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Paper Citation Record · LEDGER

LexGPT 0.1: pre-trained GPT-J models with Pile of Law

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.05431.

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

pith.paper-citation-record.v1
2306.05431 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 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:58:04.150083Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T23:23:28.981306Z

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 10cbf2bb-0c7a-41ba-aef3-2d826ce704ff · inbound

Legal Evalutions and Challenges of Large Language Models cites this paper.

Legal Evalutions and Challenges of Large Language Models LexGPT 0.1: pre-trained GPT-J models with Pile of Law

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-12T19:58:04.150083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T19:58:04.150083Z digest=sha256:5b1d5dfbb33bd62e6d6505e5b86e69b9a3dee578e84aba95f7ecfabeebfbcdea

Observation b304a58e-f104-4c71-a6f4-f106b4ddbc1c · inbound

A Comprehensive Framework for Reliable Legal AI: Combining Specialized Expert Systems and Adaptive Refinement cites this paper.

A Comprehensive Framework for Reliable Legal AI: Combining Specialized Expert Systems and Adaptive Refinement LexGPT 0.1: pre-trained GPT-J models with Pile of Law

Reference 17

Resolution
verified exact
local_arxiv, observed 2026-08-10T23:23:29.010863Z

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=2026-08-10T23:23:28.232195Z digest=sha256:69614c43732aab1fe20531d0d32329b420d67ac95a6de871df9acc75c5cc1bb8