Paper Citation Record · LEDGER

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models?

As of 17 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 2 inbound Pith citation observations for arXiv:2411.18021.

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

pith.paper-citation-record.v1
2411.18021 v1

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:38:43.017198Z

measured 72 of 72 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T01:33:19.872233Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

70 of 70 outbound references displayed

verified exact1
verified fuzzy19
unresolved50
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1822456b-3d72-4428-9417-5f530f173ceb · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 1

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Observation eb9d7fd5-0388-440c-9d6b-194a71de7bc0 · outbound

This paper cites XLNet: Generalized Autoregressive Pretraining for Language Understanding.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? XLNet: Generalized Autoregressive Pretraining for Language Understanding

Reference 2

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Observation be79b7d9-f8df-4d36-bf8b-edc4dbf7abd8 · outbound

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

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 3

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Observation 333908a3-a03c-40c8-b0e9-5d4422190e91 · outbound

This paper cites StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding

Reference 4

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Observation 8b6f4e40-009e-41aa-ad48-5a01947187e1 · outbound

This paper cites ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T11:38:41.739419Z

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Observation d94e09c9-eb64-4527-b6f2-75f14c36abe2 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T11:38:41.743708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2e32b0f-5f66-4445-ba46-4726fb1feb5c · outbound

This paper cites BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T11:38:41.815655Z

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Observation dc7ab865-359e-4c39-b475-74b6dd8e9975 · outbound

This paper cites ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T11:38:41.905964Z

Source-reported events for the cited work

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Observation 67bf597c-95da-4e38-be05-eb90ea6dce1b · outbound

This paper cites Funnel-transformer: Filtering out sequential redundancy for efficient language processing,.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Funnel-transformer: Filtering out sequential redundancy for efficient language processing,

Reference 9

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Source-reported events for the cited work

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Observation a26d045c-eeda-47d7-ac3f-77c1455ca995 · outbound

This paper cites SpanBERT: Improving Pre-training by Representing and Predicting Spans,.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? SpanBERT: Improving Pre-training by Representing and Predicting Spans,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:38:44.995407Z

Source-reported events for the cited work

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Observation 1736c401-162f-48ee-bd35-7d8406d50a8d · outbound

This paper cites ConvBERT: Improving BERT with span-based dynamic convolution,.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? ConvBERT: Improving BERT with span-based dynamic convolution,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:38:44.981610Z

Source-reported events for the cited work

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Observation a500246d-bb67-424c-a27a-05909ddbd9af · outbound

This paper cites Mpnet: Masked and permuted pre-training for language understanding,.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Mpnet: Masked and permuted pre-training for language understanding,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:38:44.948100Z

Source-reported events for the cited work

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Observation f97f6c8e-432f-4307-a212-a203623635bf · outbound

This paper cites LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T11:38:41.926736Z

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Observation 15c3e0ac-e24c-4242-9563-2f04869bcf35 · outbound

This paper cites Unilmv2: Pseudo-masked language models for unified language model pre-training,.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Unilmv2: Pseudo-masked language models for unified language model pre-training,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:38:44.934218Z

Source-reported events for the cited work

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Observation ac8ebfc7-4986-4c44-90b2-59b843681f39 · outbound

This paper cites DeBERTa: Decoding-enhanced BERT with Disentangled Attention.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? DeBERTa: Decoding-enhanced BERT with Disentangled Attention

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T11:38:41.994508Z

Source-reported events for the cited work

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Observation 7fc90784-e4cc-4a16-9585-96746be31ce4 · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T11:38:42.123990Z

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Observation 132aac2b-422d-4c58-9b66-2f3ef78e3780 · outbound

This paper cites SQuAD: 100,000+ Questions for Machine Comprehension of Text.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? SQuAD: 100,000+ Questions for Machine Comprehension of Text

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T11:38:42.175956Z

Source-reported events for the cited work

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Observation 87adb590-4eec-442a-a8dd-3c574ae72236 · outbound

This paper cites Know What You Don't Know: Unanswerable Questions for SQuAD.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Know What You Don't Know: Unanswerable Questions for SQuAD

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T11:38:42.181443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2eefadff-e079-4f97-9a15-0a08510731a6 · outbound

This paper cites SciBERT: A Pretrained Language Model for Scientific Text.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? SciBERT: A Pretrained Language Model for Scientific Text

