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Paper Citation Record · LEDGER

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali

As of 12 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2412.13860.

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

pith.paper-citation-record.v1
2412.13860 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:46:10.158896Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

48 of 48 outbound references displayed

  • verified exact3
  • verified fuzzy0
  • unresolved45
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d96f0e3b-3223-4760-a52d-be8f56b17d90 · outbound

This paper cites Towards a Cleaner Document-Oriented Multilingual Crawled Corpus.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Towards a Cleaner Document-Oriented Multilingual Crawled Corpus

Reference 1

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source=arxiv_source observed=2026-08-11T12:46:09.941371Z digest=sha256:3f98258341234657d3c03681ba85367f8bbc07985bcf05bdaaf12e480a4a4ac4

Observation 5c0cc411-5f81-4439-a5df-9202c9a2da0c · outbound

This paper cites an unresolved cited work.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Unresolved cited work

Reference 2

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Observation 8dc02396-e731-40ea-8ed1-16c41c69708f · outbound

This paper cites To Code, or Not To Code? Exploring Impact of Code in Pre-training.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali To Code, or Not To Code? Exploring Impact of Code in Pre-training

Reference 3

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source=arxiv_source observed=2026-08-11T12:46:09.950698Z digest=sha256:39fa67f0d66085e7fc626c452120863d513d9020734c540775f9057e9069932f

Observation 7d92f2c7-6509-4b2d-a2ec-ae88604561b4 · outbound

This paper cites Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell

Reference 4

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source=arxiv_source observed=2026-08-11T12:46:09.956067Z digest=sha256:4c6a2e3cf7c6a86a8aa4f5f51e77e711ef5d4f87f329a89ff0697b258d2e0028

Observation 58645642-61ab-41e8-9917-f1dc540e344a · outbound

This paper cites LoRA Learns Less and Forgets Less.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali LoRA Learns Less and Forgets Less

Reference 5

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Observation c770ce2b-eca3-4b8a-82cc-4bb268ed8286 · outbound

This paper cites Language Models are Few-Shot Learners.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Language Models are Few-Shot Learners

Reference 6

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source=arxiv_source observed=2026-08-11T12:46:09.966154Z digest=sha256:bef2242acc9ebc473aeb74a5a1675eff1447cf4aa7b0c6ca79732fb83c5af191

Observation 32e721f9-1d98-4956-b2d8-8aa3a72c2fee · outbound

This paper cites an unresolved cited work.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Unresolved cited work

Reference 7

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source=arxiv_source observed=2026-08-11T12:46:09.971469Z digest=sha256:d1be1f0bdf16c1601e8301c0966eaabc883ad9277a1958cbd5f74252bf24af87

Observation 8831ca91-8caf-4fc4-b1c7-40101b7ca944 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 8

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source=arxiv_source observed=2026-08-11T12:46:09.975651Z digest=sha256:b3c7f1b598d721f3c36eae32131bf0e22c9ea204ea6b59d5b0eff6f46eb7d1e6

Observation 0bfb20dc-e217-4e3f-9bac-1236dfbe97d4 · outbound

This paper cites an unresolved cited work.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Unresolved cited work

Reference 9

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source=arxiv_source observed=2026-08-11T12:46:09.980267Z digest=sha256:77bf2e75e06585cf6bbe27cfb1ccd335715094cb028ebb8ff230e0dccdf5ccb7

Observation 7be5ea65-1b99-490e-b792-7ded29321443 · outbound

This paper cites No Language Left Behind: Scaling Human-Centered Machine Translation.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali No Language Left Behind: Scaling Human-Centered Machine Translation

Reference 10

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source=arxiv_source observed=2026-08-11T12:46:09.984117Z digest=sha256:2adec36997af1fc35d3a2b619aba8a927d5cd762b49d42c09a5ce462193f169a

Observation f48a6afb-ea20-4420-9d4d-bb446a8cc32e · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali QLoRA: Efficient Finetuning of Quantized LLMs

