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Paper Citation Record · LEDGER

An Empirical Study on Information Extraction using Large Language Models

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2305.14450.

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

pith.paper-citation-record.v1
2305.14450 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:10:18.136677Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:59:58.839259Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8521c910-643f-4483-be77-0d6b37e19988 · inbound

Ontology-grounded Automatic Knowledge Graph Construction by LLM under Wikidata schema cites this paper.

Ontology-grounded Automatic Knowledge Graph Construction by LLM under Wikidata schema An Empirical Study on Information Extraction using Large Language Models

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-10T23:10:18.136677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:10:18.136677Z digest=sha256:6f26382afdf31f0b4877b268cd6714eab0487d6e3a85c209e5ef67f20a6c9243

Observation 6a83e843-4d9a-4b91-98ae-d35d1d8d0ea5 · inbound

STATE ToxiCN: A Benchmark for Span-level Target-Aware Toxicity Extraction in Chinese Hate Speech Detection cites this paper.

STATE ToxiCN: A Benchmark for Span-level Target-Aware Toxicity Extraction in Chinese Hate Speech Detection An Empirical Study on Information Extraction using Large Language Models

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-10T14:21:24.217212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:21:24.217212Z digest=sha256:0921a2a21ddf5c9bffe28f6815fae01540a5d09800e8bb9e497aa82a491bb452

Observation 4e8d47ec-9535-425f-b58a-9757bd6b6ac2 · inbound

MPL: Multiple Programming Languages with Large Language Models for Information Extraction cites this paper.

MPL: Multiple Programming Languages with Large Language Models for Information Extraction An Empirical Study on Information Extraction using Large Language Models

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T15:10:10.398716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:10:10.398716Z digest=sha256:7630c59fc1ae181e8d5a0426b1e721ca7046f5b56bcdcb36c2617a225e383d29

Observation 3ace9672-d524-4fed-b6e9-3a88c4fc332a · inbound

Fine-Grained Chinese Hate Speech Understanding: Span-Level Resources, Coded Term Lexicon, and Enhanced Detection Frameworks cites this paper.

Fine-Grained Chinese Hate Speech Understanding: Span-Level Resources, Coded Term Lexicon, and Enhanced Detection Frameworks An Empirical Study on Information Extraction using Large Language Models

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-06T17:16:30.733727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:16:30.733727Z digest=sha256:cc352e4e3fb62287eacc094313f93f8957a8f9673a24576f635ae213ebd2820a

Observation ac72c3bd-76be-488e-a54b-7e85cb4c2281 · inbound

MExplore: an entity-based visual analytics approach for medical expertise acquisition cites this paper.

MExplore: an entity-based visual analytics approach for medical expertise acquisition An Empirical Study on Information Extraction using Large Language Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-06T16:51:22.087901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:51:22.087901Z digest=sha256:5515940fd438b515c1227a783780b79db83850ed9e0c07b5bc797f61e70ff0e7

Observation 514851a7-6174-46fe-8593-23c2124d13ea · inbound

GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface cites this paper.

GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface An Empirical Study on Information Extraction using Large Language Models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T14:35:54.172614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:35:54.172614Z digest=sha256:5e956577c9390d6fbae5134b4e13f8a9741fe3712e4e24c5393825b6eae068ea

Observation c38022f4-9ebd-4a40-a593-92930eb7b77e · inbound

From scratch to silver: Creating trustworthy training data for patent-SDG classification using Large Language Models cites this paper.

From scratch to silver: Creating trustworthy training data for patent-SDG classification using Large Language Models An Empirical Study on Information Extraction using Large Language Models

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-04T19:27:04.834755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T19:27:04.834755Z digest=sha256:45683d2784e3ab5520ad5531fab7c3d707b6aaefb7a64bc48c2ddf0e38619677

Observation 6f27bfd6-677d-4a13-b9c8-2050ad249750 · inbound

Semantic Reranking at Inference Time for Hard Examples in Rhetorical Role Labeling cites this paper.

Semantic Reranking at Inference Time for Hard Examples in Rhetorical Role Labeling An Empirical Study on Information Extraction using Large Language Models

Reference 109

Resolution
verified exact
arxiv_id, observed 2026-05-20T11:28:14.296142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-05-20T11:27:30.720693Z digest=sha256:e3fb40746047fc39f2c85182bd2ec5918989101fc4c4301fda2689a77cb147de

Observation 4787323a-0957-4bce-92ad-0fffc08a8333 · inbound

DiffuSent: Towards a Unified Diffusion Framework for Aspect-Based Sentiment Analysis cites this paper.

DiffuSent: Towards a Unified Diffusion Framework for Aspect-Based Sentiment Analysis An Empirical Study on Information Extraction using Large Language Models

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-06-28T17:12:24.487385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-06-28T17:10:39.275495Z digest=sha256:36623e79be63ecadbb36feebce22816afba68b9db7efde9378a64447c40f04ae

Observation 3550e3dd-1933-4021-ab9c-2cd1eab658a4 · inbound

Task Decomposition for Efficient Annotation cites this paper.

Task Decomposition for Efficient Annotation An Empirical Study on Information Extraction using Large Language Models

Reference 169

Resolution
verified exact
arxiv_id, observed 2026-07-04T16:59:58.840760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-06-26T00:00:16.588823Z digest=sha256:05545051bc322c87ca2c9fb70bc7e2683832337f5559f20c7f25066fbb07bb89

Observation 198ae72e-2c76-46ca-b176-de47f31cf756 · inbound

JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications cites this paper.

JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications An Empirical Study on Information Extraction using Large Language Models

Reference 84

Resolution
verified exact
arxiv_id, observed 2026-07-01T17:05:51.155030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-06-29T04:11:05.226043Z digest=sha256:affc06221ba9bb3aab9a893453f7f466c7d8708db4cf0f51e0c19926efbc72b5

Observation 8f0a6e79-203c-47db-b458-a60cfdf5b51e · inbound

JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications cites this paper.

JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications An Empirical Study on Information Extraction using Large Language Models

Reference 84

Resolution
verified exact
arxiv_id, observed 2026-06-30T09:44:37.198289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-06-30T09:43:58.385814Z digest=sha256:ee8c5a65bcf74f712083291d32d2db76d06c8cd3b7e6686e15435dd220b1316f

Observation f9517dd4-f80c-4242-8e0f-b6dee550f424 · inbound

Discourse-Aware Policy Analysis with Argumentation: A Hybrid LLM-Symbolic Framework for Disaster Governance cites this paper.

Discourse-Aware Policy Analysis with Argumentation: A Hybrid LLM-Symbolic Framework for Disaster Governance An Empirical Study on Information Extraction using Large Language Models

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-02T05:47:19.872628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T05:47:19.872628Z digest=sha256:81679340bb926ba9912e0580d748815ad623ecada13f418ca6afad8de0d640dc