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Paper Citation Record · LEDGER

Can Large Language Models perform Relation-based Argument Mining?

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2402.11243.

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

pith.paper-citation-record.v1
2402.11243 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:59:29.104990Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T13:06:58.950888Z

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 c401029d-f25a-40c5-a6bc-3ccfeffe60db · inbound

LLMs for Argument Mining: Detection, Extraction, and Relationship Classification of pre-defined Arguments in Online Comments cites this paper.

LLMs for Argument Mining: Detection, Extraction, and Relationship Classification of pre-defined Arguments in Online Comments Can Large Language Models perform Relation-based Argument Mining?

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-07T12:59:29.104990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:59:29.104990Z digest=sha256:08a96045ce94df921cdc36717771f0e32b7eec4e0e8315047dec3225a756ee98

Observation 35236ec7-7e6a-42ce-8ef0-b73d4c4e9fa4 · inbound

Towards Robust Argumentative Essay Understanding via TIDE: An Interactive Framework with Trial and Debate cites this paper.

Towards Robust Argumentative Essay Understanding via TIDE: An Interactive Framework with Trial and Debate Can Large Language Models perform Relation-based Argument Mining?

Reference 75

Resolution
verified exact
arxiv_id, observed 2026-05-20T13:48:19.839130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-20T13:45:13.963439Z digest=sha256:4dc29aab0024cb543c7e6643ab9e866c47a96d7a3fe944b07b8a6088392fa692

Observation 0abb9675-0c9d-49ba-98de-911349f8a47e · inbound

CAF-Gen: A Multi-Agent System for Enriching Argumentation Structures cites this paper.

CAF-Gen: A Multi-Agent System for Enriching Argumentation Structures Can Large Language Models perform Relation-based Argument Mining?

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-07-02T13:06:58.952383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-28T01:37:06.028099Z digest=sha256:a0e527f9d77077e94aade160496d632cb16b188d4f3a694e979efdf9518c9cb4