprompting

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

This paper tries to establish that zero-shot fact verification with large language models can be improved by changing what goes into the prompt rather than by adding training data or switching to a larger model. The proposed pipeline, ZeFaV, extracts entity-relation triples from the claim and evidence, keeps only evidence relations connected to the claim's entities through a closure procedure, rewrites the evidence as a hierarchical structure, and then asks the model to decide True or False with all of this context. On HoVer and FEVEROUS-S, the paper reports F1 scores that beat several prior LLM-based fact-checking methods, including a few-shot program-guided baseline, while using an open-weights 70B model in a zero-shot setting. If correct, this would mean a cheap, reusable recipe: few-shot relation extraction plus evidence reorganization can give a general-purpose LLM the structured context it needs for multi-hop verification.

What carries the argument

The mechanism is a three-stage prompt construction pipeline. Stage one uses a relation-extraction prompt fine-tuned on FewRel to turn a sentence into (head, relation, tail) triples. Stage two applies a closure filter: seed a hypothesis set with the entities appearing in the claim's triples, then repeatedly add every evidence triple whose head entity is already in the set and add its tail to the set until no further addition is possible; this is meant to keep exactly the evidence relations connected to the claim and discard unrelated ones. Stage three uses InfoRE, a prompt that reorganizes evidence into a hierarchical structure with indentation marking subordination, and then the final verification prompt concatenates the original evidence, the organized evidence, the claim, and both relation lists with a chain-of-thought instruction.

What would settle it

One decisive check would be to annotate, for a random sample of claims, whether the closure-filtered evidence relations retain the evidence sentences that determine the verdict; if a large fraction of verdict-relevant evidence is filtered out, then the relation stage cannot be doing the grounding work claimed, and the reported gains would stem from InfoRE and context alone. A simpler probe is to reproduce Table 2 on 3-hop HoVer with a higher-quality relation extractor: the current numbers show adding relations to InfoRE lowers F1 from 72.03 to 70.61, so a corrected extractor should flip that drop if relations truly help.

Watch

Extended reading notes

Core claim

ZeFaV's claim is that a zero-shot LLM verifier can be made substantially better by restructuring the evidence and grounding it with relations before asking for a verdict. The pipeline extracts (head, relation, tail) triples from the claim and evidence, filters the evidence triples to those reachable from claim entities, rewrites the evidence into a hierarchical form, and then feeds the original evidence, rewritten evidence, and relation lists to the LLM with a chain-of-thought True/False instruction. On HoVer (2-/3-/4-hop) and FEVEROUS-S, the paper reports F1 scores of 77.85, 70.61, 67.47, and 86.74, respectively, comparing favorably with ProgramFC, QACheck, and InfoRE. The paper also reports an ablation in which removing the original evidence context or InfoRE hurts more than removing the relation component, and in which the relation component alone is the weakest single addition.

Load-bearing premise

The load-bearing assumption is that the relation extractor fine-tuned on FewRel produces triples that are complete and accurate enough on HoVer and FEVEROUS-S that the closure filter keeps the evidence needed to decide the claim and the relation lists guide the verifier rather than mislead it, which the paper does not directly test and which the ablation suggests may fail on some subsets.

Editorial extensions

If this is right

Zero-shot verification can reach the level of a few-shot program-decomposition system when the prompt includes relation triples, hierarchically reorganized evidence, and the original evidence.
The original evidence context and the InfoRE rewrite carry most of the accuracy gain; the relation component is a smaller add-on that can reduce F1 on some subsets (3-hop HoVer and FEVEROUS-S) when InfoRE is already present.
The pipeline can be applied to new claims without in-domain training examples, so the same FewRel-tuned extractor and prompts transfer to any evidence text.
Structured and numerical evidence remains the main limitation, with the lowest F1 on FEVEROUS-S numerical reasoning.

