Paper Citation Record · LEDGER

Joint Localization and Activation Editing for Low-Resource Fine-Tuning

As of 10 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 2 inbound Pith citation observations for arXiv:2502.01179.

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

pith.paper-citation-record.v1
2502.01179 v4

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T16:20:57.837404Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T03:26:41.396467Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T07:21:26.636844Z

Reference resolution

56 of 56 outbound references displayed

verified exact3
verified fuzzy5
unresolved47
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bcea6f30-2f32-479b-bbc5-1750aceaa5b0 · outbound

This paper cites write newline.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.649493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f175398-cc64-4264-bc43-906a5e0b8030 · outbound

This paper cites B it F it: Simple parameter-efficient fine-tuning for transformer-based masked language-models.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning B it F it: Simple parameter-efficient fine-tuning for transformer-based masked language-models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.654871Z

Source-reported events for the cited work

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Observation 4695aea3-c6e5-4efc-9fdd-568536a9ec6d · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Piqa: Reasoning about physical commonsense in natural language

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.658498Z

Source-reported events for the cited work

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Observation a27fd3a5-cb1b-4fb3-8d57-9f714ee9c4df · outbound

This paper cites an unresolved cited work.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Unresolved cited work

Reference 4

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T16:20:58.431155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8cd4a717-67bd-45e4-97ea-4e53ad167b71 · outbound

This paper cites Examining modularity in multilingual LM s via language-specialized subnetworks.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Examining modularity in multilingual LM s via language-specialized subnetworks

Reference 5

Resolution: verified exact; doi, observed 2026-08-09T16:20:57.939010Z

Source-reported events for the cited work

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Observation ef65735f-52c7-44a9-8f8c-0388df3b237d · outbound

This paper cites B ool Q : Exploring the surprising difficulty of natural yes/no questions.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning B ool Q : Exploring the surprising difficulty of natural yes/no questions

Reference 6

Resolution: malformed identifier; no resolver link, observed 2026-08-09T16:20:57.669111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cbcf527-3c88-4ce7-84d3-5f7bd7ab47a6 · outbound

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

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.673110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0b2e791-d049-40c6-a3a5-5d3c0e17ea65 · outbound

This paper cites S., Desai, A., Poli, M., Grogan, J., Liu, A., Rao, A., Rudra, A., and R \'e , C.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning S., Desai, A., Poli, M., Grogan, J., Liu, A., Rao, A., Rudra, A., and R \'e , C

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:20:58.550436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2b7fbe46-8ba3-4457-9f53-09daf5a349d4 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Qlora: Efficient finetuning of quantized llms

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a03d95d-55e4-4404-8f83-efca937e7924 · outbound

This paper cites an unresolved cited work.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-09T16:20:58.535844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4927addc-fdb9-40bc-8120-682a18e494d3 · outbound

This paper cites The Llama 3 Herd of Models.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning The Llama 3 Herd of Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.687063Z

Source-reported events for the cited work

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Observation 60cb45bb-09a5-434c-996a-e9c9cde5bfb2 · outbound

This paper cites and Alistarh, D.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning and Alistarh, D

Reference 12

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Source-reported events for the cited work

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Observation e1e63093-60bc-4635-933f-9e94a3283997 · outbound

This paper cites Creating training corpora for NLG micro-planners.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Creating training corpora for NLG micro-planners

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.693950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef73f607-c73f-4c37-868b-c5400d0d6887 · outbound

This paper cites I., Strobelt, H., Hayashi, H., Novikova, J., Kanerva, J., Chim, J., Zhou, J., Clive, J., Maynez, J., Sedoc, J., Juraska, J., Dhole, K., Chandu, K.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning I., Strobelt, H., Hayashi, H., Novikova, J., Kanerva, J., Chim, J., Zhou, J., Clive, J., Maynez, J., Sedoc, J., Juraska, J., Dhole, K., Chandu, K

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.697951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5664f83a-28ec-4b03-901f-ee5f094d5c76 · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.701222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cb50cfb-bd4f-4f22-bda6-79dc1131fb64 · outbound

This paper cites Does Prompt Formatting Have Any Impact on LLM Performance?.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Does Prompt Formatting Have Any Impact on LLM Performance?

