DocIE@XLLM25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations

Ashish Kangen; Michael F\"arber; Nicholas Popovi\v{c}; Tim Schopf

REVIEW 3 major objections 5 minor 12 references

DocIE@XLLM25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations

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

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Pith's one-line read A fully automatic pipeline turns Wikipedia abstracts into synthetic demonstrations, letting zero-shot document-level entity and relation extraction run without manual annotation, although relation extraction still scores poorly.

desk verdict A useful synthetic dataset and a clean pipeline, but the central claim is unmeasured because the paper never runs the no-demonstration baseline. read the letter →

arxiv 2507.05997 v1 pith:CUINF4QS submitted 2025-07-08 cs.CL

Nicholas Popovič , Ashish Kangen , Tim Schopf , Michael Färber This is my paper · ORCID

classification cs.CL

keywords document-levelinformationextractionzero-shotrelationin-contextlearningsyntheticdatagenerationretrieval-basedpromptingLLMverification

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

The paper tries to establish that a fully synthetic, automatically verified demonstration database can replace manually annotated examples in zero-shot document-level entity and relation extraction. The pipeline uses a reasoning-optimized language model to annotate Wikipedia abstracts, applies rule-based and LLM-based checks to filter errors, and at inference time retrieves one similar example to prompt the same model. This removes the need for human annotation while keeping the setup zero-shot, since the demonstrations are generated and validated without using the task's training data. The authors report that the approach builds a usable dataset of 5,010 documents, but their evaluation shows that even with such demonstrations, in-context joint extraction remains hard, with relation F1 below 4% on the shared task.

What carries the argument

The central object is the synthetic demonstration database: 5,010 Wikipedia abstracts annotated with roughly 59k entities and 30k relation triples, produced by an LLM and then filtered by rule-based span and ID checks plus an LLM-based triple verification step. The retrieval step uses sentence transformers (all-MiniLM-L6-v2) to select one demonstration most similar to the query document, and the inference prompt feeds that demonstration followed by a partially completed annotation JSON that the model must finish. The prompts are engineered to force machine-parseable JSON, inline HTML tags for mentions, entity IDs for coreference, and natural-language descriptions of triples, which together enable the verification hooks.

What would settle it

Take the same retrieval-based in-context pipeline and swap the synthetic demonstrations for gold-annotated demonstrations from Re-DocRED (with the same schema and document type); if the gold demonstrations yield substantially higher entity and relation F1 than the synthetic ones on a held-out set, then the claim that synthetically verified demonstrations can replace manual annotation is falsified. Alternatively, sample a subset of the synthetic annotations and have human annotators judge their correctness; if a significant fraction of triples or entity labels are wrong, the demonstration database is likely noisy enough to mislead the model.

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Extended reading notes

Core claim

The central claim is that a fully automatic pipeline combining synthetic data generation, two-phase verification, and retrieval-based in-context learning can supply high-quality demonstrations for zero-shot document-level IE. The pipeline first has DeepSeek-R1-Distill-Qwen2.5-32B produce JSON annotations with span-tagged text, entity lists, and relation triples described in natural language. Rule-based checks confirm that spans appear in the text and that triple subject and object ids match annotated entities; an LLM-based check then verifies each triple against its natural language description, discarding whole relation types if inconsistencies appear. On the DocIE test set, entity identification reaches 32.86 F1, entity classification 16.19, and relation extraction only 3.29 (general) and 3.01 (strict), with 63.91% of outputs parseable. On Re-DocRED, valid-output results are higher but still weak, indicating that the bottleneck is not just document length or output validity.

Load-bearing premise

The synthetic annotations produced by DeepSeek-R1-Distill-Qwen2.5-32B, after the automatic verification steps, are accurate enough to serve as effective demonstrations—yet the paper never validates that accuracy against human annotations or gold-standard labels, only against internal consistency checks.

