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Paper Citation Record · LEDGER

Using Captum to Explain Generative Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2312.05491.

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

pith.paper-citation-record.v1
2312.05491 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T20:29:43.700229Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T10:44:37.834723Z

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 eaf3df3a-cb8f-49ce-934a-604dead4f4ee · inbound

LLM attribution analysis across different fine-tuning strategies and model scales for automated code compliance cites this paper.

LLM attribution analysis across different fine-tuning strategies and model scales for automated code compliance Using Captum to Explain Generative Language Models

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-10T10:44:37.837130Z

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=2026-05-10T10:40:59.158671Z digest=sha256:0eec7ed78a7bad05d7006a4bbede8f7d69bfceb009dc45eba7f1057798755d58

Observation e9fbb7d1-7f6d-4bb9-a69c-bfe3fb522eac · inbound

LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation cites this paper.

LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation Using Captum to Explain Generative Language Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-07-11T20:29:43.700229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T20:29:43.700229Z digest=sha256:c5b30500f9106647aed02014ff7c9c124b06be70f71e0960598be52fb07ace43