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Paper Citation Record · LEDGER

AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2208.01448.

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

pith.paper-citation-record.v1
2208.01448 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T12:12:58.286398Z

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

38
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 40905c47-d75c-4701-b840-ff46e5c144d5 · inbound

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model cites this paper.

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

Reference 140

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T00:51:11.339125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-12T00:51:10.919818Z digest=sha256:667c3947b11163fc703d3068f607149865ba3ac195f8a90a59eebd8cca178024

Observation f67d4730-51dd-4ba2-8e63-ac71d4c18713 · inbound

BloombergGPT: A Large Language Model for Finance cites this paper.

BloombergGPT: A Large Language Model for Finance AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

Reference 106

Resolution
verified exact
arxiv_id, observed 2026-05-13T23:19:46.795412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-13T23:19:46.231145Z digest=sha256:95523d5043dab836aaa39951fdf9a637ebbc3145300d2b27200e077a1be7b2dd

Observation 796de4c1-4fd7-4555-8822-2e86cb84281d · inbound

A Survey of Large Language Models cites this paper.

A Survey of Large Language Models AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

Reference 117

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T22:46:40.163020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T22:46:39.268353Z digest=sha256:8961387e52117f6457cb3469681e42ad13079b064ffbd9ae532aecf9d0e437ce

Observation aad98358-2443-4e93-a983-ea783ecbd641 · inbound

CodeT5+: Open Code Large Language Models for Code Understanding and Generation cites this paper.

CodeT5+: Open Code Large Language Models for Code Understanding and Generation AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-19T05:26:57.576621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-19T05:26:57.440959Z digest=sha256:6b0150ae8339c7e83c22a2358e80b8d88a89dbaa21ee0c94fae8a52ff980c06e

Observation 0a2f33a7-715f-4f9a-a7e4-2615c31f0acd · inbound

Scaling Data-Constrained Language Models cites this paper.

Scaling Data-Constrained Language Models AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

Reference 108

Resolution
verified exact
arxiv_id, observed 2026-05-18T01:35:21.370132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T01:35:21.150772Z digest=sha256:443f1307112cf074a51980de4b4ed1e7cbc9da8c31389c3cbc105296d7904a70

Observation d89798c6-4cad-4324-b204-c39978780847 · inbound

A Comprehensive Overview of Large Language Models cites this paper.

A Comprehensive Overview of Large Language Models AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

Reference 122

Resolution
verified exact
arxiv_id, observed 2026-05-19T20:28:39.411189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-19T20:28:38.900026Z digest=sha256:280bfac0fd8a4e08a5f0f54d5b638e4365cd0bd9cc111b6fa7fe5ca56646d544

Observation f573acf3-780b-4c6a-9614-99d1843d5310 · inbound

Large Language Models: A Survey cites this paper.

Large Language Models: A Survey AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

Reference 89

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T15:22:55.271402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-11T15:22:54.023279Z digest=sha256:6b7f35020232b9005f43c636bdb2cc7047706ceb809dcdd55c3883783d1e4ca4

Observation 60b53a27-2607-4ed9-ba7e-eea05b539578 · inbound

Optimizing Datasets for Code Summarization: Is Code-Comment Coherence Enough? cites this paper.

Optimizing Datasets for Code Summarization: Is Code-Comment Coherence Enough? AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-08T12:12:58.286398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T12:12:58.286398Z digest=sha256:2f698a6cb6ff30ddba80a24fc9e554f6ff42d878e8afee546d25e67105aaf714

Observation 92347395-5567-476b-b319-f3dd66da57aa · 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? AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-06T13:37:54.767217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:37:54.767217Z digest=sha256:8f7229fea030db1604a02487c52abe83beb5b57cac25d794a37a03d2a35fcdf4

Observation 868c8b96-305e-47d0-9f9e-fbf3884e7b6c · inbound

Towards Reliable Generative AI-Driven Scaffolding: Reducing Hallucinations and Enhancing Quality in Self-Regulated Learning Support cites this paper.

Towards Reliable Generative AI-Driven Scaffolding: Reducing Hallucinations and Enhancing Quality in Self-Regulated Learning Support AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

Reference 79

Resolution
unresolved
no resolver link, observed 2026-08-05T23:04:57.030098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T23:04:57.030098Z digest=sha256:9ed627377c8ac52828f63b01a3da27f1809d5dd4b44e1551a6da4b7b4dc14f9e

Observation 5b456e16-40dc-4cb9-a1a5-84bf5b72166d · inbound

Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned cites this paper.

Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

Reference 1986

Resolution
unresolved
no resolver link, observed 2026-08-04T22:55:28.575322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:55:28.575322Z digest=sha256:f9f43165e651b4ba83c59906e880bdde90b289fade57e4434e8454f1342d5fde

Observation 0c5fb8b0-58c2-4ee7-a7da-bbd9a2eef542 · inbound

Not All Tokens Matter: Data-Centric Optimization for Efficient Code Summarization cites this paper.

Not All Tokens Matter: Data-Centric Optimization for Efficient Code Summarization AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-03T07:34:21.990897Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T07:34:21.990897Z digest=sha256:20950f01b92d105f65b9f89cb9af38e6fcb3ebd53339619e6612a5aa4db76c5a

Observation 7f9922ab-7725-4e7a-adbe-fb83e30afd41 · inbound

A Meta Reinforcement Learning Approach to Goals-Based Wealth Management cites this paper.

A Meta Reinforcement Learning Approach to Goals-Based Wealth Management AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

Reference 244

Resolution
metadata mismatch
arxiv_id, observed 2026-05-08T18:44:01.726123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-08T18:42:50.962120Z digest=sha256:85d249f73799627b89ead77ee0100665d0f2a328f94dff74ceb91b378345dfad