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Paper Citation Record · LEDGER

A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysis

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2412.02279.

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

pith.paper-citation-record.v1
2412.02279 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:46:21.132167Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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

External citation measurements

3
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 499ef3a0-d9b5-40cb-b3e2-a480f3fae58c · inbound

FCKT: Fine-Grained Cross-Task Knowledge Transfer with Semantic Contrastive Learning for Targeted Sentiment Analysis cites this paper.

FCKT: Fine-Grained Cross-Task Knowledge Transfer with Semantic Contrastive Learning for Targeted Sentiment Analysis A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysis

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-07T13:46:21.132167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:46:21.132167Z digest=sha256:b08038c6d9cebae05fc5bc6d11fa030adb13c566a1a25a0ed2398bf9d4598ffb

Observation e1586dc4-9eca-4555-94dd-7509e9f47095 · inbound

Large Language Models Enhanced by Plug and Play Syntactic Knowledge for Aspect-based Sentiment Analysis cites this paper.

Large Language Models Enhanced by Plug and Play Syntactic Knowledge for Aspect-based Sentiment Analysis A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysis

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-07T00:43:08.091051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:43:08.091051Z digest=sha256:9f0f3bcf6336ba179dcc47603102ee232bf2db42809dae2628158106df9f14f2

Observation da1def06-c551-4d03-aab4-4b662e6f6141 · inbound

Balanced Training Data Augmentation for Aspect-Based Sentiment Analysis cites this paper.

Balanced Training Data Augmentation for Aspect-Based Sentiment Analysis A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysis

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-06T17:59:30.946482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:59:30.946482Z digest=sha256:62a12d3a4f6c72bef4a67cac9f32e1078cb5c47b0b8daf6c7bd8591970ee1ebb

Observation 32d07096-abe4-47e2-9ffa-2348db53a8a9 · inbound

LASQ: A Low-resource Aspect-based Sentiment Quadruple Extraction Dataset cites this paper.

LASQ: A Low-resource Aspect-based Sentiment Quadruple Extraction Dataset A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysis

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-05-11T08:40:58.973056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T16:34:01.296047Z digest=sha256:8a1e893a729dee9a8613f33a1466c758abff58bd7739c3b3d2656aebe53e31a4

Observation b17fe7a7-2618-4cde-b459-c1100c91df63 · inbound

Annotation Quality in Aspect-Based Sentiment Analysis: A Case Study Comparing Experts, Students, Crowdworkers, and Large Language Model cites this paper.

Annotation Quality in Aspect-Based Sentiment Analysis: A Case Study Comparing Experts, Students, Crowdworkers, and Large Language Model A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysis

Reference 91

Resolution
verified exact
arxiv_id, observed 2026-05-09T05:20:11.716528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-07T04:21:26.598193Z digest=sha256:a6af1d226cc3589d119ecdd9218098f00f539ee36c497d701b0ad86753378914