Editorial extensions

If this is right

If the approach works as claimed, building demonstration databases for zero-shot IE no longer requires manual annotation, lowering the cost of adapting to new schemas and domains.
The released synthetic dataset of over 5k documents becomes a reusable resource for future few-shot and zero-shot document-level IE research.
The two-phase verification strategy can be applied to other schema-constrained generation tasks that need structured outputs.
The large share of unparseable outputs (63.91% valid on DocIE, 38.8% on Re-DocRED) identifies output validity as a key bottleneck separate from extraction accuracy.
The consistently low relation extraction scores suggest that in-context joint relation extraction at document level remains unsolved even with strong LLMs and demonstrations.

Reading between the lines

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

A direct test of the paper's central premise would be to run the same retrieval-ICL pipeline using gold annotations from Re-DocRED as demonstrations and compare against the synthetic ones; a large gap in favor of gold would indicate that synthetic data, despite verification, still misleads the model.
The parse-failure problem might be addressable with constrained decoding that forces the model to output valid JSON, which could raise F1 more than improving the demonstrations themselves.
The LLM-based triple verification step, which checks directionality against natural language descriptions, could be reused as a general post-verification module for any relation extraction pipeline.
The paper's synthetic dataset, although built from Wikipedia abstracts, may transfer poorly to other domains; an extension would measure retrieval quality and downstream F1 when the demonstration source domain differs from the query domain.

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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

Colleague,

Here's my take on DocIE@XLLM25. It's a clean, honest shared-task systems report, and it ships a genuinely useful synthetic dataset. But the paper's headline claim—that fully synthetic demonstrations can substitute for manual annotation in zero-shot IE—is never actually tested, because the experiments never compare against a no-demonstration baseline.

What's new: the dataset itself (5,010 Wikipedia abstracts with ~59k entity annotations and ~30k relation triples) and the automatic pipeline that builds it. The two-phase annotation with rule-based span checks and LLM-based triple verification is a thoughtful design; having the model write a natural-language description of each triple before emitting the structured form, then checking consistency, is a nice way to catch directionality errors. Reporting the unparseable-output rate on both benchmarks (63.9% valid on DocIE, 38.8% on Re-DocRED) is honest and useful.

The soft spots are real. Most importantly, there is no control isolating the effect of the synthetic demonstrations. We never see the same model without demonstrations, or with randomly retrieved ones, or with a manual one. The stress-test note is correct: the only internal control mentioned (Section 5.1) concerns annotation yield, not inference quality. Given the prompt instruction to use only the provided entity and relation types, a topically similar but schema-mismatched demonstration could actively suppress recall—which might help explain the sub-4% relation F1. Without a baseline, the central contribution is unmeasured.

Second, the 'high-quality' claim for the synthetic database is not validated against gold labels. The internal consistency checks are reasonable, but an LLM verifying an LLM's triples can miss systematic bias. A small human-annotated sample would settle it.

Third, the unparseable-output problem is substantial. Two-thirds of outputs parse on DocIE; fewer on Re-DocRED. The paper's own analysis in Section 5.4 doesn't confirm the length hypothesis, so this remains a major practical weakness.

The citation pattern is fine, and the evaluation is against external benchmarks, so there's no circularity. The pipeline design is coherent and the results are reported without spin.

Who gets value: anyone working on zero-shot IE, synthetic data generation, or in-context learning. The dataset is a plausible resource. The paper deserves a serious referee, but it needs a no-demonstration baseline and some gold validation before the central claim is credible. I'd send it to review expecting a major revision.

Referee Report

3 major / 5 minor

Summary. The paper describes a fully automatic pipeline for synthetic data generation for document-level entity and relation extraction, and its use for retrieval-based in-context learning. The pipeline uses DeepSeek-R1-Distill-Qwen2.5-32B in a zero-shot prompt to annotate Wikipedia abstracts, followed by rule-based checks (entity spans appear in text, entity IDs match triples) and an LLM-based triple-directionality verification. The resulting dataset contains about 5k documents, 59k entities, and 30k relation triples. At inference, the most similar synthetic document is retrieved and used as a single in-context demonstration for the same model, with a two-call strategy over the first paragraph and the full document. The method is evaluated on the DocIE shared task and on Re-DocRED, reporting entity F1 around 32-38% and relation F1 below 7%. The paper concludes that zero-shot document-level IE remains challenging.

