Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2303.00293.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T05:42:30.836647Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-12T02:16:16.113882Z
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 ea6896dc-6d82-4b44-a05c-8db433b4016f · inbound
Evaluating and Improving Robustness in Large Language Models: A Survey and Future Directions cites this paper.
Evaluating and Improving Robustness in Large Language Models: A Survey and Future Directions How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:42:30.836647Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6b60595c-900b-4df0-a624-2e2ec45e33ac · inbound
Flipping Knowledge Distillation: Leveraging Small Models' Expertise to Enhance LLMs in Text Matching cites this paper.
Flipping Knowledge Distillation: Leveraging Small Models' Expertise to Enhance LLMs in Text Matching How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:29:06.003724Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4d957117-8bc1-4a78-b208-573b405e15cf · inbound
Paraphrase-Induced Output-Mode Collapse: When LLMs Break Character Under Semantically Equivalent Inputs cites this paper.
Paraphrase-Induced Output-Mode Collapse: When LLMs Break Character Under Semantically Equivalent Inputs How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T18:16:07.386512Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 6d04a4cf-c2a9-4d7b-8816-02af3e44117b · inbound
Paraphrase-Induced Output-Mode Collapse: When LLMs Break Character Under Semantically Equivalent Inputs cites this paper.
Paraphrase-Induced Output-Mode Collapse: When LLMs Break Character Under Semantically Equivalent Inputs How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T02:16:16.115641Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.