Reference 11

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Observation 9db93b54-db76-4858-83e1-265f397b52bf · outbound

This paper cites The Llama 3 Herd of Models.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali The Llama 3 Herd of Models

Reference 12

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Observation 53293091-d198-4e09-bf9e-bcfeb241d2f2 · outbound

This paper cites Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy

Reference 13

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Observation bebf4876-e402-4b87-8e81-cdde46f662ff · outbound

This paper cites IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages

Reference 14

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Observation fdfb8f1b-524f-4376-905a-ea1aeec0e1f9 · outbound

This paper cites an unresolved cited work.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Unresolved cited work

Reference 15

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Observation b3d51c39-4fe7-4316-90bd-bcc3f1c58fd0 · outbound

This paper cites an unresolved cited work.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Unresolved cited work

Reference 16

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Observation e6ec6a5a-1f1d-469e-a684-7ec9a8ccb6c7 · outbound

This paper cites an unresolved cited work.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Unresolved cited work

Reference 17

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source=arxiv_source observed=2026-08-11T12:46:10.017773Z digest=sha256:ad5da05a8c32c7dc408a7e534dea2b21aa851d73a156ad70fdd5b411c7324ce1

Observation 46f61ca0-d8bd-4b2d-9ba3-16d288635a4e · outbound

This paper cites Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?

Reference 18

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source=arxiv_source observed=2026-08-11T12:46:10.022630Z digest=sha256:c5f98a645092acdcf912c63c10dea2e804d0e8ed80f465775574827a6f4b0bae

Observation 556f8cdd-a61f-47ec-b158-8f159dc0305c · outbound

This paper cites an unresolved cited work.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Unresolved cited work

Reference 19

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Observation 2bf34d31-6604-4750-b54c-7e721c05740b · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Measuring Massive Multitask Language Understanding

Reference 20

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Observation b3217921-37a0-4db1-99ab-1fd99b72715d · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali LoRA: Low-Rank Adaptation of Large Language Models

Reference 21

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source=arxiv_source observed=2026-08-11T12:46:10.038054Z digest=sha256:865cf4dcbc337b7f9dcedb38f61e0a49b0c62aa943113959d9b9dcad81411265

Observation 10441af5-2c1b-4ba7-9829-5a6652a6b461 · outbound

This paper cites an unresolved cited work.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Unresolved cited work

Reference 22

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Observation e55d6e39-cc11-49a5-96a7-6258aadf5d8c · outbound

This paper cites Scaling Laws for Neural Language Models.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Scaling Laws for Neural Language Models

Reference 23

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Observation 548fc2f8-53d1-4501-84b9-f062bada8cc4 · outbound

This paper cites ReLoRA: High-Rank Training Through Low-Rank Updates.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali ReLoRA: High-Rank Training Through Low-Rank Updates

Reference 24

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Observation 36a1b804-027c-443f-bbc6-cdfc1da000d6 · outbound

This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 25

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Observation dafa9540-968d-44b6-a0ba-8b86c36e50b4 · outbound

This paper cites Liu, Matt Gardner, Yonatan Belinkov, Matthew E.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Liu, Matt Gardner, Yonatan Belinkov, Matthew E

Reference 26

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Observation a3b725e8-d89d-4b97-a100-f66306d5999b · outbound

This paper cites WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences

Reference 27

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source=arxiv_source observed=2026-08-11T12:46:10.062422Z digest=sha256:f39cd4c744cff2ebf26b9215e517f965930000084711e80ac7f117245b9d4983

Observation 5132ef7d-eaca-46c5-8e17-9ec81dc104a7 · outbound

This paper cites an unresolved cited work.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Unresolved cited work

Reference 28

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 00c2e951-e499-44b0-9eeb-12ee5c1dbbdd · outbound

This paper cites Scaling Data-Constrained Language Models.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Scaling Data-Constrained Language Models