Significance. If the central claim is supported, the pipeline could lower annotation costs for schema-constrained IE by substituting fully synthetic, automatically verified demonstrations for manually annotated ones. The authors release their code and synthetic dataset, which is a contribution in itself. The evaluation is honestly reported, including parse rates and the distinction between full and valid-output results. However, the absence of a zero-shot baseline and the lack of validation of the synthetic data against a gold standard mean that the core claim that synthetic demonstrations actively help inference is not yet established. The reported relation extraction F1 scores are very low, so the practical strength of the method remains unclear. The paper is a reasonable shared-task system description but needs additional experiments to support its headline contribution.

major comments (3)

[§3.1, §5.3] The central claim of the paper is that fully synthetic, automatically verified demonstrations can substitute for manual annotation in zero-shot in-context learning. This claim is not measured: Tables 1-3 compare against external shared-task systems and against the pipeline's own valid-output subset, but never against zero-shot inference with the same model and prompt without any demonstration. Without such a baseline, the reader cannot tell whether the retrieved synthetic demonstrations help, hurt, or have no effect on extraction quality. Please add a zero-shot no-demonstration control on both DocIE and Re-DocRED, and ideally a control using a manually written demonstration. This is load-bearing for the abstract's claim and can be added with minimal effort.
[§4.2, §4.3] The paper repeatedly calls the synthetic demonstration database 'high-quality', but this quality is never validated against any gold standard. The verification mechanisms in Sections 4.2 and 4.3 are internal consistency checks: entity spans must occur in the text, triple subject/object IDs must match extracted entities, and relation directionality is judged by the same model family that produced the annotations. These checks cannot detect systematic errors such as missing entities, incorrect type assignments, or relations that are plausible but not supported by the text. Since the demonstration examples are fed to the inference model with a prompt that forbids adding new entity or relation types (Figure 4), any noise or schema mismatch in the synthetic data can directly suppress recall and relation F1. To support the quality claim, please provide a human evaluation on a random sample of the synthetic dataset, or compare the synthetic annotations against gold labels on a subset of an existing benchmark (e.g., Re-DocRED training data).
[§5.4, Table 3] Even when restricting to documents with valid outputs, relation F1 remains below 7% on Re-DocRED (general 6.87%, strict 4.26%) and the parse rate is only 38.8%. The paper notes these low numbers but does not analyze whether they stem from the inference model, the retrieval mechanism, or the synthetic demonstration quality. A concrete analysis of parse failures and their correlation with retrieval relevance or schema overlap would help interpret the results and would strengthen the paper's contribution, especially because the abstract claims that ICL with synthetic demonstrations is a viable alternative to manual annotation.

minor comments (5)

[Abstract] The abstract says 'in-context joint entity and relation extraction at document-level remains a challenging task', which is consistent with the results; consider wording the contribution more cautiously in the abstract, e.g., 'we present a pipeline and dataset' rather than implying the method already achieves strong extraction performance.
[Section 4.2] The sentence 'We find that this strategy drastically decreases failures of the model to adhere to the annotation format for long documents' is about the inference pipeline but appears in Section 4.2, which is about synthetic data generation; please move it to Section 3.1 or clarify.
[Figure 4] The inference prompt contains 'relation_types' + ": [...]" with a stray plus sign; this looks like a LaTeX/typing artifact and should be fixed to 'relation_types': [...].
[Section 5.1] The text-length experiment is described only qualitatively and with a figure. Reporting the correlation coefficient or a small table of error rates across length buckets would make the claim 'text length and total verification failures are highly correlated' more precise.
[Section 6] The sentence 'exhibits consistency and wide coverage of entity and relation types' is grammatically awkward; consider 'is internally consistent and covers a wide range of entity and relation types'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the synthetic demonstration pipeline and the DocIE/Re-DocRED evaluations are externally grounded, and self-citations are motivational rather than load-bearing.

