arxiv: 2604.21193 · v1 · submitted 2026-04-23 · 💻 cs.AI

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Trust but Verify: Introducing DAVinCI -- A Framework for Dual Attribution and Verification in Claim Inference for Language Models

Vipula Rawte , Ryan Rossi , Franck Dernoncourt , Nedim Lipka

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Pith reviewed 2026-05-09 22:45 UTC · model grok-4.3

classification 💻 cs.AI

keywords DAVinCIdual attributionclaim verificationlanguage modelsfactual accuracyentailment reasoningfact checkingLLM hallucinations

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The pith

DAVinCI improves LLM factual accuracy by attributing claims to both internal model parts and external sources before verifying them with entailment checks.

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

The paper presents DAVinCI as a two-stage framework that first attributes each generated claim to internal model components and external sources, then verifies it using entailment-based reasoning paired with confidence calibration. This approach targets the problem of hallucinations in large language models, which the authors note create risks in domains like healthcare, law, and scientific communication where outputs must be trustworthy. Evaluation on datasets including FEVER and CLIMATE-FEVER shows gains of 5 to 20 percent in classification accuracy, attribution precision, recall, and F1-score over verification-only baselines, with ablation studies isolating the roles of evidence selection and recalibration. The authors also release a modular implementation for easy integration into existing LLM pipelines, framing the work as a bridge between attribution and verification for more accountable systems.

Core claim

DAVinCI attributes generated claims to internal model components and external sources in the first stage, then verifies each claim in the second stage through entailment-based reasoning and confidence calibration; this dual process yields 5-20% gains in classification accuracy, attribution precision, recall, and F1-score on FEVER and CLIMATE-FEVER compared to standard verification baselines, while ablation experiments separate the effects of evidence span selection, recalibration thresholds, and retrieval quality.

What carries the argument

The DAVinCI two-stage process of dual attribution (internal plus external) followed by entailment-based verification with confidence calibration.

If this is right

LLM outputs become more auditable because each claim carries traceable internal and external attributions.
The modular implementation allows direct insertion into existing language model pipelines without full retraining.
Ablation results indicate that evidence span selection and recalibration thresholds are the main drivers of the observed gains.
The framework scales verification beyond single-source checks to handle both model-internal and retrieved evidence.
Improved metrics on fact-checking datasets suggest reduced risk of unverified claims in high-stakes applications.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same attribution-plus-verification pattern could be tested on tasks like summarization or dialogue to see whether it reduces unsupported statements there too.
If retrieval quality remains high, the approach might lower hallucination rates in live applications where external sources change over time.
Extending the calibration step to other uncertainty signals, such as token probabilities, might further lift performance without new tuning.

Load-bearing premise

That entailment reasoning plus confidence calibration can verify claims after dual attribution without adding new errors or needing domain-specific tuning beyond the tested datasets.

What would settle it

A controlled experiment on an unseen dataset where adding the dual attribution stage increases overall error rate or fails to beat a simple verification baseline.

Figures

Figures reproduced from arXiv: 2604.21193 by Franck Dernoncourt, Nedim Lipka, Ryan Rossi, Vipula Rawte.

**Figure 1.** Figure 1: Claim Input: The process begins with a textual claim, typically generated by a language model or extracted from a corpus. This claim serves as the input to the attribution module. Attribution Module: This module identifies relevant evidence passages that support or refute the claim. It operates in two modes: (a) Full-Evidence Attribution retrieves entire passages based on semantic similarity to the claim. … view at source ↗

read the original abstract

Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across a wide range of NLP tasks, yet they remain prone to factual inaccuracies and hallucinations. This limitation poses significant risks in high-stakes domains such as healthcare, law, and scientific communication, where trust and verifiability are paramount. In this paper, we introduce DAVinCI - a Dual Attribution and Verification framework designed to enhance the factual reliability and interpretability of LLM outputs. DAVinCI operates in two stages: (i) it attributes generated claims to internal model components and external sources; (ii) it verifies each claim using entailment-based reasoning and confidence calibration. We evaluate DAVinCI across multiple datasets, including FEVER and CLIMATE-FEVER, and compare its performance against standard verification-only baselines. Our results show that DAVinCI significantly improves classification accuracy, attribution precision, recall, and F1-score by 5-20%. Through an extensive ablation study, we isolate the contributions of evidence span selection, recalibration thresholds, and retrieval quality. We also release a modular DAVinCI implementation that can be integrated into existing LLM pipelines. By bridging attribution and verification, DAVinCI offers a scalable path to auditable, trustworthy AI systems. This work contributes to the growing effort to make LLMs not only powerful but also accountable.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

