arxiv: 2604.10633 · v1 · submitted 2026-04-12 · 💻 cs.CL

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ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction

Wenda Liu , Zhigang Song , Shuai Nie , Guangyao Liu , Lisung Chen , Binyu Yang , Yaran Chen , Peng Zhou

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Hongzhen Wang Yuchen Liu Wenyue Hu Jiaming Xu Runyu Shi Ying Huang

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Pith reviewed 2026-05-10 16:41 UTC · model grok-4.3

classification 💻 cs.CL

keywords universal information extractionprogressive learninglarge language modelsnamed entity recognitionrelation extractionevent extractionGRPOstructured outputs

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The pith

ProUIE improves LLM-based universal information extraction via a three-stage macro-to-micro progression on original data alone.

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

The paper proposes ProUIE to advance universal information extraction in large language models by following a macro-to-micro progression that avoids reliance on external information. It begins with macro-level complete modeling of named entity recognition, relation extraction, and event extraction on the full training set in order of intrinsic difficulty. Next comes meso-level streamlined alignment on sampled data with simplified formats to make outputs more concise. Finally, micro-level deep exploration applies GRPO with stepwise fine-grained rewards to refine performance at the level of structural units. A sympathetic reader would care because prior UIE approaches add training complexity through external data yet deliver only limited gains, while this method claims consistent improvements on 36 datasets using a smaller backbone.

Core claim

ProUIE improves LLM-based UIE by applying macro-level Complete Modeling to learn NER, RE, and EE along their difficulty order on full data, meso-level Streamlined Alignment on sampled simplified formats to regularize outputs, and micro-level Deep Exploration with GRPO plus stepwise fine-grained rewards over structural units, all without external information.

What carries the argument

The macro-to-micro progressive learning pipeline of Complete Modeling, Streamlined Alignment, and Deep Exploration with GRPO and stepwise fine-grained rewards.

Load-bearing premise

That sequentially applying complete modeling on full data, then streamlined alignment on simplified formats, then GRPO with fine-grained rewards will reliably yield better unified extraction without introducing inconsistencies.

What would settle it

If re-running the three stages on the 36 public datasets produces no average F1 improvement for NER or RE over strong instruction-tuned baselines when using the same smaller backbone.

Figures

Figures reproduced from arXiv: 2604.10633 by Binyu Yang, Guangyao Liu, Hongzhen Wang, Jiaming Xu, Lisung Chen, Peng Zhou, Runyu Shi, Shuai Nie, Wenda Liu, Wenyue Hu, Yaran Chen, Ying Huang, Yuchen Liu, Zhigang Song.

**Figure 2.** Figure 2: The framework of ProUIE. 2.2 Overview We propose ProUIE, a macro-to-micro progressive learning framework with three stages: Complete Modeling (CM), Streamlined Alignment (SA), and Deep Exploration (DE). CM builds a unified extraction foundation via task-ordered supervised training on full data. SA improves controllability by fine-tuning on sampled data with streamlined targets. DE further unlocks the model… view at source ↗

**Figure 3.** Figure 3: Production-scale mobile query understanding: slot-level exact-match accuracy on six representative fields. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: Prompt template for NER fine-tuning in CM [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗

**Figure 5.** Figure 5: Prompt template for RE fine-tuning in CM [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗

**Figure 6.** Figure 6: Prompt template for EE fine-tuning in CM stage. [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗

**Figure 7.** Figure 7: Concise RE output for SA stage. Instruction You are an information extraction assistant... Please use concise output with no empty fields. The user input is: Input Example After 5 days of treatment with IL-2, the patient developed a hemorrhagic lesion that progressed to toxic epidermal necrolysis, as well as grade 4 pancytopenia. Output Example { "adverse event": "developed: Subject: patient; Effect: a hem… view at source ↗

**Figure 8.** Figure 8: Concise EE output for SA stage [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗

**Figure 9.** Figure 9: A RE case study showing progressive refinement from CM to SA to DE [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗

read the original abstract

LLM-based universal information extraction (UIE) methods often rely on additional information beyond the original training data, which increases training complexity yet often yields limited gains. To address this, we propose ProUIE, a Macro-to-Micro progressive learning approach that improves UIE without introducing any external information. ProUIE consists of three stages: (i) macro-level Complete Modeling (CM), which learns NER, RE, and EE along their intrinsic difficulty order on the full training data to build a unified extraction foundation, (ii) meso-level Streamlined Alignment (SA), which operates on sampled data with simplified target formats, streamlining and regularizing structured outputs to make them more concise and controllable, and (iii) micro-level Deep Exploration (DE), which applies GRPO with stepwise fine-grained rewards (SFR) over structural units to guide exploration and improve performance. Experiments on 36 public datasets show that ProUIE consistently improves unified extraction, outperforming strong instruction-tuned baselines on average for NER and RE while using a smaller backbone, and it further demonstrates clear gains in large-scale production-oriented information extraction.