Reference 29

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Observation 9a776ca0-a075-4244-90a3-489e36796be5 · outbound

This paper cites GPT-4 Technical Report.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali GPT-4 Technical Report

Reference 30

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Observation d9a7d230-a684-4e2b-8f01-4e463de32a41 · outbound

This paper cites Carbon Emissions and Large Neural Network Training.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Carbon Emissions and Large Neural Network Training

Reference 31

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Observation 093d43c2-2a77-466b-a16e-0624e003acd5 · outbound

This paper cites INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge

Reference 32

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Observation c0b45d1a-311d-4da4-b020-f14da9c3ccbe · outbound

This paper cites WinoGrande: An Adversarial Winograd Schema Challenge at Scale.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali WinoGrande: An Adversarial Winograd Schema Challenge at Scale

Reference 33

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Observation 28113a2a-d941-482a-bde2-dbb67726f368 · outbound

This paper cites an unresolved cited work.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Unresolved cited work

Reference 34

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation c9d148a5-c80b-4e87-8826-af00173db7fb · outbound

This paper cites an unresolved cited work.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Unresolved cited work

Reference 35

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Observation 592d317d-f3b5-4e32-a65e-9f75ccc5c3a9 · outbound

This paper cites LLM See, LLM Do: Guiding Data Generation to Target Non-Differentiable Objectives.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali LLM See, LLM Do: Guiding Data Generation to Target Non-Differentiable Objectives

Reference 36

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Observation 1f9659f8-277b-4f69-a414-e798554dd9a9 · outbound

This paper cites Energy and Policy Considerations for Deep Learning in NLP.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Energy and Policy Considerations for Deep Learning in NLP

Reference 37

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Observation 24af6fa8-e49e-4cda-a0ff-32278dad7253 · outbound

This paper cites Hashimoto.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Hashimoto

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation c369351e-53f3-4c0e-994f-8731724d4951 · outbound

This paper cites an unresolved cited work.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Unresolved cited work

Reference 39

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:46:10.119661Z digest=sha256:9160373302670729fabe125e801f2276049343d5e668c1b4096117c505b16435

Observation 3be49ca8-ef8b-4eea-93fe-186f9059de5d · outbound

This paper cites an unresolved cited work.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Unresolved cited work

Reference 40

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5903fae7-375d-45c2-93a3-53bde9a10cc1 · outbound

This paper cites Will we run out of data? Limits of LLM scaling based on human-generated data.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Will we run out of data? Limits of LLM scaling based on human-generated data

Reference 41

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Source-reported events for the cited work

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Observation eca352b8-7bb8-47f6-a9cf-f7169c8c15cd · outbound

This paper cites an unresolved cited work.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Unresolved cited work

Reference 42

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 633d513e-30ba-426f-8b95-e4914ac942ce · outbound

This paper cites Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning

Reference 43

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3ff3d513-88ec-4503-99e8-22c679b85d1f · outbound

This paper cites an unresolved cited work.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Unresolved cited work

Reference 44

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 3c94964d-b3aa-4f51-bc12-fd34abd039b7 · outbound

This paper cites GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Reference 45

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Source-reported events for the cited work

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Observation db14d772-a198-4f3e-99d2-a60c42d1779c · outbound

This paper cites Investigating Continual Pretraining in Large Language Models: Insights and Implications.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali Investigating Continual Pretraining in Large Language Models: Insights and Implications

Reference 46

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unresolved
no resolver link, observed 2026-08-11T12:46:10.150028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e0795a1d-4109-4007-8d50-82535b9b7fb6 · outbound

This paper cites online" 'onlinestring :=.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali online" 'onlinestring :=

Reference 47

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unresolved
no resolver link, observed 2026-08-11T12:46:10.154302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e4533c2b-def3-4f41-bb8f-aff2444a7a9d · outbound

This paper cites write newline.

Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali write newline

Reference 48

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.