DAVinCI is a clean engineering synthesis of attribution and verification that delivers modest 5-20% gains on FEVER-style tasks and ships usable code.

read the letter

DAVinCI wires together dual attribution (model internals plus external retrieval) with an entailment-plus-calibration verification stage. The main reported outcome is a 5-20% lift in classification accuracy, precision, recall, and F1 over verification-only baselines on FEVER and CLIMATE-FEVER, backed by an ablation that isolates evidence span selection, recalibration thresholds, and retrieval quality. They also release a modular implementation meant to drop into existing LLM pipelines without retraining the base model.

Referee Report

0 major / 3 minor

Summary. The manuscript introduces DAVinCI, a Dual Attribution and Verification framework for improving the factual reliability of LLM-generated claims. The framework attributes claims to both internal model components and external sources, then verifies them via entailment-based reasoning combined with confidence calibration. It reports 5-20% gains in classification accuracy, attribution precision, recall, and F1-score on FEVER and CLIMATE-FEVER relative to verification-only baselines, includes an ablation study isolating evidence span selection, recalibration thresholds, and retrieval quality, and releases a modular open-source implementation.

Significance. If the empirical results hold, DAVinCI offers a practical, integrable approach to making LLM outputs more auditable and trustworthy by explicitly linking attribution and verification. The ablation study helps isolate component contributions, and the code release supports reproducibility—both are clear strengths for a framework paper in this area.

minor comments (3)

The abstract states performance gains of '5-20%' without specifying per-metric or per-dataset values or the exact baselines; the results section should report these numbers with standard deviations or confidence intervals for clarity.
The description of internal attribution to model components is high-level; adding a short pseudocode or diagram in the framework section would improve reproducibility.
Figure captions for the ablation results should be expanded to include key takeaway numbers so readers can interpret them without constant reference to the main text.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive summary of our work on DAVinCI and for recommending minor revision. The referee's assessment correctly identifies the framework's dual attribution and verification stages, the reported gains on FEVER and CLIMATE-FEVER, the ablation study, and the open-source release. No specific major comments were listed in the report, so we have no point-by-point rebuttals to provide. We remain available to incorporate any minor changes requested by the editor.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper presents DAVinCI as a two-stage framework (dual attribution followed by entailment-based verification with calibration) evaluated empirically on FEVER and CLIMATE-FEVER against standard baselines. No equations, fitted parameters, or derivation steps are described that reduce a claimed result to its own inputs by construction. Ablation studies isolate contributions from evidence selection, thresholds, and retrieval without self-referential fitting. The reported 5-20% gains rest on external dataset comparisons and modular implementation rather than any self-definitional, self-citation load-bearing, or ansatz-smuggled step. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the unstated premise that standard entailment models and retrieval components can be composed without domain-specific failure modes; no free parameters, axioms, or invented entities are explicitly listed in the abstract.

axioms (1)

domain assumption LLM outputs can be decomposed into discrete claims that are independently attributable and verifiable.
Implicit in the two-stage design described in the abstract.

pith-pipeline@v0.9.0 · 5550 in / 1288 out tokens · 95039 ms · 2026-05-09T22:45:52.093480+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

16 extracted references · 5 canonical work pages · 2 internal anchors

[1]

Trust but Verify: Introducing DAVinCI -- A Framework for Dual Attribution and Verification in Claim Inference for Language Models

Introduction The rapid proliferation of large language models (LLMs) has revolutionized natural language gen- eration, enabling systems to produce fluent and contextually rich text across diverse domains. How- ever, thisfluencyoftenmasksacriticalvulnerability: the tendency of LLMs to generate factually incor- rect or hallucinated content (Rawte et al., 20...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[2]

Early work in automated fact-checking focused on datasets such as FEVER (Thorne et al., 2018), whichintroducedthetaskofverifyingclaimsagainst evidence retrieved from Wikipedia

Related Work The challenge of ensuring factual correctness in natural language generation has led to a grow- ing body of research in claim verification, retrieval- augmented generation, and model attribution. Early work in automated fact-checking focused on datasets such as FEVER (Thorne et al., 2018), whichintroducedthetaskofverifyingclaimsagainst eviden...