full rationale

The derivation chain does not reduce to its inputs. The synthetic demonstration database is produced from Wikipedia abstracts via a zero-shot LLM annotation prompt and rule/LLM verification (Sections 4.2-4.3); the evaluation targets are the external DocIE shared task and Re-DocRED test sets (Sections 5.3-5.4). No parameter is fitted to the test labels and no equation links the synthetic annotations to the reported F1 scores by construction. The inference prompt's instruction 'Do not add any entity or relation types! Use only the ones provided' constrains output to the query document's schema, not to the synthetic demonstration's schema, so the demonstration is an example rather than a definitional target. Self-citations (Popovic and Färber 2022; Rogulsky et al. 2024, which share authors) appear only as related-work motivation for few-shot IE and synthetic augmentation; neither is used to justify a theorem or to rule out alternatives. The main weaknesses - no zero-shot-without-demonstration baseline, no human validation of synthetic annotation quality, unparseable outputs, and the note in footnote 4 that training data was seen during prompt construction - are evaluation/correctness concerns, not circularity. The paper's modest scores are reported directly against external labels, which is the opposite of a self-fulfilling construction.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claim rests on three unvalidated assumptions about model annotation quality, automated verification sufficiency, and retrieval relevance. The only hand-tuned numerical choices (truncation length, rerun temperature, and k=1) are design decisions that affect the results but are not fitted to the test sets. No new entities, forces, or conserved quantities are introduced.

free parameters (3)

truncation length threshold = 100 words
Sentences are added until the abstract exceeds 100 words; the threshold was chosen by hand based on initial error-rate experiments (Section 5.1) and affects the composition of the synthetic dataset and downstream retrieval.
rerun temperature = 0.2
Annotations that fail verification are rerun once with temperature 0.2 (Section 5.2), a hand-chosen setting that changes the annotation acceptance rate.
number of in-context demonstrations = 1
Only a single retrieved synthetic example is used as the demonstration in both LLM calls (Section 3.1); the choice of k is not varied or tuned on a validation set.

assumptions (3)

domain assumption DeepSeek-R1-Distill-Qwen2.5-32B produces accurate schema-constrained entity and relation annotations in a zero-shot setting
The entire synthetic dataset is built from this model's outputs. No inter-annotator agreement, human evaluation, or comparison to gold labels is reported, so the accuracy of the base annotations is assumed.
domain assumption The automated verification steps (span checks, ID consistency, triple verification prompt) are sufficient to ensure high annotation quality
Sections 4.2 and 4.3 describe the checks and state they 'substantially improve annotation fidelity', but no measurement against a gold standard is provided, and the final dataset is still labeled 'high quality'.
domain assumption Similarity measured by all-MiniLM-L6-v2 embeddings identifies demonstrations that improve extraction performance
Section 5.3 uses this retrieval model without evaluating retrieval quality, such as comparison to other embeddings or random demonstrations. The assumption that similar documents make good in-context examples is unstated and untested.

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Cite this review

Pith. "Pith review of DocIE@XLLM25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations." pith.science (2026). https://pith.science/paper/CUINF4QS

@misc{pith2026250705997,
  author       = {Pith},
  title        = {Pith review of: DocIE@XLLM25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CUINF4QS}},
  note         = {Machine review of arXiv:2507.05997}
}

abstract

Large, high-quality annotated corpora remain scarce in document-level entity and relation extraction in zero-shot or few-shot settings. In this paper, we present a fully automatic, LLM-based pipeline for synthetic data generation and in-context learning for document-level entity and relation extraction. In contrast to existing approaches that rely on manually annotated demonstrations or direct zero-shot inference, our method combines synthetic data generation with retrieval-based in-context learning, using a reasoning-optimized language model. This allows us to build a high-quality demonstration database without manual annotation and to dynamically retrieve relevant examples at inference time. Based on our approach we produce a synthetic dataset of over $5k$ Wikipedia abstracts with approximately $59k$ entities and $30k$ relation triples. Finally, we evaluate in-context learning performance on the DocIE shared task, extracting entities and relations from long documents in a zero-shot setting. We find that in-context joint entity and relation extraction at document-level remains a challenging task, even for state-of-the-art large language models.

