Paper Citation Record · LEDGER

Text Classification via Large Language Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2305.08377.

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

pith.paper-citation-record.v1
2305.08377 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:52:30.375712Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T15:43:32.875577Z

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 6875d0ee-2f5a-41e4-a4c4-65e207abcc1f · inbound

SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use cites this paper.

SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Text Classification via Large Language Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:30.375712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45b2d580-965c-44f0-834c-c603bc2f61b0 · inbound

Semantic Source Code Segmentation using Small and Large Language Models cites this paper.

Semantic Source Code Segmentation using Small and Large Language Models Text Classification via Large Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T18:14:06.368007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c22b0ff-da08-49cf-ba32-4440e293b21b · inbound

ClaHF: A Human Feedback-inspired Reinforcement Learning Framework for Improving Classification Tasks cites this paper.

ClaHF: A Human Feedback-inspired Reinforcement Learning Framework for Improving Classification Tasks Text Classification via Large Language Models

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T15:13:24.951372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e12a4e64-2f08-410c-aa7c-dda84ae53760 · inbound

Mask-to-Correct$^+$: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction cites this paper.

Mask-to-Correct$^+$: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction Text Classification via Large Language Models

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-21T01:03:52.806058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 11e17a00-a6b5-4b35-8a83-e12031cf98b9 · inbound

Structuring Human-AI Productive Interdependence by Strategic Level of Automation Selection for Qualitative Inquiry cites this paper.

Structuring Human-AI Productive Interdependence by Strategic Level of Automation Selection for Qualitative Inquiry Text Classification via Large Language Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-06-29T15:43:32.877162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8498d679-45c7-41b5-9a36-ac4156718ed8 · inbound

Large Language Models for Token-Efficient and Semantic-Preserving Opinion Summarization cites this paper.

Large Language Models for Token-Efficient and Semantic-Preserving Opinion Summarization Text Classification via Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-14T08:58:37.267426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.