Paper Citation Record · LEDGER

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI

As of 21 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2412.09263.

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

pith.paper-citation-record.v1
2412.09263 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:11:02.360439Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T13:37:54.934359Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

34 of 34 outbound references displayed

verified exact1
verified fuzzy29
unresolved4
parse uncertain0
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External citation measurements

0
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

Observation c3362e41-ea9d-4660-8b9d-47661238666c · outbound

This paper cites A large annotated corpus for learning natural language inference,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI A large annotated corpus for learning natural language inference,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.985756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e859b954-e64f-42ab-969b-131b8afd1b0e · outbound

This paper cites A decomposable attention model for natural language inference,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI A decomposable attention model for natural language inference,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.969552Z

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Observation f9e522f1-6741-4a7c-9d7c-077ee6e1c0c7 · outbound

This paper cites Enhanced lstm for natural language inference,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Enhanced lstm for natural language inference,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.953895Z

Source-reported events for the cited work

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Observation de836a0f-e1e6-4321-8efd-c24290ec72cc · outbound

This paper cites Improving language understanding by generative pre-training,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Improving language understanding by generative pre-training,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.937895Z

Source-reported events for the cited work

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Observation f6c3f9eb-9837-4c7c-8298-3d0c54b46588 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 5

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Source-reported events for the cited work

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Observation d1bcf9c1-ab9b-411a-97bd-dd686767ce79 · outbound

This paper cites Xlnet: General- ized autoregressive pretraining for language understanding,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Xlnet: General- ized autoregressive pretraining for language understanding,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.911632Z

Source-reported events for the cited work

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Observation 3f3828b2-5f33-46f0-bbb2-d0ab5ad84316 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T17:11:02.228497Z

Source-reported events for the cited work

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Observation b574fda0-445c-4e26-82ed-62311d1e348e · outbound

This paper cites Albert: A lite bert for self-supervised learning of language representations,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Albert: A lite bert for self-supervised learning of language representations,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.896105Z

Source-reported events for the cited work

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Observation ab741dd5-9ff5-4bc8-801a-a97f02c7cd13 · outbound

This paper cites The Convexity and Concavity of Envelopes of the Minimum-Relative-Entropy Region for the DSBS.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI The Convexity and Concavity of Envelopes of the Minimum-Relative-Entropy Region for the DSBS

Reference 9

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Source-reported events for the cited work

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Observation 749b8d35-7ef8-4903-9fb7-921540c3cbb3 · outbound

This paper cites Entailment as few-shot learner,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Entailment as few-shot learner,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.880142Z

Source-reported events for the cited work

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Observation db737871-18a3-4f5a-aa2e-e59560182cfd · outbound

This paper cites Papers with Code - SNLI Benchmark (Natural Language Inference) — paperswithcode.com.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Papers with Code - SNLI Benchmark (Natural Language Inference) — paperswithcode.com

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.864886Z

Source-reported events for the cited work

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Observation 4ae4b648-606b-437b-aed1-897cd2b083ee · outbound

This paper cites An extended model of natural logic,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI An extended model of natural logic,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.848447Z

Source-reported events for the cited work

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Observation 479ac25a-b80a-4e5b-88d0-b1c4a2881874 · outbound

This paper cites Natural language inference,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Natural language inference,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.831902Z

Source-reported events for the cited work

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Observation 83d902cd-999b-415f-ade6-94c131c6bd2a · outbound

This paper cites Deep contextualized word representations,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Deep contextualized word representations,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.815699Z

Source-reported events for the cited work

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Observation e0d73e51-661e-491f-ad63-ead84766aca8 · outbound

This paper cites Llms will always hallucinate, and we need to live with this,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Llms will always hallucinate, and we need to live with this,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.800711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e8db2c24-3380-4c76-82c4-7c324335f226 · outbound

This paper cites A broad-coverage challenge corpus for sentence understanding through inference,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI A broad-coverage challenge corpus for sentence understanding through inference,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.784528Z

Source-reported events for the cited work

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Observation 99245431-8aeb-459b-a8fe-074b8c0e9c2a · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.767277Z

Source-reported events for the cited work

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Observation efe4a68a-ff10-4b33-9f5d-2759f7c15099 · outbound