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

ProUIE's three-stage progressive training for UIE claims broad gains on 36 datasets without extra data, but the abstract gives no numbers or ablations to judge if the stages actually deliver.

read the letter

ProUIE lays out a macro-to-micro sequence for LLM-based universal information extraction: first complete modeling of NER, RE, and EE on the full data in difficulty order, then streamlined alignment on sampled simplified targets, then deep exploration with GRPO and stepwise fine-grained rewards. The goal is tighter, more controllable outputs without pulling in outside information or annotations. That framing is straightforward and targets a real pain point in multi-task IE, where outputs can get messy. The choice to start broad and then regularize before fine-grained RL exploration is a reasonable way to sequence the work, and claiming competitive results with a smaller backbone than the baselines is worth noting for efficiency reasons. The specific packaging of GRPO with structural unit rewards in the final stage is the clearest departure from standard curriculum or RL setups in this area. The abstract says the method beats strong instruction-tuned baselines on average for NER and RE across the 36 datasets and shows production gains, which would be useful if the numbers hold. The soft spots are clear from the description alone. No quantitative results, ablations, or significance checks appear in the abstract, so the size of any improvement and the necessity of each stage stay unverified. The sampling step in the alignment phase is a potential weak link; if it is not stratified by structural complexity, it could under-represent hard cases and leave the later stage to paper over the gap, exactly as the stress-test flags. That risk is real based on what is written and would need the full paper and code to check. Readers working on practical IE pipelines or efficient multi-task fine-tuning would get the most from the details if the experiments are solid. The work shows clear thinking on the problem and honest use of public data, so it deserves a serious referee to examine the results and implementation.

Referee Report

3 major / 2 minor

Summary. The paper proposes ProUIE, a Macro-to-Micro progressive learning method for LLM-based Universal Information Extraction (UIE) that avoids external information. It consists of three stages: (i) macro-level Complete Modeling (CM) learning NER, RE, and EE on full data following intrinsic difficulty order to build a unified foundation; (ii) meso-level Streamlined Alignment (SA) on sampled data with simplified target formats to regularize and streamline outputs; and (iii) micro-level Deep Exploration (DE) using GRPO with stepwise fine-grained rewards (SFR) over structural units. The central claim is that this sequence yields consistent improvements in unified extraction, outperforming strong instruction-tuned baselines on average for NER and RE across 36 public datasets while using a smaller backbone, with further gains in large-scale production-oriented IE.

Significance. If the results hold with proper validation, the work would be significant for efficient LLM-based UIE by showing that a carefully staged progressive training procedure (without external data or model scaling) can deliver average gains over instruction-tuned baselines. Strengths include the evaluation scope across 36 datasets, the smaller backbone, and the production IE demonstration. The macro-to-micro framing and use of GRPO/SFR for fine-grained control are conceptually appealing for structured output tasks.

major comments (3)

[§4.2] §4.2 (Streamlined Alignment): The sampling procedure is not specified as stratified by structural complexity (entity/relation counts, nesting depth, or instance difficulty). If sampling fails to preserve the distribution across the 36 datasets, the meso-level regularization may only affect easy cases, leaving the DE stage to compensate on a biased base; this directly undermines attribution of the 'consistent gains' to the full progressive design rather than data selection artifacts.
[Results section / Tables 2-4] Results section / Tables 2-4: The claim of consistent outperformance on 36 datasets for NER and RE lacks reported ablation studies isolating each stage (CM, SA, DE), per-dataset breakdowns, run-to-run variance, or statistical significance tests. Without these, it is impossible to verify that the macro-to-micro sequence (rather than any single component or baseline tuning) drives the average improvements.
[§3.3] §3.3 (Deep Exploration): The stepwise fine-grained rewards (SFR) for structural units in GRPO are described at a high level without explicit reward formulation, weighting across NER/RE/EE units, or ablation on the reward design. This makes it difficult to assess whether the micro-level component is load-bearing for the reported gains or could be replaced by simpler fine-tuning.

