Paper Citation Record · LEDGER

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning

As of 17 August 2026, this Paper Citation Record lists 100 of 111 outbound references and 5 inbound Pith citation observations for arXiv:2411.10928.

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

pith.paper-citation-record.v1
2411.10928 v1

Coverage vector

measured 100 of 111 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:14:24.308625Z

measured 105 of 105 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:46:51.193753Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T16:26:35.777715Z

Reference resolution

100 of 111 outbound references displayed

verified exact0
verified fuzzy46
unresolved54
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation be30b2e9-9e4a-4868-8b6c-02f732616e5c · outbound

This paper cites Flamingo: a visual language model for few-shot learning.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Flamingo: a visual language model for few-shot learning

Reference 1

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Observation a3fa0501-a09c-48c6-a31d-890763afe444 · outbound

This paper cites PaLM 2 Technical Report.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning PaLM 2 Technical Report

Reference 2

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Observation 8a7cb665-c351-49fb-99a2-8fbedfa91bb2 · outbound

This paper cites Composable sparse fine-tuning for cross-lingual transfer.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Composable sparse fine-tuning for cross-lingual transfer

Reference 3

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Observation c2e0e617-22a8-48b4-9a91-54987970165d · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 4

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Observation 3299ecbb-9ea8-4653-851a-45e03e749649 · outbound

This paper cites Dual lottery ticket hypothesis.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Dual lottery ticket hypothesis

Reference 5

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Observation 34a2b509-13fb-437c-8645-e43ed2b7a846 · outbound

This paper cites Language models are few-shot learners.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Language models are few-shot learners

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T19:14:23.884781Z

Source-reported events for the cited work

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Observation 41adf560-1d0e-42e8-a83f-51931ff1b7b2 · outbound

This paper cites Dark experience for general continual learning: a strong, simple baseline.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Dark experience for general continual learning: a strong, simple baseline

Reference 7

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Observation 140d1801-0c5e-4cf6-87c4-2895c5d12839 · outbound

This paper cites Honeybee: Locality-enhanced projector for multimodal llm.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Honeybee: Locality-enhanced projector for multimodal llm

Reference 8

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Observation bcbb0e57-99de-4119-8c4e-33e7801215ed · outbound

This paper cites A unified lottery ticket hypothe- sis for graph neural networks.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning A unified lottery ticket hypothe- sis for graph neural networks

Reference 9

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Observation 0e2fdcb3-d5ef-4808-b817-ac4f94ad194e · outbound

This paper cites Improved Baselines with Momentum Contrastive Learning.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Improved Baselines with Momentum Contrastive Learning

Reference 10

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Observation 49e7361f-f641-43e2-bcbd-e3931cee507f · outbound

This paper cites An empiri- cal study of training self-supervised vision transformers.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning An empiri- cal study of training self-supervised vision transformers

Reference 11

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Observation 2fca8490-ca60-4669-8aa5-17cb9a548933 · outbound

This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality

Reference 12

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Observation 880f13ef-c1b8-40d7-a08f-a48ed0189da7 · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning PaLM: Scaling Language Modeling with Pathways

Reference 13

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Observation 7d35fbd7-bf2e-4955-8a99-d7629b36e81c · outbound

This paper cites Instructblip: Towards general- purpose vision-language models with instruction tuning.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Instructblip: Towards general- purpose vision-language models with instruction tuning

Reference 14

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Observation f67ce704-e37f-4287-90c2-f544b005eb97 · outbound

This paper cites A tutorial on the cross-entropy method.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning A tutorial on the cross-entropy method

Reference 15

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Observation 8d8bc2a5-c8fe-4345-b03f-003c251cfa24 · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 16

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Source-reported events for the cited work

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Observation 165d2a00-4f6f-4736-a360-e570f8084fcd · outbound

This paper cites On the mathematical foundations of theoretical statistics.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning On the mathematical foundations of theoretical statistics

Reference 17

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Observation 5cfd363e-cb44-497b-a2a8-02550c207f03 · outbound

This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning The lottery ticket hypothesis: Finding sparse, trainable neural networks

Reference 18

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Observation 0332478c-1441-4cf0-8f4e-ba2dff6475e7 · outbound

This paper cites Sparsegpt: Massive lan- guage models can be accurately pruned in one-shot.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Sparsegpt: Massive lan- guage models can be accurately pruned in one-shot

Reference 19

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Source-reported events for the cited work

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Observation 51d60eac-ee0a-4656-82aa-691c3e4137ef · outbound