This paper cites Large dual encoders are generalizable retrievers,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Large dual encoders are generalizable retrievers,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.751376Z

Source-reported events for the cited work

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Observation 1f237836-d47c-4908-bf85-0a0e696ac808 · outbound

This paper cites The pascal recognising textual entailment challenge,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI The pascal recognising textual entailment challenge,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.735785Z

Source-reported events for the cited work

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Observation 32fbac64-e47c-422b-87a7-30a4b3943e57 · outbound

This paper cites Knowledgeable reader: Enhancing cloze-style reading comprehension with external commonsense knowledge,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Knowledgeable reader: Enhancing cloze-style reading comprehension with external commonsense knowledge,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.719550Z

Source-reported events for the cited work

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Observation ee12c8cc-3111-4b88-b496-85f229e711eb · outbound

This paper cites Xnli: Evaluating cross-lingual sentence representations,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Xnli: Evaluating cross-lingual sentence representations,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.703903Z

Source-reported events for the cited work

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Observation 50f8ad58-6d3b-4a1b-8664-7d0b44558d3d · outbound

This paper cites Attention is all you need,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Attention is all you need,

Reference 22

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Source-reported events for the cited work

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Observation fcfd8e92-097a-433b-8ae6-df5a3cac39b7 · outbound

This paper cites High-precision medical speech recognition through synthetic data and semantic correction: United-medasr,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI High-precision medical speech recognition through synthetic data and semantic correction: United-medasr,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.678397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ddf00858-acaa-4171-9928-ecc0a3b5c45b · outbound

This paper cites Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.662919Z

Source-reported events for the cited work

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Observation 945e4c18-fcf6-4061-a079-4c9a90f4660c · outbound

This paper cites Smart: Robust and efficient fine- tuning for pre-trained natural language models through principled regularized optimization,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Smart: Robust and efficient fine- tuning for pre-trained natural language models through principled regularized optimization,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.646062Z

Source-reported events for the cited work

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Observation f9e001c5-b50c-4248-a181-5422d1f0d53a · outbound

This paper cites Palm 2 technical report,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Palm 2 technical report,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.630567Z

Source-reported events for the cited work

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Observation 28e69e27-d56f-40b2-98a7-7ba607667fd4 · outbound

This paper cites Claude 3 model card,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Claude 3 model card,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.615450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 11ce0e6e-c612-41e7-8367-d6febd490e1d · outbound

This paper cites Gpt-4 technical report,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Gpt-4 technical report,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.599349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 37785f1c-5ba9-41cb-9f94-2a0ec59ae92b · outbound

This paper cites Scitail: A textual entailment dataset from science question answering,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Scitail: A textual entailment dataset from science question answering,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.581490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 67a28d43-cd13-4103-98c5-fe86b68b2973 · outbound

This paper cites Multi-task deep neural networks for natural language un- derstanding,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Multi-task deep neural networks for natural language un- derstanding,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.564962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation df94679d-53c7-410e-8915-4578e1fe08ea · outbound

This paper cites Anli: A new benchmark for natural language understanding,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Anli: A new benchmark for natural language understanding,

Reference 31

Resolution: verified exact; raw_fallback, observed 2026-08-11T17:11:17.464500Z

Source-reported events for the cited work

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Observation c1b1ed91-b2f6-4dc0-9c9e-ceb90a826333 · outbound

This paper cites e-snli: Natural language inference with natural language explanations,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI e-snli: Natural language inference with natural language explanations,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.549331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ff5fe36f-63df-4a93-8873-9721a1c47270 · outbound

This paper cites Scaling instruction-finetuned language models,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI Scaling instruction-finetuned language models,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.534048Z

Source-reported events for the cited work

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Observation 43419b01-a914-46b3-8fc2-04fbb1196363 · outbound

This paper cites e-snli: Natural language inference with natural language explanations,.

First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI e-snli: Natural language inference with natural language explanations,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:11:17.517682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 3ff88a52-1719-405a-80ac-5075153207c8 · inbound

Survey of NLU Benchmarks Diagnosing Linguistic Phenomena: Why not Standardize Diagnostics Benchmarks? cites this paper.

Survey of NLU Benchmarks Diagnosing Linguistic Phenomena: Why not Standardize Diagnostics Benchmarks? First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-06T13:38:04.472952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.