Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-09T11:12:08.289178Z
Paper Citation Record · LEDGER
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting
As of 10 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 4 inbound Pith citation observations for arXiv:2502.02797.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T05:52:38.387523Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-20T07:18:07.045532Z
Reference resolution
38 of 38 outbound references displayed
- verified exact2
- verified fuzzy15
- unresolved18
- parse uncertain0
- malformed identifier3
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation c2ccc43e-9e2b-4e7c-925c-f5bb6aa4c785 · outbound
This paper cites HellaSwag presents a context followed by several plausible endings, and the model must choose the most appropriate continuation.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting HellaSwag presents a context followed by several plausible endings, and the model must choose the most appropriate continuation
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T11:12:16.058577Z
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 6d7fa0af-16b3-4db9-8843-7d4579950811 · outbound
This paper cites exp − ⟨r,z⟩ 2 α ! ⟨r,z⟩ 2 # r+ d−1X j=1 E.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting exp − ⟨r,z⟩ 2 α ! ⟨r,z⟩ 2 # r+ d−1X j=1 E
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T11:12:16.080018Z
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 fc444b66-8d68-4f14-9717-5498278db48a · outbound
This paper cites an unresolved cited work.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Unresolved cited work
Reference 3
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T11:12:16.035563Z
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 b9f055f2-d46f-46ec-bbc1-a89304aac1be · outbound
This paper cites The Llama 3 Herd of Models.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting The Llama 3 Herd of Models
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T11:12:08.165381Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6f256338-d201-4106-b555-34eb22ff346a · outbound
This paper cites an unresolved cited work.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Unresolved cited work
Reference 5
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T11:12:16.014017Z
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 854a8fd4-5572-425b-b969-34600f2fd6d3 · outbound
This paper cites an unresolved cited work.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Unresolved cited work
Reference 6
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T11:12:16.002966Z
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 50348aa0-de56-4d17-8122-64802b448311 · outbound
This paper cites an unresolved cited work.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Unresolved cited work
Reference 7
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T11:12:15.992760Z
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 ff7b63de-605d-4509-afb8-954cb436f66d · outbound
This paper cites an unresolved cited work.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Unresolved cited work
Reference 8
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T11:12:15.981055Z
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 d1137124-7b6e-47f1-9110-4e3af87fa5a3 · outbound
This paper cites org/CorpusID:215786151.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting org/CorpusID:215786151
Reference 9
- Resolution
- verified exact
- arxiv_id_nonexistent, observed 2026-08-09T11:12:15.715919Z
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 edf95260-5ff9-4d74-a7bf-98bf98702169 · outbound
This paper cites Online Batch Selection for Faster Training of Neural Networks.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Online Batch Selection for Faster Training of Neural Networks
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T11:12:08.188932Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bdf3674e-d94a-4e7f-ad11-9bcb54f29261 · outbound
This paper cites org/CorpusID:5324823.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting org/CorpusID:5324823
Reference 11
- Resolution
- verified exact
- raw_fallback, observed 2026-08-09T11:12:09.791349Z
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 412af3d0-4e8a-4aa3-90e3-163c718e1d68 · outbound
This paper cites Rajasegaran, J., Hayat, M., Khan, S.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Rajasegaran, J., Hayat, M., Khan, S
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T11:12:16.103297Z
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 f41b576d-52a8-43f4-bedf-996de2be4cde · outbound
This paper cites Progressive Neural Networks.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Progressive Neural Networks
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T11:12:08.204038Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 771fc37b-44ac-4711-90e8-e94bb3d7cec9 · outbound
This paper cites org/CorpusID:12253672.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting org/CorpusID:12253672
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T11:12:08.208134Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 99d97692-4d4d-4e3f-b9f0-c456f257e38b · outbound
This paper cites MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
Reference 18
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-09T11:12:08.220099Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a8f477e3-57e1-4cf2-bd67-b1c1b4467abd · outbound
This paper cites Following a similar setup as us, Biderman et al.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Following a similar setup as us, Biderman et al
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T11:12:16.069410Z
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 a8474ce1-bc1b-4281-87aa-3b3011465a60 · outbound
This paper cites an unresolved cited work.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Unresolved cited work
Reference 22
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T11:12:16.047304Z
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 6daab356-2370-40f1-b67c-bdd79792b22c · outbound
This paper cites PIQA presents a goal and two possible solutions, requiring models to choose the most appropriate solution that demonstrates an understanding of everyday physical interactions.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting PIQA presents a goal and two possible solutions, requiring models to choose the most appropriate solution that demonstrates an understanding of everyday physical interactions
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T11:12:16.024669Z
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 0c4ec655-c716-4224-af00-562ee86007e5 · outbound
This paper cites an unresolved cited work.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Unresolved cited work
Reference 29
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T11:12:15.969356Z
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 09131df9-59a1-4024-90a3-d6029de9960a · outbound
This paper cites It is a widely used large-scale image classification dataset, consisting of over a million images spanning 1000 classes.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting It is a widely used large-scale image classification dataset, consisting of over a million images spanning 1000 classes
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T11:12:15.958626Z
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 7308244a-cde9-46bc-9e28-6d2a656b94fc · outbound