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.704938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe9ef24f-b1b6-47ee-9c15-934864e5623f · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Measuring Massive Multitask Language Understanding

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.708122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0d0f421-d0f4-48ad-8488-2c9e13ee7082 · outbound

This paper cites Parameter-efficient transfer learning for nlp.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Parameter-efficient transfer learning for nlp

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.711720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be72ac95-d784-4810-ae32-69c72b65f685 · outbound

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

Joint Localization and Activation Editing for Low-Resource Fine-Tuning LoRA: Low-Rank Adaptation of Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.715382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d386fb0-131f-41ed-ac26-9288e0dfdfa5 · outbound

This paper cites LLM -adapters: An adapter family for parameter-efficient fine-tuning of large language models.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning LLM -adapters: An adapter family for parameter-efficient fine-tuning of large language models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.718563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ff1b8e1-fe90-4736-9d7a-e984b4147f6a · outbound

This paper cites Categorical reparameterization with gumbel-softmax.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Categorical reparameterization with gumbel-softmax

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.721838Z

Source-reported events for the cited work

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Observation cc590908-5329-449c-8c18-8c4520a06f8e · outbound

This paper cites LLM s beyond E nglish: Scaling the multilingual capability of LLM s with cross-lingual feedback.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning LLM s beyond E nglish: Scaling the multilingual capability of LLM s with cross-lingual feedback

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.724891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 358dba79-561e-4062-8252-e16c4811effb · outbound

This paper cites Differentiable subset pruning of transformer heads.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Differentiable subset pruning of transformer heads

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.727825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbe22a72-ee5c-4be1-8873-d46d4b47a96c · outbound

This paper cites An Exponential Learning Rate Schedule for Deep Learning.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning An Exponential Learning Rate Schedule for Deep Learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.730990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0007bd8-cc28-4fa2-9f17-2e0dd07a767d · outbound

This paper cites Y., Zhou, W., Shen, M., Zhou, P., Bhagavatula, C., Choi, Y., and Ren, X.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Y., Zhou, W., Shen, M., Zhou, P., Bhagavatula, C., Choi, Y., and Ren, X

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.734365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bddeda9a-6bb3-4ae7-8e5e-9e50f87694c9 · outbound

This paper cites ROUGE : A package for automatic evaluation of summaries.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning ROUGE : A package for automatic evaluation of summaries

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.737277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7171ba64-ca42-4372-ba70-1b2746ca7616 · outbound

This paper cites Decoupled Weight Decay Regularization.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Decoupled Weight Decay Regularization

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.740182Z

Source-reported events for the cited work

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Observation ca919979-5251-4ff2-9f55-8f5ba616dcb4 · outbound

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Joint Localization and Activation Editing for Low-Resource Fine-Tuning Unresolved cited work

Reference 28

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Observation 0777003c-f332-4af0-8f15-e0394bfead26 · outbound

This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.747187Z

Source-reported events for the cited work

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Observation b2325ac7-654b-4202-8e3a-6d5d156dc9be · outbound

This paper cites A., Veness, J., Bellemare, M.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning A., Veness, J., Bellemare, M

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.750370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddcfcef9-c3cf-4817-9981-b2c18a42b91e · outbound

This paper cites B., and Lapata, M.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning B., and Lapata, M

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.753917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd20b0ca-5029-42e0-8861-cd9b384634ac · outbound

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Joint Localization and Activation Editing for Low-Resource Fine-Tuning and Sennrich, R

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:20:58.486388Z

Source-reported events for the cited work

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Observation 10d7962a-45f4-4cf8-aa65-d9330a78bb10 · outbound

This paper cites The E 2 E dataset: New challenges for end-to-end generation.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning The E 2 E dataset: New challenges for end-to-end generation

Reference 33

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Source-reported events for the cited work

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Joint Localization and Activation Editing for Low-Resource Fine-Tuning B leu: a method for automatic evaluation of machine translation

Reference 34

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Source-reported events for the cited work

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Observation 58279789-51a4-4ad8-a97e-95758f102148 · outbound

This paper cites Identifying semantic induction heads to understand in-context learning.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Identifying semantic induction heads to understand in-context learning

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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This paper cites L., Bhagavatula, C., and Choi, Y.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning L., Bhagavatula, C., and Choi, Y

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.769297Z

Source-reported events for the cited work

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Observation f85029e3-4a25-4b2e-965d-8be5e07c4713 · outbound