2018
[3]

Retrieval-Augmented Generation (RAG) frame- works (Lewis et al., 2021) have emerged as a pop- ular solution for grounding LLM outputs in external sources

extended this paradigm to other domains, emphasizing the need for domain-specific reason- ing and evidence selection. Retrieval-Augmented Generation (RAG) frame- works (Lewis et al., 2021) have emerged as a pop- ular solution for grounding LLM outputs in external sources. These models combine dense retriev- ers with generative architectures to produce flu...

2021
[4]

and trust-aware scoring (Jiang et al., 2021) aim to align model confidence with prediction re- liability. In the context of claim verification, cali- brated confidence can help distinguish between supported, refuted, and uncertain claims - a capa- bility that is central to our DAVinCI framework. Our work builds on these foundations by inte- grating attrib...

2021
[5]

This section details the architecture, components, and workflow of DAVinCI, along with the rationale behind key design choices

Proposed Method TheDAVinCIframeworkisdesignedtoenhancethe factual reliability of LLM outputs by integrating two complementary stages: Attribution and Verification. This section details the architecture, components, and workflow of DAVinCI, along with the rationale behind key design choices. DAVinCI operates in a two-stage pipeline: 1.Attribution:Given a c...

1957
[6]

Experiments 4.1. Dataset We assess DAVinCI using two prominent datasets for claim verification: FEVER 2 and CLIMATE- FEVER3.Bothdatasetsofferannotatedclaims,sup- porting evidence, and ground-truth labels, making them well-suited for evaluating attribution and veri- fication workflows. FEVER:A curated subset of the original FEVER dataset, featuring claims ...
[7]

Ablation Study We conduct an ablation study to evaluate the im- pact of individual components by comparing full evidence Tables 4 and 5, span-based evidence, and varying threshold settings (0.7, 0.8, 0.9) Ta- bles 6 and 7, enabling us to measure how each design choice influences performance and trust- worthiness. 5.1. DAVinCI withfullevidence The full-evi...
[8]

Conclusion and Future Work In this work, we introduced DAVinCI - a Dual Attri- bution and Verification framework designed to en- hancethefactualreliabilityoflargelanguagemodel outputs. By integrating evidence attribution and entailment-based verification into a unified pipeline, DAVinCI enables LLMs to not only generate claims but also justify and validat...
[9]

Ethics Statement This work does not involve the collection of new hu- man data or personally identifiable information. All experiments were conducted using publicly avail- able datasets (FEVER and CLIMATE-FEVER) that are widely used in the NLP community for factuality and claim verification research. We ensured that our framework, DAVinCI, operates transp...
[10]

DAVinCI consistently outperforms the baseline across multiple metrics

Discussion and Limitations Our experiments demonstrate that integrating at- tribution and verification into a unified pipeline sig- nificantly improves the factual reliability and inter- pretability of LLM outputs. DAVinCI consistently outperforms the baseline across multiple metrics. One of the key insights from our ablation study is the importance of ev...
[11]

Bibliographical References Isabelle Augenstein, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, and Jakob Grue Simonsen
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MultiFC:Areal-worldmulti-domaindataset for evidence-based fact checking of claims. In Proceedings of the 2019 Conference on Empiri- calMethodsinNaturalLanguageProcessingand the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4685–4697, Hong Kong, China. Association for Computational Linguistics. Farima Fatahi Bayat,...

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InProceedings of the 2018 Conference of the North American ChapteroftheAssociationforComputationalLin- guistics: Demonstrations, pages 26–30, New Or- leans, Louisiana

ClaimRank: Detecting check-worthy claims in Arabic and English. InProceedings of the 2018 Conference of the North American ChapteroftheAssociationforComputationalLin- guistics: Demonstrations, pages 26–30, New Or- leans, Louisiana. Association for Computational Linguistics. Yuning Jiang, Manfred Jeusfeld, and Jianguo Ding

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A survey of hallucination in large foundation models

Evaluating the data inconsistency of open- source vulnerability repositories. InProceedings of the 16th International Conference on Availabil- ity, Reliability and Security, ARES ’21, New York, NY, USA. Association for Computing Machinery. Patrick Lewis, Ethan Perez, Aleksandra Piktus, FabioPetroni,VladimirKarpukhin, NamanGoyal, Heinrich Küttler, Mike Lew...

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FEVER: a large-scale dataset for Fact Extraction and VERification

Languagemodelsthatseekforknowledge: Modular search & generation for dialogue and prompt completion. InFindings of the Associa- tion for Computational Linguistics: EMNLP 2022, pages 373–393, Abu Dhabi, United Arab Emi- rates. Association for Computational Linguistics. James Thorne and Andreas Vlachos. 2018. Auto- mated fact checking: Task formulations, met...

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