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Paper Citation Record · LEDGER

Building Efficient Universal Classifiers with Natural Language Inference

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2312.17543.

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

pith.paper-citation-record.v1
2312.17543 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:35:54.202665Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

11
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d3b2ad4d-476b-4c80-8271-06a02f4ec6a3 · inbound

GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface cites this paper.

GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface Building Efficient Universal Classifiers with Natural Language Inference

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-06T14:35:54.202665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:35:54.202665Z digest=sha256:98112455a67d67429713b6f23fa1464d57c61f080a99a7374478928258381658

Observation f039a53d-3fc9-49a6-8bf5-e03114e1ffe0 · inbound

A Tale of LLMs and Induced Small Proxies: Scalable Small Language Models for Knowledge Mining cites this paper.

A Tale of LLMs and Induced Small Proxies: Scalable Small Language Models for Knowledge Mining Building Efficient Universal Classifiers with Natural Language Inference

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-04T12:59:00.357170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T12:59:00.357170Z digest=sha256:895c57c39df1d00a0859cc6ae5cd4a9a9d96f97670e658f181b8650fe09e4302

Observation 5dfa93a9-2996-45cf-9132-9b134cb63b01 · inbound

Ideological discrepancy between publishers and news content is linked with audience engagement and consensus on Facebook cites this paper.

Ideological discrepancy between publishers and news content is linked with audience engagement and consensus on Facebook Building Efficient Universal Classifiers with Natural Language Inference

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-05-10T15:55:35.002402Z

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=2026-05-10T15:53:40.024017Z digest=sha256:da57f09d0ddd88bf21268edf995a6db1decd2027ee45459198d4f607169bc81b

Observation f867a682-9ded-4997-9678-7f6266a8cf69 · inbound

The Consistency Illusion: How Multi-Agent Debate Hides Reasoning Misalignment cites this paper.

The Consistency Illusion: How Multi-Agent Debate Hides Reasoning Misalignment Building Efficient Universal Classifiers with Natural Language Inference

Reference 24

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T23:47:27.590359Z

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=2026-06-27T17:56:09.877800Z digest=sha256:4388d163e72dc6a8583bed67d47698425fcd9778628d19dca7a84ca87bb4e1e0

Observation c534f402-e0c2-4bf4-9f18-cf48e071f136 · inbound

Pareto-Guided Teacher Alignment for Fair Personalized Text Generation cites this paper.

Pareto-Guided Teacher Alignment for Fair Personalized Text Generation Building Efficient Universal Classifiers with Natural Language Inference

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-06-27T16:41:03.662875Z

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=2026-06-27T16:06:58.653836Z digest=sha256:a97179aef3cc427c9e3bfe94d38618a682898ac418e93a6146a17788349586e6

Observation ffe8ef5b-a7b7-410c-a934-1eacaab9295c · inbound

CREDENCE: Claim Reduction for Decomposition & Enhanced Credibility -- Semantic Metrics and Convergence Analysis cites this paper.

CREDENCE: Claim Reduction for Decomposition & Enhanced Credibility -- Semantic Metrics and Convergence Analysis Building Efficient Universal Classifiers with Natural Language Inference

Reference 19

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T03:29:31.361584Z

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=2026-06-26T17:56:54.438723Z digest=sha256:b1b247970faa30500f5503b9581b091875d52a90eed582e3e12c30f1eecc0d0b

Observation ac8b724f-431b-4435-8aeb-63cdad1cec5f · inbound

Task Decomposition for Efficient Annotation cites this paper.

Task Decomposition for Efficient Annotation Building Efficient Universal Classifiers with Natural Language Inference

Reference 72

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T16:59:58.922859Z

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=2026-06-26T00:00:16.588823Z digest=sha256:e76605f97517075a1bd9db7e04ed60db7d9c65daba4b7907644fb950b3461968

Observation ee4eacc1-ff29-4d5d-b3e7-911bb7bb4e25 · inbound

Population-Level Profiling of DSM-5 Depressive Symptoms Among Self-Reported ADHD and ASD Users on Twitter: An Exploratory Study Using Advanced NLP and Statistical Analysis cites this paper.

Population-Level Profiling of DSM-5 Depressive Symptoms Among Self-Reported ADHD and ASD Users on Twitter: An Exploratory Study Using Advanced NLP and Statistical Analysis Building Efficient Universal Classifiers with Natural Language Inference

Reference 23

Resolution
malformed identifier
no resolver link, observed 2026-07-11T04:43:58.910824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T04:43:58.910824Z digest=sha256:5b3587c22b0150bc5e7cb793564cf924df6e8d2919c8f8000e0f99b530a7228e