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T11:38:42.185962Z

Source-reported events for the cited work

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Observation 66cd5e2b-5b03-4c62-bda4-318995d035da · outbound

This paper cites Publicly Available Clinical BERT Embeddings.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Publicly Available Clinical BERT Embeddings

Reference 20

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Observation ddb62400-6eda-4720-b839-3e64c42e38a1 · outbound

This paper cites BioBERT: a pre-trained biomedical language representation model for biomedical text mining,.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? BioBERT: a pre-trained biomedical language representation model for biomedical text mining,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:38:44.875596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5de9a60d-cad8-42fa-811f-b379b4a2d40a · outbound

This paper cites BERTweet: A pre-trained language model for English Tweets.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? BERTweet: A pre-trained language model for English Tweets

Reference 22

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Source-reported events for the cited work

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Observation 485c7017-ee68-4ea1-9a08-9f9839a6eacb · outbound

This paper cites CamemBERT: a Tasty French Language Model.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? CamemBERT: a Tasty French Language Model

Reference 23

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Source-reported events for the cited work

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Observation 7e4ed634-daa5-48fa-9afa-78ca47cc3e73 · outbound

This paper cites FlauBERT: Unsupervised Language Model Pre-training for French.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? FlauBERT: Unsupervised Language Model Pre-training for French

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T11:38:42.205519Z

Source-reported events for the cited work

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Observation d3dc28d3-ab35-4c67-b461-3b7339ae648e · outbound

This paper cites BERTje: A Dutch BERT Model.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? BERTje: A Dutch BERT Model

Reference 25

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Source-reported events for the cited work

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Observation 38004a54-76b0-4818-bed9-98ee48d04cc9 · outbound

This paper cites AraBERT: Transformer-based Model for Arabic Language Understanding.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? AraBERT: Transformer-based Model for Arabic Language Understanding

Reference 26

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Source-reported events for the cited work

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Observation a56cfb56-f644-42ef-8df8-c1010d1c0335 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? On the Opportunities and Risks of Foundation Models

Reference 27

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This paper cites Attention is all you need,.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Attention is all you need,

Reference 28

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Observation 4f51fb3d-a13f-46fc-a6b9-a6a95aa60909 · outbound

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

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Improving language understanding by generative pre-training,

Reference 29

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Observation 210aa1a6-e30d-4f94-8f04-c3b359ddbb45 · outbound

This paper cites Language is primarily a tool for communication rather than thought,.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Language is primarily a tool for communication rather than thought,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:38:44.771113Z

Source-reported events for the cited work

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Observation b7148ccb-3275-427d-90c4-d22a47ca07e3 · outbound

This paper cites Detecting formal thought disor- der by deep contextualized word representations,.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Detecting formal thought disor- der by deep contextualized word representations,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:38:44.759347Z

Source-reported events for the cited work

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Observation 8e2e027f-836c-4c14-aaab-71617ffff419 · outbound

This paper cites Long short-term memory,.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Long short-term memory,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:38:44.747377Z

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Observation 0ab1233a-c179-4667-aaa5-b04902845049 · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Efficient Estimation of Word Representations in Vector Space

Reference 33

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This paper cites Glove: Global vectors for word representation,.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Glove: Global vectors for word representation,

Reference 34

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Observation 18d2b7b3-4bd9-491a-a3da-1f78f99590c9 · outbound

This paper cites Language models are unsupervised multitask learners,.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Language models are unsupervised multitask learners,

Reference 35

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Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Language models are few-shot learners,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:38:44.640585Z

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Observation ddf59bd5-0aba-44bf-94ee-2ddcddead9b7 · outbound

This paper cites Explaining Predictive Uncertainty by Looking Back at Model Explanations.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Explaining Predictive Uncertainty by Looking Back at Model Explanations

Reference 37

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Observation c79ca51d-e92f-4675-ac15-ec26c081dfe1 · outbound

This paper cites BERT Rediscovers the Classical NLP Pipeline.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? BERT Rediscovers the Classical NLP Pipeline

Reference 38

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Observation ad861e9e-2655-4fd5-897f-ea6b4927cc75 · outbound

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Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? TinyBERT: Distilling BERT for Natural Language Understanding

Reference 39

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Observation 2c992f5f-ff89-41aa-975c-3e95047ce132 · outbound

This paper cites ERNIE: Enhanced Language Representation with Informative Entities.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? ERNIE: Enhanced Language Representation with Informative Entities

