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arxiv: 2601.04932 · v2 · submitted 2026-01-08 · 💻 cs.CL

GenProve: Learning to Generate Text with Fine-Grained Provenance

Jingxuan Wei , Xingyue Wang , Yanghaoyu Liao , Jie Dong , Yuchen Liu , Caijun Jia , Bihui Yu , Junnan Zhu This is my paper

Pith reviewed 2026-05-16 16:22 UTC · model grok-4.3

classification 💻 cs.CL
keywords fine-grained provenancetext generationLLM hallucination mitigationprovenance triplesReFInE datasetGRPO optimizationinference gap
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The pith

GenProve trains models to output fluent answers together with sentence-level provenance triples that label each claim as quotation, compression, or inference.

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

The paper establishes Generation-time Fine-grained Provenance as a task requiring models to generate answers while producing structured evidence links at the sentence level. It supplies the ReFInE dataset of expert-annotated examples that separate direct quotation from compression and from inference. Training proceeds through supervised fine-tuning then Group Relative Policy Optimization that rewards both answer quality and provenance correctness at once. This yields better joint performance than fourteen competing models and shows that current systems manage quotation well but falter on inference steps. Readers should care because coarse citations leave users unable to judge how much of a claim rests on explicit evidence versus added reasoning.

Core claim

GenProve uses supervised fine-tuning on ReFInE followed by Group Relative Policy Optimization with a composite reward for answer fidelity and provenance correctness, resulting in outputs that include explicit sentence-level provenance triples distinguishing quotation, compression, and inference, and this approach outperforms 14 strong LLMs while revealing a persistent gap in handling inference-based provenance.

What carries the argument

The composite reward in Group Relative Policy Optimization (GRPO) that scores both the generated answer and the accuracy of its sentence-level provenance annotations for quotation, compression, and inference.

If this is right

  • Generated text carries verifiable links showing exactly how each sentence relates to its sources.
  • Joint evaluation of fidelity and provenance becomes the standard for measuring accountable generation.
  • Training explicitly targets the inference gap, directing future work toward better reasoning provenance.
  • Systems can now be audited at the level of individual claims rather than whole responses.

Where Pith is reading between the lines

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

  • Provenance training of this kind could be adapted to summarization or question-answering pipelines outside the current dataset.
  • The observed quotation-inference gap indicates that next-token prediction alone does not teach explicit evidence chaining.
  • User interfaces might display inference steps with different visual cues than direct quotes to aid trust assessment.
  • Scaling the method to longer documents would test whether sentence-level provenance remains tractable.

Load-bearing premise

The expert-verified annotations reliably distinguish quotation, compression, and inference in a manner that generalizes beyond the ReFInE dataset.

What would settle it

Independent expert re-annotation of GenProve outputs on new topics showing no gain in provenance accuracy over baselines would falsify the performance claim.

read the original abstract

Large language models (LLM) often hallucinate, and while adding citations is a common solution, it is frequently insufficient for accountability as users struggle to verify how a cited source supports a generated claim. Existing methods are typically coarse-grained and fail to distinguish between direct quotes and complex reasoning. In this paper, we introduce Generation-time Fine-grained Provenance, a task where models must generate fluent answers while simultaneously producing structured, sentence-level provenance triples. To enable this, we present ReFInE (Relation-aware Fine-grained Interpretability & Evidence), a dataset featuring expert verified annotations that distinguish between Quotation, Compression, and Inference. Building on ReFInE, we propose GenProve, a framework that combines Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO). By optimizing a composite reward for answer fidelity and provenance correctness, GenProve significantly outperforms 14 strong LLMs in joint evaluation. Crucially, our analysis uncovers a reasoning gap where models excel at surface-level quotation but struggle significantly with inference-based provenance, suggesting that verifiable reasoning remains a frontier challenge distinct from surface-level citation.

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.

Referee Report

4 major / 2 minor

Summary. The paper introduces the Generation-time Fine-grained Provenance task, presents the ReFInE dataset with expert-verified sentence-level annotations distinguishing Quotation, Compression, and Inference, and proposes the GenProve framework that combines SFT with GRPO to optimize a composite reward for answer fidelity and provenance correctness. It claims that GenProve significantly outperforms 14 strong LLMs in joint evaluation of these metrics while analysis reveals a reasoning gap in which models handle surface-level quotation well but struggle with inference-based provenance.