Reading between the lines

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

The FewRel-trained extractor is implicitly treated as a portable relation vocabulary for fact verification, so the same extractor could be dropped into other multi-hop QA tasks without per-task relation annotation.
A testable extension is to convert tables and numbers into triples before the closure step, since the weakest FEVEROUS-S category is numerical reasoning; if that conversion lifts the 72.83% F1, the bottleneck is representation rather than the verifier.
The closure filter is a graph-reachability selection rule, which suggests the active ingredient may be entity-anchored subgraph selection rather than relation semantics; replacing InfoRE with any evidence selector that keeps passages sharing claim entities would isolate whether the relational form itself matters.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

ZeFaV is a prompt-engineering paper. The pipeline is: fine-tune a relation extractor on FewRel, extract claim and evidence relations, run a closure filter to drop evidence relations whose head entity is not in the claim's hypothesis set, reorganize the evidence with InfoRE, and feed all of that to Llama-3-70B for zero-shot fact verification. The specific combination is new, the authors release code, and they evaluate on HoVer and FEVEROUS-S with an ablation and a challenge-type breakdown. That is real work, and the full ablation table is honestly reported.

The problem is what the ablation says. Table 2 compares ZeFaV full (relation + InfoRE) with InfoRE-only. With evidence context, removing the relation module improves F1 on HoVer 3-hop (72.03 vs 70.61), HoVer 4-hop (67.64 vs 67.47), and FEVEROUS-S (86.89 vs 86.74). Without evidence context, InfoRE-only beats the full pipeline on all four settings. So the one genuinely new component — the FewRel relation extraction plus closure — does not help; it mostly hurts. Section 4.2 says "both InfoRE and Relation help increase the performance," which is not a reading of its own table. The observed advantage over InfoRE in Table 1 is better explained by the base model (Llama-3 vs Llama-2) and by InfoRE itself.

Other soft spots are smaller but real. The zero-shot label is imprecise because the relation extractor is fine-tuned on FewRel. The comparison against InfoRE and QACheck uses different base models, so the headline numbers are confounded; the only same-model baseline, ProgramFC, beats ZeFaV on HoVer 3-hop (70.84 vs 70.61), which undercuts the claim of being better on HoVer. There are no error bars or significance tests, and the code link has no commit hash or fine-tuning details. The paper also never measures relation extraction precision/recall on these datasets, so it cannot tell whether the closure filter is dropping useful evidence.

None of this makes the paper worthless. The method is plausible as a recipe, and the result "InfoRE helps, relation filtering doesn't" is a useful negative result if the authors are willing to report it that way. But the central claim — that ZeFaV boosts zero-shot fact verification — is not supported by the evidence in the paper. A serious referee should see it, mostly to force the authors to either drop the relation-module claims or rerun controlled same-base-model experiments with significance. As submitted, it should be rejected; with an honest reframing it could be a modest empirical note.

Referee Report

4 major / 6 minor

Summary. The paper proposes ZeFaV, a pipeline for zero-shot fact verification with large language models. The method first fine-tunes an LLM on FewRel for relation extraction, then reorganizes the evidence with the InfoRE technique, and finally combines the claim, the original evidence, the reorganized evidence, and extracted claim/evidence relations in a prompt that asks the LLM to return True or False. Experiments on HoVer and FEVEROUS-S with Meta-Llama-3-70B-Instruct report F1 scores that are claimed to outperform prior methods. However, the paper's own ablation in Table 2 shows that the relation-extraction component often lowers F1 relative to using InfoRE alone, and the main comparisons in Table 1 are confounded by the use of different base models across methods.

