Paper Citation Record · LEDGER

Large Language Model for Extracting Complex Contract Information in Industrial Scenes

As of 9 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2507.06539.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.06539 v2

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:05:00.526954Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

18 of 18 outbound references displayed

verified exact3
verified fuzzy1
unresolved13
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cdb6cd56-c233-4dec-bda3-6fe5591d9999 · outbound

This paper cites Qwen Technical Report.

Large Language Model for Extracting Complex Contract Information in Industrial Scenes Qwen Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T19:04:58.704936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3470bbaa-1ad5-4323-9125-bfd6cbbc04ba · outbound

This paper cites Keybert: Minimal keyword extraction with bert.

Large Language Model for Extracting Complex Contract Information in Industrial Scenes Keybert: Minimal keyword extraction with bert

Reference 7

Resolution: malformed identifier; no resolver link, observed 2026-08-06T19:04:59.388828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c2a93a9-f519-4dbc-99fc-1e0ab3fa2fa7 · outbound

This paper cites Scaling Language Models: Methods, Analysis & Insights from Training Gopher.

Large Language Model for Extracting Complex Contract Information in Industrial Scenes Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T19:04:59.717555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8476f9c2-51ed-46de-bbef-0b506b29eeb8 · outbound

This paper cites GPT-RE: In-context Learning for Relation Extraction using Large Language Models.

Large Language Model for Extracting Complex Contract Information in Industrial Scenes GPT-RE: In-context Learning for Relation Extraction using Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T19:05:00.047398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b75e7d8-0d25-46df-b5f0-7693dab72476 · outbound

This paper cites Code4Struct: Code Generation for Few-Shot Event Structure Prediction.

Large Language Model for Extracting Complex Contract Information in Industrial Scenes Code4Struct: Code Generation for Few-Shot Event Structure Prediction

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T19:05:00.386580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6b88b13-c81b-4954-ab92-18a6a6dacecd · outbound

This paper cites Baichuan 2: Open Large-scale Language Models.

Large Language Model for Extracting Complex Contract Information in Industrial Scenes Baichuan 2: Open Large-scale Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T19:05:00.512675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae640134-eb79-4a5e-8f8f-1a948690d58b · outbound

This paper cites ZeroGen: Efficient Zero-shot Learning via Dataset Generation.

Large Language Model for Extracting Complex Contract Information in Industrial Scenes ZeroGen: Efficient Zero-shot Learning via Dataset Generation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T19:05:00.516123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa2efced-88cc-4d1b-b5cc-20d2765a1e94 · outbound

This paper cites Generative Entity Typing with Curriculum Learning.

Large Language Model for Extracting Complex Contract Information in Industrial Scenes Generative Entity Typing with Curriculum Learning

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:05:00.590257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1825bea4-4d5a-4c65-bab1-772b60439cf5 · outbound

This paper cites Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors.

Large Language Model for Extracting Complex Contract Information in Industrial Scenes Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:05:00.576992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a4923cae-b120-4c8b-b6a5-e7aa67303012 · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

Large Language Model for Extracting Complex Contract Information in Industrial Scenes Fine-Tuning Language Models from Human Preferences

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T19:05:00.526954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd6e580b-e55d-4626-85d7-c558e8240a93 · outbound

This paper cites Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model.

Large Language Model for Extracting Complex Contract Information in Industrial Scenes Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 1975

Resolution: unresolved; no resolver link, observed 2026-08-06T19:04:59.893228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 828531ff-d439-40e5-af56-fb86db235616 · outbound

This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

Large Language Model for Extracting Complex Contract Information in Industrial Scenes D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 1992

Resolution: unresolved; no resolver link, observed 2026-08-06T19:04:58.823604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2c77f2e-7519-49be-af8c-baa9f5544cd5 · outbound

This paper cites Unified Structure Generation for Universal Information Extraction.

Large Language Model for Extracting Complex Contract Information in Industrial Scenes Unified Structure Generation for Universal Information Extraction

Reference 2004

Resolution: unresolved; no resolver link, observed 2026-08-06T19:04:59.270187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7f822eb-49cf-4e7f-b270-ed923b689d6a · outbound

This paper cites Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models.

Large Language Model for Extracting Complex Contract Information in Industrial Scenes Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models

Reference 2011

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:05:00.672133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 63003e2b-895b-4a42-b47e-bbf84eb235ec · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for lan- guage understanding.

Large Language Model for Extracting Complex Contract Information in Industrial Scenes Bert: Pre-training of deep bidirectional transformers for lan- guage understanding

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T19:04:58.989914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72b878e9-bdf7-4ad0-b582-8664c5f018da · outbound

This paper cites and Tarau, P.

Large Language Model for Extracting Complex Contract Information in Industrial Scenes and Tarau, P

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:05:00.698920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a330deb1-288b-476f-afc5-a2056ae8a6b8 · outbound

This paper cites GLM: General Language Model Pretraining with Autoregressive Blank Infilling.

Large Language Model for Extracting Complex Contract Information in Industrial Scenes GLM: General Language Model Pretraining with Autoregressive Blank Infilling

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T19:04:59.150177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cacaa653-27de-449f-bd1c-f12db6043ff5 · outbound

This paper cites GPT-NER: Named Entity Recognition via Large Language Models.

Large Language Model for Extracting Complex Contract Information in Industrial Scenes GPT-NER: Named Entity Recognition via Large Language Models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T19:05:00.222750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.