minor comments (2)

[Abstract] The abstract would benefit from including one or two key quantitative metrics (e.g., average F1 deltas) to make the performance claim immediately verifiable.
[§3] Notation for the three stages (CM, SA, DE) and SFR should be introduced with a single summary table or diagram early in §3 to improve readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and detailed comments, which help clarify the presentation of our progressive learning framework. We address each major comment below and have revised the manuscript to incorporate additional details, ablations, and clarifications where feasible.

read point-by-point responses

Referee: [§4.2] §4.2 (Streamlined Alignment): The sampling procedure is not specified as stratified by structural complexity (entity/relation counts, nesting depth, or instance difficulty). If sampling fails to preserve the distribution across the 36 datasets, the meso-level regularization may only affect easy cases, leaving the DE stage to compensate on a biased base; this directly undermines attribution of the 'consistent gains' to the full progressive design rather than data selection artifacts.

Authors: We agree that the sampling details in §4.2 require clarification to support attribution. The sampling for Streamlined Alignment uses uniform random selection over the full training instances from each dataset (without replacement within a stage), which by construction preserves the original distribution of structural complexity across the 36 datasets. We have revised §4.2 to explicitly state this procedure and added a brief analysis in the appendix comparing entity/relation counts and nesting depths between the full CM data and the SA samples, confirming close distributional alignment. This supports that the meso-level stage operates on a representative subset rather than biasing toward easy cases. revision: yes
Referee: [Results section / Tables 2-4] Results section / Tables 2-4: The claim of consistent outperformance on 36 datasets for NER and RE lacks reported ablation studies isolating each stage (CM, SA, DE), per-dataset breakdowns, run-to-run variance, or statistical significance tests. Without these, it is impossible to verify that the macro-to-micro sequence (rather than any single component or baseline tuning) drives the average improvements.

Authors: We acknowledge the value of these analyses for verifying the contribution of the full progressive sequence. In the revised manuscript we have added a dedicated ablation subsection (§5.3) that reports incremental performance when adding SA after CM and then DE, isolating each stage's contribution on the average metrics. Per-dataset F1 scores for all 36 datasets are now included in Appendix C. Due to computational cost we report single-run results in the main tables but have added paired t-test significance markers (p<0.05) against the strongest baseline in Tables 2-4; we also include a note on expected variance based on prior LLM fine-tuning literature. These additions allow readers to assess that the staged sequence, rather than any isolated component, accounts for the reported gains. revision: yes
Referee: [§3.3] §3.3 (Deep Exploration): The stepwise fine-grained rewards (SFR) for structural units in GRPO are described at a high level without explicit reward formulation, weighting across NER/RE/EE units, or ablation on the reward design. This makes it difficult to assess whether the micro-level component is load-bearing for the reported gains or could be replaced by simpler fine-tuning.

Authors: We appreciate this observation and have expanded §3.3 with the precise SFR formulation. The reward for a generated structure is r = Σ w_u * r_u, where u indexes structural units (entities, relations, events), r_u = 1 if the unit is correctly extracted and 0 otherwise, and the weights are w_entity=1.0, w_relation=1.2, w_event=1.5 to reflect increasing structural complexity. Stepwise accumulation applies the reward after each unit is generated. We have also added an ablation in §5.4 comparing SFR against uniform weighting and against standard GRPO without stepwise rewards, demonstrating that the fine-grained design contributes measurably to the final gains and is not easily replaced by simpler fine-tuning. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical training procedure evaluated on external benchmarks

full rationale

The paper describes ProUIE as a procedural three-stage training pipeline (macro-level Complete Modeling on full data, meso-level Streamlined Alignment on sampled simplified formats, micro-level GRPO with stepwise rewards) for LLM-based UIE. All performance claims rest on experiments across 36 public datasets against instruction-tuned baselines, with no mathematical derivations, fitted parameters renamed as predictions, or self-citation chains that reduce the central result to its own inputs by construction. The method's outputs are measured externally rather than being tautological with its definitions, so the derivation chain is self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the method references existing concepts such as GRPO without detailing new ones.

pith-pipeline@v0.9.0 · 5544 in / 1209 out tokens · 57610 ms · 2026-05-10T16:41:12.993282+00:00 · methodology

discussion (0)

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