This paper cites It consists of 60,000 32x32 color images divided into ten classes, with 6,000 images per class.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting It consists of 60,000 32x32 color images divided into ten classes, with 6,000 images per class
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T11:12:15.946836Z
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 b9efab78-7e0e-425c-9894-dd53841d50ee · outbound
This paper cites This dataset is used for fine-grained image classification tasks.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting This dataset is used for fine-grained image classification tasks
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T11:12:15.934177Z
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 6d8d407e-5051-4bc6-b42f-ebc083f36932 · outbound
This paper cites an unresolved cited work.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Unresolved cited work
Reference 33
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T11:12:15.922736Z
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 137723b8-1704-4e20-a93c-84ab1f940b38 · outbound
This paper cites This dataset is commonly used for fine-grained image classification and flower recognition tasks.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting This dataset is commonly used for fine-grained image classification and flower recognition tasks
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T11:12:15.911767Z
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 cf488483-90c7-4328-a29d-3a14f6ebb672 · outbound
This paper cites It provides a rich resource for fine-grained car classification task.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting It provides a rich resource for fine-grained car classification task
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T11:12:15.900306Z
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 362cd836-dbf1-4197-9ac2-485d88f2f590 · outbound
This paper cites This dataset is widely used for fine-grained dog breed classification and recognition tasks.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting This dataset is widely used for fine-grained dog breed classification and recognition tasks
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T11:12:15.889747Z
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 b15b7d3e-923d-4d9c-a933-845dbb8d00d6 · outbound
This paper cites learning without forgetting.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting learning without forgetting
Reference 37
- Resolution
- malformed identifier
- arxiv_id_nonexistent, observed 2026-08-09T11:12:09.618747Z
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 7329e9ca-7020-480f-a135-f2f3755f7d4e · outbound
This paper cites We use varying α∈[0,1] for WiSE-FT.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting We use varying α∈[0,1] for WiSE-FT
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T11:12:15.877828Z
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 4a07ffec-cf2a-40ba-ae5f-52c075750bf2 · outbound
This paper cites Robust fine-tuning of zero-shot models.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Robust fine-tuning of zero-shot models
Reference 715
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T11:12:08.212220Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d2a9bcb5-33a8-432d-ac1b-f24b15ac94e8 · outbound
This paper cites Program Synthesis with Large Language Models.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Program Synthesis with Large Language Models
Reference 2015
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-09T11:12:08.151280Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 652fedc3-3e29-4a3d-ab20-63b95647d0a0 · outbound
This paper cites Soup to go: mitigating forgetting during continual learning with model averaging.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Soup to go: mitigating forgetting during continual learning with model averaging
Reference 2016
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T11:12:08.181402Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 669d8da3-f5fc-4c34-a3dd-a28249ef1196 · outbound
This paper cites org/CorpusID:19243534.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting org/CorpusID:19243534
Reference 2017
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T11:12:16.114414Z
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 6375b629-10eb-4e90-82b5-d4b965620b82 · outbound
This paper cites org/CorpusID:3652214.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting org/CorpusID:3652214
Reference 2018
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T11:12:16.091009Z
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 bceb9997-81d9-4e03-b133-b29e78819aa3 · outbound
This paper cites Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMs.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMs
Reference 2019
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T11:12:08.196583Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2dc35d19-4a5f-42b8-9dae-830518ad121a · outbound
This paper cites Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T11:12:08.160998Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0975d986-e081-4f49-9190-1d3485098c4e · outbound
This paper cites org/CorpusID:232427874.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting org/CorpusID:232427874
Reference 2021
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T11:12:16.126050Z
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 26d3063b-2bd8-4fac-8c5d-c7760eb26654 · outbound
This paper cites Biased Importance Sampling for Deep Neural Network Training.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Biased Importance Sampling for Deep Neural Network Training
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T11:12:08.177819Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b50c536a-cea3-42cb-bcb6-2a261be1382f · outbound
This paper cites Mistral 7B.
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Mistral 7B
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T11:12:08.170193Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 80febf3b-4f6f-4f2d-bfb4-44f361f16f20 · inbound
Good SFT Optimizes for SFT, Better SFT Prepares for Reinforcement Learning cites this paper.
Good SFT Optimizes for SFT, Better SFT Prepares for Reinforcement Learning Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T05:52:38.387523Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 54d57dfe-4679-4273-a596-261ae3d82390 · inbound
Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts cites this paper.
Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting
Reference 75
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T06:15:58.986795Z
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 f34a04a3-6780-4f9f-9e69-7b280cef8f84 · inbound
Fine-Tuning Without Forgetting via Loss-Adaptive Learning Rates cites this paper.
Fine-Tuning Without Forgetting via Loss-Adaptive Learning Rates Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting
Reference 51
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T07:18:07.047290Z
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 8e65ae95-452e-483c-bc48-f69654095d5b · inbound
UniGen-AR: Unifying Visual Generation with Auto-Regressive Modeling cites this paper.
UniGen-AR: Unifying Visual Generation with Auto-Regressive Modeling Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T22:25:12.430501Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.