Significance. The idea of using extracted relations as structured hints for zero-shot fact verification is plausible, and the paper makes its code available and evaluates on public datasets, which are positive features. If the claimed gains were robust, ZeFaV would be a useful addition to the fact-checking toolkit. However, the central contribution—the FewRel-fine-tuned relation-extraction and closure-filtering component—is not supported by the paper's own ablation, and the headline comparison against InfoRE and QACheck does not isolate the proposed mechanism from the choice of the base LLM. The empirical support is therefore currently insufficient to establish the claimed improvement.

major comments (4)

[4.1, Table 1] The main comparison against InfoRE and QACheck is not controlled: ZeFaV uses Meta-Llama-3-70B-Instruct, while InfoRE uses Llama-2-70B and QACheck uses InstructGPT (Section 4.1 and Table 1). Any observed improvement could be due to the stronger base model rather than to ZeFaV's relation-extraction and InfoRE components. The text also claims that 'ZeFaV obtained better results than other methodologies on the HoVer dataset,' but Table 1 shows ProgramFC scoring 70.84 on HoVer 3-hop, higher than ZeFaV's 70.61. In addition, the FEVEROUS-S F1 is reported as 86.54 in the text but 86.74 in Table 1. The comparison should be rerun with a common base model and the numerical inconsistencies corrected.
[4.2, Table 2] The ablation directly contradicts the conclusion that 'both InfoRE and Relation help increase the performance.' Replacing the full ZeFaV (Relation+InfoRE) with InfoRE-only raises F1 on HoVer 3-hop from 70.61 to 72.03, on HoVer 4-hop from 67.47 to 67.64, and on FEVEROUS-S with evidence context from 86.74 to 86.89; the same replacement raises F1 in all four settings without evidence context (e.g., FEVEROUS-S from 75.97 to 76.11). No error bars, confidence intervals, or significance tests are reported, so even the direction of the paired comparison is not statistically established. The paper's central claim that the relation-extraction module boosts fact verification is therefore unsupported by its own ablation.
[Algorithm 1 and Section 3] The closure filter in Algorithm 1 is a plausible source of the observed degradation: it retains an evidence relation only if its head entity appears in the claim-hypothesis set, and the paper provides no measurement of relation-extraction precision or recall on HoVer or FEVEROUS claims. If the FewRel-fine-tuned model produces incomplete or noisy triples, the filter can silently discard evidence relations needed for verification, which would explain the F1 drops in Table 2. The authors should report relation-extraction quality on the target datasets and provide at least one qualitative example of the filter's behavior on the cases where adding the relation component lowers accuracy.
[3 and 4.1] The method is described as 'zero-shot,' but the relation-extraction stage is fine-tuned on the FewRel dataset and the InfoRE stage uses few-shot prompting examples. If 'zero-shot' is intended only for inference on the target fact-verification datasets, this should be stated explicitly; otherwise the title's zero-shot claim is misleading and the comparison with few-shot methods such as ProgramFC needs to account for the fine-tuning budget used by ZeFaV's relation-extraction component.

minor comments (6)

[Abstract] The phrase 'potential results results' contains a duplicated word and should be corrected, likely to 'promising results.'
[Table 1 caption] The caption contains the typo 'Emprical results' and the dataset name is rendered as 'HoV er'; both should be fixed.
[Section 3] The prompt template for relation extraction does not specify how the full FewRel relation list is inserted, whether all 80 relations are used, or how the LLM is fine-tuned (training split, number of examples, epochs, learning rate, LoRA settings). These details are needed for reproducibility.
[Section 4.2] The prose acknowledges that performance 'slightly decreases when combined with the relation' on HoVer 3-hop and FEVEROUS-S, yet the following sentence claims both components help; this internal inconsistency should be resolved by reporting the paired differences with error bars and a clear interpretation.
[Table 3] The challenge type 'Combining Tables and Tex' contains a typo and should be 'Combining Tables and Text.'
[Figure 2] The confusion matrix in Figure 2 is referenced but not described in sufficient detail; the claim that the model predicts refuted claims as supported claims should be accompanied by the actual counts or percentages from the confusion matrix.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: ZeFaV's relation extraction and InfoRE components are evaluated on held-out datasets against external baselines; the internal ablation contradiction is a validity concern, not a circularity.

