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Paper Citation Record · LEDGER

Leveraging Large Language Models for NLG Evaluation: Advances and Challenges

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2401.07103.

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

pith.paper-citation-record.v1
2401.07103 v2

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 2026-08-12T06:34:41.77262+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:44:21.151093Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T11:04:07.534092Z

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 9d5e1444-ae91-44aa-b5e1-85dca59da090 · inbound

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation cites this paper.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Leveraging Large Language Models for NLG Evaluation: Advances and Challenges

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-12T14:44:21.151093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:44:21.151093Z digest=sha256:90710ad50002298d34095e817f475440d566f9a11f905590ca402ca46c78128a

Observation 5d0a54fe-95da-4fb9-834a-114a85a676a1 · inbound

ACE-$M^3$: Automatic Capability Evaluator for Multimodal Medical Models cites this paper.

ACE-$M^3$: Automatic Capability Evaluator for Multimodal Medical Models Leveraging Large Language Models for NLG Evaluation: Advances and Challenges

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-11T14:58:45.497919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:58:45.497919Z digest=sha256:7764771ce4bba91fc8322198ab8b0ff543deccab72b70e9670a81be7bb2e8bbd

Observation 9ee80a61-22c9-4344-a398-bc03994daa9c · inbound

Reasoning-Enhanced Self-Training for Long-Form Personalized Text Generation cites this paper.

Reasoning-Enhanced Self-Training for Long-Form Personalized Text Generation Leveraging Large Language Models for NLG Evaluation: Advances and Challenges

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-10T21:42:44.069118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T21:42:44.069118Z digest=sha256:564bf3182b99b2c1c3a945eeb2f2a0cffdded436c3ddeb3f9acaf54f2b973625

Observation 2f8711d2-262b-438a-9335-351a37504cda · inbound

Reference-free Evaluation Metrics for Text Generation: A Survey cites this paper.

Reference-free Evaluation Metrics for Text Generation: A Survey Leveraging Large Language Models for NLG Evaluation: Advances and Challenges

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-10T17:41:29.111448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T17:41:29.111448Z digest=sha256:b3817d443b031325153559046866e04e3ad984848d1b3be12fd093fe6fa43ae6

Observation 4be1c353-9fea-41a9-b0d1-faf73251904c · inbound

RewardAnything: Generalizable Principle-Following Reward Models cites this paper.

RewardAnything: Generalizable Principle-Following Reward Models Leveraging Large Language Models for NLG Evaluation: Advances and Challenges

Reference 89

Resolution
verified exact
local_arxiv, observed 2026-08-07T11:04:07.539350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-07T11:04:07.104919Z digest=sha256:d12a3331a8523b677d46eee336bac85e3689f96451184c8b2fc10d8aeb458f03