Reference 40

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Source-reported events for the cited work

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Observation 2acaf411-ea9d-4db1-833e-f10d3982ce4d · outbound

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

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:38:44.628872Z

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Observation ee575e78-3192-46aa-ba14-40ed7b006225 · outbound

This paper cites Linguistic Knowledge and Transferability of Contextual Representations.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Linguistic Knowledge and Transferability of Contextual Representations

Reference 42

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This paper cites Recursive deep models for semantic compositionality over a sentiment treebank,.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Recursive deep models for semantic compositionality over a sentiment treebank,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:38:44.617796Z

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Observation bfd2ed8d-1a5a-4008-8a62-e18e97995875 · outbound

This paper cites Automatically constructing a corpus of sentential paraphrases,.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Automatically constructing a corpus of sentential paraphrases,

Reference 44

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This paper cites SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation

Reference 45

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This paper cites A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

Reference 46

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This paper cites The PASCAL recognising tex- tual entailment challenge,.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? The PASCAL recognising tex- tual entailment challenge,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:38:44.401328Z

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Observation b943e0d4-f61b-4b88-aaf9-73267d1be858 · outbound

This paper cites The Winograd schema challenge,.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? The Winograd schema challenge,

Reference 48

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Observation 054a32ab-4114-470f-944b-903756bfabe8 · outbound

This paper cites CycleTrans: A Transformer- Based Clinical Foundation Model for Safer Prescription,.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? CycleTrans: A Transformer- Based Clinical Foundation Model for Safer Prescription,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:38:44.251202Z

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Observation a0d1a259-83a2-4533-9596-90b02e32fa84 · outbound

This paper cites EchoMamba4Rec: Harmonizing Bidirectional State Space Models with Spectral Filtering for Advanced Sequential Recommendation.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? EchoMamba4Rec: Harmonizing Bidirectional State Space Models with Spectral Filtering for Advanced Sequential Recommendation

Reference 50

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This paper cites Reformer: The Efficient Transformer.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Reformer: The Efficient Transformer

Reference 51

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Observation 4faa7f99-a45b-487f-993d-571166d3da3a · outbound

This paper cites Zhang, Y.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Zhang, Y

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:38:44.163714Z

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Observation 6e0b986e-fee9-4f3b-9474-617d642df1ea · outbound

This paper cites Language-agnostic BERT Sentence Embedding.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Language-agnostic BERT Sentence Embedding

Reference 53

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Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Unresolved cited work

Reference 54

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This paper cites DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations

Reference 55

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Observation db710626-b1a6-4e14-8bc6-9c30d9b6d1cb · outbound

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Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Zaheer, G

Reference 56

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Observation 78f4d54d-7bfd-4a1c-9786-198fcda23218 · outbound

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Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Unresolved cited work

Reference 57

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Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Longformer: The Long-Document Transformer

Reference 58

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Observation 46ba05bb-b45f-4bc2-a7ea-8bc4321041ea · outbound

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Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? SimCSE: Simple Contrastive Learning of Sentence Embeddings

Reference 59

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Observation 821d9241-4e0d-40a1-95e4-3d868be9847b · outbound

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Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models

Reference 60

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Observation c3822f70-7af0-4b55-8324-5f1aec32ae9c · outbound

This paper cites UL2: Unifying Language Learning Paradigms.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? UL2: Unifying Language Learning Paradigms

Reference 61

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Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 62

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Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Unresolved cited work

Reference 64

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Observation bb8bbf38-75fd-478c-8bab-b2afce8e8bda · outbound

This paper cites an unresolved cited work.

Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Unresolved cited work

Reference 66

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Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Unresolved cited work

Reference 67

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Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Unresolved cited work

Reference 68

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Observation 844cbe16-66a6-4bdd-bcb2-0cd4f89b446d · outbound

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Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Unresolved cited work

Reference 69

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Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Unresolved cited work

Reference 2019

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Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Unresolved cited work

Reference 2020

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Observation 78f23cc7-399e-4f6f-bc5c-c743f0d3c7eb · outbound

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Can bidirectional encoder become the ultimate winner for downstream applications of foundation models? Unresolved cited work

Reference 2021

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Pith citing papers

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The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers cites this paper.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Can bidirectional encoder become the ultimate winner for downstream applications of foundation models?

Reference 38

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Observation 6f6f73b3-0b70-4ae9-a97e-a4f71b64240c · inbound

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Reference 38

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