Significance. If the empirical results prove robust, the work would be significant for moving beyond coarse-grained citations toward accountable, fine-grained provenance in LLM generation. It directly targets hallucination and verifiability issues, introduces a new dataset and optimization approach, and surfaces a concrete distinction between quotation and inference capabilities that could guide future research on trustworthy reasoning.

major comments (4)
  1. [Abstract] Abstract: the claim that GenProve 'significantly outperforms 14 strong LLMs in joint evaluation' is stated without any quantitative metrics, baseline details, or ablation results, so the central superiority claim cannot be assessed from the provided text.
  2. [ReFInE Dataset] ReFInE dataset section: no inter-annotator agreement statistics are reported for the expert sentence-level labels (Quotation/Compression/Inference), leaving open whether these distinctions are stable or dataset-specific conventions.
  3. [GenProve Framework] GenProve framework and experiments: the GRPO composite reward is trained and evaluated on the same ReFInE annotations with no reported ablation isolating GRPO from SFT and no external validation set, so measured gains could arise from fitting annotation patterns rather than improved reasoning.
  4. [Analysis] Analysis of reasoning gap: the reported gap between quotation and inference performance rests on the same internal evaluation; without cross-dataset testing or held-out validation the conclusion that 'verifiable reasoning remains a frontier challenge' is not yet load-bearing.
minor comments (2)
  1. [Introduction] The notation for provenance triples (sentence-level) would benefit from an early concrete example to clarify the three categories before the formal definition.
  2. [Related Work] Related-work discussion of prior coarse-grained citation methods could include a brief comparison table of granularity levels to better situate the contribution.

Simulated Author's Rebuttal

4 responses · 0 unresolved

We thank the referee for the thoughtful and constructive report. We address each major comment below and will revise the manuscript to improve clarity, completeness, and robustness of the claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that GenProve 'significantly outperforms 14 strong LLMs in joint evaluation' is stated without any quantitative metrics, baseline details, or ablation results, so the central superiority claim cannot be assessed from the provided text.

    Authors: We agree that the abstract must be self-contained. In the revision we will insert the key joint-evaluation numbers (e.g., GenProve vs. best baseline on fidelity and provenance F1), name the 14 LLMs, and briefly note the main SFT-vs-GRPO ablation result so that the superiority claim can be evaluated directly from the abstract. revision: yes

  2. Referee: [ReFInE Dataset] ReFInE dataset section: no inter-annotator agreement statistics are reported for the expert sentence-level labels (Quotation/Compression/Inference), leaving open whether these distinctions are stable or dataset-specific conventions.

    Authors: We acknowledge the omission. The expert annotations were performed with a documented protocol; we will add inter-annotator agreement statistics (Cohen’s kappa and raw agreement percentages) for the three provenance classes in the revised dataset section. revision: yes

  3. Referee: [GenProve Framework] GenProve framework and experiments: the GRPO composite reward is trained and evaluated on the same ReFInE annotations with no reported ablation isolating GRPO from SFT and no external validation set, so measured gains could arise from fitting annotation patterns rather than improved reasoning.

    Authors: We will add an explicit ablation table isolating the contribution of GRPO over SFT alone on the ReFInE test split. We also agree that an external validation set would strengthen the result; we will report performance on a held-out corpus (or clearly state its absence as a limitation) in the revised experiments section. revision: partial

  4. Referee: [Analysis] Analysis of reasoning gap: the reported gap between quotation and inference performance rests on the same internal evaluation; without cross-dataset testing or held-out validation the conclusion that 'verifiable reasoning remains a frontier challenge' is not yet load-bearing.

    Authors: We will tone down the phrasing and add a cross-dataset experiment on an additional held-out corpus where feasible. If external data are unavailable, we will explicitly list the reliance on the ReFInE test split as a limitation while noting that the quotation–inference gap is consistent across all 14 evaluated models. revision: partial

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper introduces a new task (Generation-time Fine-grained Provenance) and dataset (ReFInE) with expert sentence-level annotations distinguishing Quotation/Compression/Inference, then trains GenProve via SFT+GRPO on a composite reward for fidelity and provenance correctness. Outperformance claims and the reported reasoning gap rest on empirical joint evaluation against 14 LLMs. No equations, self-definitions, or load-bearing self-citations appear in the provided text that reduce any prediction or result to its inputs by construction. The derivation is a standard empirical ML pipeline with independent content from the new annotations and optimization; any risk of dataset-specific fitting is a correctness concern, not circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The approach rests on standard supervised fine-tuning and policy optimization assumptions plus the premise that expert sentence-level labels capture meaningful provenance distinctions; no free parameters or invented entities are described in the abstract.

axioms (2)
  • domain assumption Expert annotations in ReFInE provide ground-truth distinctions between quotation, compression, and inference.
    Invoked when constructing the dataset and reward signal; no independent verification method is stated.
  • domain assumption Composite reward combining answer fidelity and provenance correctness produces aligned model behavior.
    Central to the GRPO stage; exact weighting and components not specified in abstract.

pith-pipeline@v0.9.0 · 5516 in / 1365 out tokens · 38946 ms · 2026-05-16T16:22:19.260798+00:00 · methodology

discussion (0)

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. TRACER: Verifiable Generative Provenance for Multimodal Tool-Using Agents

    cs.CL 2026-05 unverdicted novelty 7.0

    TRACER attaches verifiable sentence-level provenance records to multimodal agent outputs using tool-turn alignment and semantic relations, yielding 78.23% answer accuracy and fewer tool calls than baselines on TRACE-Bench.