full rationale

The derivation chain contains no step in which a target claim is defined in terms of its own output, no fitted parameter is renamed as a prediction, and no load-bearing self-citation is used. The relation-extraction component is fine-tuned on the external FewRel dataset and applied to held-out HoVer/FEVEROUS-S claims; the reported F1 scores are computed from the verifier's predictions on those held-out claims, not from the relation extractor's training labels. InfoRE [5] and ProgramFC [14] are external prior works with different author sets, and their use as components and baselines does not make ZeFaV's result forced by prior assumptions. The ablation in Table 2 does undermine the textual claim that 'both InfoRE and Relation help increase the performance' (e.g., relation+InfoRE is below InfoRE-only on HoVer 3-hop, 4-hop, and FEVEROUS-S), and the main comparison is confounded by using a different base LLM than the InfoRE baseline. However, those are correctness and experimental-design concerns, not instances of definitional, fitted-input, or self-citation circularity under the requested taxonomy. The paper is self-contained against external datasets and baselines, so the circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper borrows FewRel for fine-tuning, InfoRE [5] as a component, and HoVer/FEVEROUS-S as evaluation. It does not fit a numeric parameter to the target benchmark; the only learned component is the relation extractor trained on FewRel. The reliance on prior work is external, not circular.

assumptions (4)

domain assumption Large language models can perform reliable zero-shot reasoning over claims and evidence when given structured prompts.
The entire method is built on the premise that Llama-3-70B-Instruct will follow the ZeFaV prompt and produce accurate True/False verdicts (Section 3, final prompt).
domain assumption FewRel relation categories are sufficient to express the relations present in HoVer and FEVEROUS claims and evidence.
The relation extraction prompt asks the model to classify into 'one of the appropriate categories' drawn from FewRel; if the target relations are out of the FewRel label set, the extracted triples will be wrong (Section 3, step 1).
domain assumption InfoRE's re-organization preserves the factual content needed for verification.
ZeFaV relies on InfoRE [5] to restructure evidence into hierarchical form without losing the information used to make the verdict (Section 3, step 3).
domain assumption The evaluation splits and labels in HoVer and FEVEROUS-S are correct and comparable.
All F1 numbers are computed on the splits from Pan et al. [14]; the paper does not validate labels (Section 4.1).

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Cite this review

Pith. "Pith review of ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification." pith.science (2026). https://pith.science/paper/U32KIYXN

@misc{pith2026241111247,
  author       = {Pith},
  title        = {Pith review of: ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/U32KIYXN}},
  note         = {Machine review of arXiv:2411.11247}
}

read the original abstract

In this paper, we propose ZeFaV - a zero-shot based fact-checking verification framework to enhance the performance on fact verification task of large language models by leveraging the in-context learning ability of large language models to extract the relations among the entities within a claim, re-organized the information from the evidence in a relationally logical form, and combine the above information with the original evidence to generate the context from which our fact-checking model provide verdicts for the input claims. We conducted empirical experiments to evaluate our approach on two multi-hop fact-checking datasets including HoVer and FEVEROUS, and achieved potential results results comparable to other state-of-the-art fact verification task methods.

Figures

Figures reproduced from arXiv: 2411.11247 by the authors.

**Figure 1.** Overview of the ZeFaV framework 4 Empirical Results 4.1 Main experimental results We perform ZeFaV on Meta-Llama-3-70B-Instruct1 and evaluate the performance on the division of HoVer [9] and FEVEROUS-S [2] datasets by Pan et al. [14] through the F1-score metric. We run the LLMs with LoRA technique [8] quantized to 4 bits, and the maximum length for the generation model is 2,048. Our ZeFaV can run on one NVIDIA A40 G… view at source ↗

**Figure 2.** Confusion matrix of ZeFaV on HoVer [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

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Reference graph

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Pith tools

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