This paper cites Catastrophic forgetting in connection- ist networks.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Catastrophic forgetting in connection- ist networks

Reference 20

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Observation 4b089e94-a2c1-4be0-8eb8-bbdc62f4d484 · outbound

This paper cites Clip-adapter: Better vision-language models with feature adapters.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Clip-adapter: Better vision-language models with feature adapters

Reference 21

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Observation befe1434-20bc-4b71-b35f-a9ddb18c7612 · outbound

This paper cites Making the V in VQA matter: El- evating the role of image understanding in Visual Question Answering.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Making the V in VQA matter: El- evating the role of image understanding in Visual Question Answering

Reference 22

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Observation 9be2f9d0-4809-473f-9a93-3dc0348ca446 · outbound

This paper cites On calibration of modern neural networks.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning On calibration of modern neural networks

Reference 23

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Observation f89077bb-50fb-48f0-a856-5b94d0956cee · outbound

This paper cites Learn- ing both weights and connections for efficient neural net- work.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Learn- ing both weights and connections for efficient neural net- work

Reference 24

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Observation fd1e060d-8e21-4ba9-b1b2-5c579a43095e · outbound

This paper cites Towards attack-tolerant federated learning via critical parameter analysis.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Towards attack-tolerant federated learning via critical parameter analysis

Reference 25

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Observation 93fd71a6-c836-41cf-bc58-b8192c2735c8 · outbound

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

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 26

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Observation 8f03c5c4-2d37-4a57-8ab4-bfe23922360e · outbound

This paper cites Flora: Low- rank adapters are secretly gradient compressors.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Flora: Low- rank adapters are secretly gradient compressors

Reference 27

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Observation 185b2326-7f64-44da-9af3-8d8b982c4e9c · outbound

This paper cites Deep residual learning for image recognition.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Deep residual learning for image recognition

Reference 28

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Observation 337672d2-638b-4eb1-8e87-076bd4306a66 · outbound

This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Momentum contrast for unsupervised visual rep- resentation learning

Reference 29

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Observation 3a0bc9ea-ceb7-4f1c-a077-a3aa2399a1f9 · outbound

This paper cites Parameter-efficient transfer learning for nlp.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Parameter-efficient transfer learning for nlp

Reference 30

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This paper cites Lora: Low-rank adaptation of large language models.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Lora: Low-rank adaptation of large language models

Reference 31

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This paper cites Multi-metrics adaptively identifies backdoors in fed- erated learning.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Multi-metrics adaptively identifies backdoors in fed- erated learning

Reference 32

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This paper cites Fisher calibration for backdoor-robust heterogeneous federated learning.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Fisher calibration for backdoor-robust heterogeneous federated learning

Reference 33

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This paper cites Gqa: A new dataset for real-world visual reasoning and compositional question answering.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Gqa: A new dataset for real-world visual reasoning and compositional question answering

Reference 34

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Observation 6af8b3fd-360d-452d-af1f-3017f6844728 · outbound

This paper cites HFT: Half Fine-Tuning for Large Language Models.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning HFT: Half Fine-Tuning for Large Language Models

Reference 35

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This paper cites Editing models with task arith- metic.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Editing models with task arith- metic

Reference 36

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This paper cites Overcoming catastrophic forgetting in neural networks.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Overcoming catastrophic forgetting in neural networks

Reference 37

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This paper cites Simple and scalable predictive uncertainty esti- mation using deep ensembles.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Simple and scalable predictive uncertainty esti- mation using deep ensembles

Reference 38

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Snip: Single-shot network pruning based on connec- tion sensitivity

Reference 39

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Observation 0045bbf7-dfac-4dc8-82de-93e7600c256d · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 40

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Pruning filters for efficient convnets

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Blip: Bootstrapping language-image pre-training for uni- fied vision-language understanding and generation

Reference 42

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:14:25.528167Z

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Observation 00490ce4-46ba-496b-bd77-5922ecdc46e8 · outbound

This paper cites Federated optimiza- tion in heterogeneous networks.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Federated optimiza- tion in heterogeneous networks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:14:25.513902Z

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Observation b079f96d-50e4-4d81-9655-f32dd53cd4c4 · outbound

This paper cites Graphadapter: Tuning vision-language models with dual knowledge graph.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Graphadapter: Tuning vision-language models with dual knowledge graph

Reference 45

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Observation 89d21f91-bdbc-4cbd-9381-6afc0a0b55d7 · outbound

This paper cites Parameter-efficient sparsity for large language models fine-tuning.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Parameter-efficient sparsity for large language models fine-tuning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:14:25.487734Z

