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Paper Citation Record · LEDGER

Facilitating large language model Russian adaptation with Learned Embedding Propagation

As of 11 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2412.21140.

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

pith.paper-citation-record.v1
2412.21140 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:10:29.143023Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

  • verified exact1
  • verified fuzzy17
  • unresolved18
  • parse uncertain0
  • malformed identifier0
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External citation measurements

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Outbound references

Observation c3c6180f-93ea-49f0-9b8e-b65551835c50 · outbound

This paper cites Stanford alpaca: An instruction-following llama model, 2023.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Stanford alpaca: An instruction-following llama model, 2023

Reference 1

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Observation a9eeb4e3-1a23-4d97-ba5f-1143a654172e · outbound

This paper cites Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation

Reference 2

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Observation beee252b-1d6e-4479-83d4-5036f87ae103 · outbound

This paper cites PolyLM: An Open Source Polyglot Large Language Model.

Facilitating large language model Russian adaptation with Learned Embedding Propagation PolyLM: An Open Source Polyglot Large Language Model

Reference 3

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Observation 342a8f56-d9ac-410b-a51b-eacc49269525 · outbound

This paper cites rulm: A toolkit for training neural language models, 2023.

Facilitating large language model Russian adaptation with Learned Embedding Propagation rulm: A toolkit for training neural language models, 2023

Reference 4

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raw_fallback, observed 2026-08-10T23:10:29.599449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation f01e875d-4fd4-4e3e-b344-26393ce51e14 · outbound

This paper cites Teaching llama a new language through cross-lingual knowledge transfer.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Teaching llama a new language through cross-lingual knowledge transfer

Reference 5

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Observation 379f0928-b76b-4b81-a89b-3219782ee60d · outbound

This paper cites Extrapolating Large Language Models to Non-English by Aligning Languages.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Extrapolating Large Language Models to Non-English by Aligning Languages

Reference 6

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Observation 584075f6-a2a6-454e-85b4-6be9dfb5f998 · outbound

This paper cites Empowering Cross-lingual Abilities of Instruction-tuned Large Language Models by Translation-following demonstrations.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Empowering Cross-lingual Abilities of Instruction-tuned Large Language Models by Translation-following demonstrations

Reference 7

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local_arxiv, observed 2026-08-10T23:10:29.332467Z

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Observation 61ab5408-d8ea-4484-bdc1-2cdadb02cdc8 · outbound

This paper cites Improving in-context learning of multilingual generative language models with cross-lingual alignment.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Improving in-context learning of multilingual generative language models with cross-lingual alignment

Reference 8

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verified fuzzy
raw_fallback, observed 2026-08-10T23:10:29.577526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 163b893f-d806-44a5-89e8-29d9d512007a · outbound

This paper cites xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning.

Facilitating large language model Russian adaptation with Learned Embedding Propagation xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning

Reference 9

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Observation 9592cb6f-974f-4b78-940c-3f4317371c65 · outbound

This paper cites Romansetu: Efficiently unlocking multilingual capabilities of large language models via romanization.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Romansetu: Efficiently unlocking multilingual capabilities of large language models via romanization

Reference 10

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Observation 1e6ad90b-1c99-4b79-b637-c961284fb91f · outbound

This paper cites Transfer learning in multilingual neural machine translation with dynamic vocabulary.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Transfer learning in multilingual neural machine translation with dynamic vocabulary

Reference 11

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Observation 46adecfd-eedb-475c-8592-6fc4d12e45bc · outbound

This paper cites Adaptation of deep bidirectional multilingual transformers for russian language.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Adaptation of deep bidirectional multilingual transformers for russian language

Reference 12

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Observation a9e671c3-7921-4a87-a272-70d93168999b · outbound

This paper cites How good is your tokenizer? on the monolingual performance of multilingual language models.

Facilitating large language model Russian adaptation with Learned Embedding Propagation How good is your tokenizer? on the monolingual performance of multilingual language models

Reference 13

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Observation f1a26fc9-ff35-40bb-82b1-769c4c221fe5 · outbound

This paper cites Cino: A chinese minority pre-trained language model.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Cino: A chinese minority pre-trained language model

Reference 14

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Observation 8c5c8170-71c3-439b-8b34-4ab5baf0f4b8 · outbound

This paper cites As good as new.