This paper cites Social IQ a: Commonsense reasoning about social interactions.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Social IQ a: Commonsense reasoning about social interactions

Reference 37

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This paper cites S., De Cao, N., and Titov, I.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning S., De Cao, N., and Titov, I

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:20:58.471708Z

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Observation a622f5aa-d6bb-4a99-b152-9fb16f5059ad · outbound

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Joint Localization and Activation Editing for Low-Resource Fine-Tuning Unresolved cited work

Reference 39

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Observation 924eee9d-398d-4221-b934-284873f4764b · outbound

This paper cites A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay

Reference 40

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Joint Localization and Activation Editing for Low-Resource Fine-Tuning A Simple and Effective Pruning Approach for Large Language Models

Reference 41

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Observation deba3e09-ae59-422e-9004-4c597ab7329d · outbound

This paper cites Spdf: Sparse pre-training and dense fine-tuning for large language models.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Spdf: Sparse pre-training and dense fine-tuning for large language models

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:20:58.453081Z

Source-reported events for the cited work

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Observation 2e5c8fe6-e822-424e-9f26-b5c348e79b82 · outbound

This paper cites Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.792873Z

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Observation df1bcd75-6253-4ea5-912e-32a36496aad1 · outbound

This paper cites Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned

Reference 44

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This paper cites Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies

Reference 45

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Joint Localization and Activation Editing for Low-Resource Fine-Tuning MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Reference 46

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Observation 4886da6d-a012-439c-87d8-c6a6fb9abc19 · outbound

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Joint Localization and Activation Editing for Low-Resource Fine-Tuning Advancing parameter efficiency in fine-tuning via representation editing

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.806132Z

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Observation 1fd65537-ce4e-46e7-a1a5-dbe453f87bf5 · outbound

This paper cites ReFT: Representation Finetuning for Language Models.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning ReFT: Representation Finetuning for Language Models

Reference 48

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Joint Localization and Activation Editing for Low-Resource Fine-Tuning Qwen2.5 Technical Report

Reference 49

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Observation ab2a1769-c94b-4ceb-83d7-f851e24ea299 · outbound

This paper cites LoFiT: Localized Fine-tuning on LLM Representations.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning LoFiT: Localized Fine-tuning on LLM Representations

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.816764Z

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This paper cites H ella S wag: Can a machine really finish your sentence? In Korhonen, A., Traum, D., and M \`a rquez, L.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning H ella S wag: Can a machine really finish your sentence? In Korhonen, A., Traum, D., and M \`a rquez, L

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.820232Z

Source-reported events for the cited work

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Observation 88ce4a3f-bd40-454a-8006-98805869a5be · outbound

This paper cites Towards Best Practices of Activation Patching in Language Models: Metrics and Methods.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Towards Best Practices of Activation Patching in Language Models: Metrics and Methods

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.823880Z

Source-reported events for the cited work

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Observation ab985646-4483-4a56-a9b0-46729e380140 · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning BERTScore: Evaluating Text Generation with BERT

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.827397Z

Source-reported events for the cited work

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Observation 46f4c472-f10a-40de-9b33-365573dc415b · outbound

This paper cites Know what you don't need: Single-shot meta-pruning for attention heads.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Know what you don't need: Single-shot meta-pruning for attention heads

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:20:58.442299Z

Source-reported events for the cited work

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Observation 2ef7ed06-e2d0-4298-b796-569e09a26ea9 · outbound

This paper cites A Survey of Large Language Models.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning A Survey of Large Language Models

Reference 55

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Observation 0fb3105c-d0e1-4d1b-abd8-377552f89288 · outbound

This paper cites Representation Engineering: A Top-Down Approach to AI Transparency.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Representation Engineering: A Top-Down Approach to AI Transparency

Reference 56

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Pith citing papers

Observation 18defefa-fbc2-43ba-a085-6d6c5b850de1 · inbound

BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification cites this paper.

BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification Joint Localization and Activation Editing for Low-Resource Fine-Tuning

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:51:06.301283Z

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Observation 8a484062-e492-47e8-b110-65b70dfaca3c · inbound

BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification cites this paper.

BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification Joint Localization and Activation Editing for Low-Resource Fine-Tuning

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:21:26.640064Z

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