Figures

Figures reproduced from arXiv: 2507.05997 by the authors.

**Figure 2.** Prompt used for zero-shot text annotation. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Prompt used for post-processing of triples. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

Figures from the paper (6 more)

**Figure 4.** Figure 4: Prompt used for inference. An example of an in-context demonstration is shown in Appendix [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

**Figure 5.** Figure 5: Category distribution of level 4 vital articles (approx. 10k). [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

**Figure 6.** Figure 6: Error distribution for initial set of experiments of first annotation phase. [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]

**Figure 7.** Figure 7: Top 30 entity types in final synthetic dataset and their frequency. [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]

**Figure 8.** Figure 8: Top 30 relation types in final synthetic dataset and their frequency. [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]

**Figure 9.** Figure 9: Example of final synthetic data. 12 [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

12 extracted references · 8 canonical work pages

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DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, and 181 others. 2025. https://arxiv.org/abs/2501.12948 Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement lea...

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, and 181 others. 2025. https://arxiv.org/abs/2501.12948 Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement lea...

arXiv 2025

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A survey on cutting-edge relation extraction techniques based on language models

Jose A. Diaz-Garcia and Julio Amador Diaz Lopez. 2024. https://arxiv.org/abs/2411.18157 A survey on cutting-edge relation extraction techniques based on language models . Preprint, arXiv:2411.18157

work page Pith review arXiv 2024

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Honghao Gui, Lin Yuan, Hongbin Ye, Ningyu Zhang, Mengshu Sun, Lei Liang, and Huajun Chen. 2024. https://doi.org/10.18653/v1/2024.acl-short.13 IEP ile: Unearthing large scale schema-conditioned information extraction corpus . In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 127--146,...

doi:10.18653/v1/2024.acl-short.13 2024

[4] [4]

Martin Josifoski, Marija Sakota, Maxime Peyrard, and Robert West. 2023. https://doi.org/10.18653/v1/2023.emnlp-main.96 Exploiting asymmetry for synthetic training data generation: S ynth IE and the case of information extraction . In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 1555--1574, Singapore. Associ...

doi:10.18653/v1/2023.emnlp-main.96 2023

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Junpeng Li, Zixia Jia, and Zilong Zheng. 2023. https://doi.org/10.18653/v1/2023.emnlp-main.334 Semi-automatic data enhancement for document-level relation extraction with distant supervision from large language models . In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5495--5505, Singapore. Association for C...

work page doi:10.18653/v1/2023.emnlp-main.334 2023

[6] [6]

XLLM ACL 2025 Shared Task-IV : Document-level Information Extraction

Shared Task Organizers. XLLM ACL 2025 Shared Task-IV : Document-level Information Extraction . https://xllms.github.io/DocIE/

work page 2025

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Nicholas Popovic and Michael F \"a rber. 2022. https://doi.org/10.18653/v1/2022.naacl-main.421 Few-shot document-level relation extraction . In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5733--5746, Seattle, United States. Association for Computation...

work page doi:10.18653/v1/2022.naacl-main.421 2022

[8] [8]

Nils Reimers and Iryna Gurevych. 2019. https://doi.org/10.18653/v1/D19-1410 Sentence- BERT : Sentence embeddings using S iamese BERT -networks . In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3982--3992, Hong Kong, Chi...

doi:10.18653/v1/d19-1410 2019