Facilitating large language model Russian adaptation with Learned Embedding Propagation As good as new

Reference 15

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Observation e8ea1ac3-5889-49ea-aac2-9cc449211e72 · outbound

This paper cites Impact of tokenization on llama russian adaptation.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Impact of tokenization on llama russian adaptation

Reference 16

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raw_fallback, observed 2026-08-10T23:10:29.488199Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 269844f2-5cf0-4db7-9c71-86319ce019c8 · outbound

This paper cites Improving large language model russian adaptation with preliminary vocabulary optimization.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Improving large language model russian adaptation with preliminary vocabulary optimization

Reference 17

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 658a45ca-72f4-4563-af17-c5982708eabf · outbound

This paper cites Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca

Reference 18

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Observation f60717e4-cb31-4e1f-8e4e-41aa0f912b19 · outbound

This paper cites SeaLLMs -- Large Language Models for Southeast Asia.

Facilitating large language model Russian adaptation with Learned Embedding Propagation SeaLLMs -- Large Language Models for Southeast Asia

Reference 19

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Observation 1b958bd2-0051-4469-b377-566d4a8913b4 · outbound

This paper cites Vikhr: The Family of Open-Source Instruction-Tuned Large Language Models for Russian.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Vikhr: The Family of Open-Source Instruction-Tuned Large Language Models for Russian

Reference 20

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Observation aaa6b3ca-5902-45c4-8836-89bd74280ec0 · outbound

This paper cites On the cross-lingual transferability of monolingual representations.

Facilitating large language model Russian adaptation with Learned Embedding Propagation On the cross-lingual transferability of monolingual representations

Reference 21

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 307ac947-5593-4259-9b07-0eb5f8b19d24 · outbound

This paper cites Improving language plasticity via pretraining with active forgetting.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Improving language plasticity via pretraining with active forgetting

Reference 22

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Observation 2a46bb98-d2a8-4575-bd3a-2374752af70a · outbound

This paper cites Exploring design choices for building language-specific llms.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Exploring design choices for building language-specific llms

Reference 23

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation c5312d44-4e10-480e-a70d-032b50928fc8 · outbound

This paper cites Lima: Less is more for alignment.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Lima: Less is more for alignment

Reference 24

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Observation 8fc4b478-fd46-4f0e-8158-6b4de74521c2 · outbound

This paper cites The Llama 3 Herd of Models.

Facilitating large language model Russian adaptation with Learned Embedding Propagation The Llama 3 Herd of Models

Reference 25

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Observation 9afbaf7a-59ad-46e6-b223-2cb58eed66bf · outbound

This paper cites Scaling Laws for Neural Language Models.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Scaling Laws for Neural Language Models

Reference 26

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Observation e71be400-41cb-4db2-ac94-5642a1c2ef4c · outbound

This paper cites Editing models with task arithmetic.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Editing models with task arithmetic

Reference 27

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raw_fallback, observed 2026-08-10T23:10:29.414780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation eb69c354-f728-48b1-95c5-d97e12a4abad · outbound

This paper cites Dataset for automatic summarization of russian news.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Dataset for automatic summarization of russian news

Reference 28

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verified fuzzy
raw_fallback, observed 2026-08-10T23:10:29.402540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation f8eda68d-1a3b-4992-8e70-00664d83424e · outbound

This paper cites Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

Reference 29

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Observation e27ba5ad-250f-41c9-8ca3-8979e644d52d · outbound

This paper cites Training language models to follow instructions with human feedback.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Training language models to follow instructions with human feedback

Reference 30

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Observation 6eab7686-8e17-4ab4-a20b-45a31c871ba0 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Facilitating large language model Russian adaptation with Learned Embedding Propagation LLaMA: Open and Efficient Foundation Language Models

Reference 31

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Observation db62b355-b26d-4576-92af-b2f67e90e963 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 32

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Observation 3dae5c33-1a3d-471b-a7ae-3d27517b79b8 · outbound

This paper cites Mistral 7B.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Mistral 7B

Reference 33

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Observation 35173460-b38f-4012-81a6-3e3c4f714d72 · outbound

This paper cites GPT-4 Technical Report.

Facilitating large language model Russian adaptation with Learned Embedding Propagation GPT-4 Technical Report

Reference 34

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Observation 737c5022-5d8c-4a19-878b-729510863af0 · outbound

This paper cites MERA: A Comprehensive LLM Evaluation in Russian.

Facilitating large language model Russian adaptation with Learned Embedding Propagation MERA: A Comprehensive LLM Evaluation in Russian

Reference 35

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Observation e24b3c13-c824-4629-974e-c53c50b1bd37 · outbound

This paper cites Rucola: Russian corpus of linguistic acceptability.

Facilitating large language model Russian adaptation with Learned Embedding Propagation Rucola: Russian corpus of linguistic acceptability

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-10T23:10:29.381716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:10:29.143023Z digest=sha256:c57648ce234f5f9b923860d68489b2b0ecb59fa6d075cefe3297cc839f1881ed

Pith citing papers

No inbound Pith citation observations are available.