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This paper cites Losparse: Structured compression of large language models based on low-rank and sparse approximation.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Losparse: Structured compression of large language models based on low-rank and sparse approximation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:14:25.474171Z

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Observation 332fb438-a9d6-4a27-9e92-00402b29ee05 · outbound

This paper cites Vila: On pre-training for visual language models.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Vila: On pre-training for visual language models

Reference 48

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Observation 6b6a2f1c-bbfd-4631-a0c2-eb65c8299cb6 · outbound

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Microsoft coco: Common objects in context

Reference 49

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This paper cites Mitigating the Alignment Tax of RLHF.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Mitigating the Alignment Tax of RLHF

Reference 50

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This paper cites Sphinx: The joint mixing of weights, tasks, and visual embeddings for multi-modal large language models.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Sphinx: The joint mixing of weights, tasks, and visual embeddings for multi-modal large language models

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:14:25.431680Z

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Observation 5c2b8b42-ec89-4abb-9da5-513b6624c8d8 · outbound

This paper cites Improved baselines with visual instruction tuning.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Improved baselines with visual instruction tuning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:14:25.417688Z

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Observation 68c8f028-249c-4105-b49a-e75d3e075dc0 · outbound

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Visual instruction tuning

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:14:25.404358Z

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Observation 258bab50-016e-4b9d-a221-07b917e04c24 · outbound

This paper cites Dora: Weight-decomposed low-rank adaptation.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Dora: Weight-decomposed low-rank adaptation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:14:25.390440Z

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Observation f6aee091-95ce-462e-9861-0b11508cf13c · outbound

This paper cites Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts

Reference 55

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Rethinking the value of network pruning

Reference 56

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This paper cites Iconqa: A new benchmark for abstract diagram understand- ing and visual language reasoning.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Iconqa: A new benchmark for abstract diagram understand- ing and visual language reasoning

Reference 57

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This paper cites Learn to explain: Multimodal reason- ing via thought chains for science question answering.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Learn to explain: Multimodal reason- ing via thought chains for science question answering

Reference 58

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Twin-merging: Dynamic integration of modular expertise in model merging

Reference 59

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Feast Your Eyes: Mixture-of-Resolution Adaptation for Multimodal Large Language Models

Reference 60

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Ok-vqa: A visual question answering benchmark requiring external knowledge

Reference 62

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Merging models with fisher-weighted averaging

Reference 63

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Catastrophic inter- ference in connectionist networks: The sequential learning problem

Reference 64

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Understanding the role of training regimes in continual learning

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Ocr-vqa: Visual question answering by reading text in images

Reference 66

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning GPT-4 Technical Report

Reference 67

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Task-specific skill localization in fine-tuned language models

Reference 68

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Revisiting Natural Gradient for Deep Networks

Reference 69

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Language models are unsupervised multitask learners

Reference 70

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Learn- ing transferable visual models from natural language super- vision

Reference 71

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Fishr: Invariant gradient variances for out-of-distribution generalization

Reference 72

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Connectionist models of recognition mem- ory: constraints imposed by learning and forgetting func- tions

Reference 73

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This paper cites Online structured laplace approximations for overcoming catastrophic forgetting.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Online structured laplace approximations for overcoming catastrophic forgetting

Reference 74

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Move- ment pruning: Adaptive sparsity by fine-tuning

Reference 75

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Test- time prompt tuning for zero-shot generalization in vision- language models

Reference 76

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Towards vqa models that can read

Reference 77

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Sparse is enough in fine-tuning pre-trained large language models

Reference 78

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning PandaGPT: One Model To Instruction-Follow Them All

Reference 79

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Prompt Tuning based Adapter for Vision-Language Model Adaption

Reference 80

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning A simple and effective pruning approach for large language models

Reference 81

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Training neu- ral networks with fixed sparse masks

Reference 82

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning A Unified View of Delta Parameter Editing in Post-Trained Large-Scale Models

Reference 83

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning LLaMA: Open and Efficient Foundation Language Models

Reference 84

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Learning to grow pretrained models for efficient trans- former training

Reference 85

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Sharpness-aware gradient matching for domain generaliza- tion

Reference 86

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Image as a foreign language: BEiT pretraining for vision and vision-language tasks

Reference 87

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Dualprompt: Complementary prompting for rehearsal-free continual learning

Reference 88

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Parameter-efficient fine-tuning for pre-trained vision models: A survey

Reference 89

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Explicit inductive bias for transfer learning with convolutional net- works

Reference 90

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Lst: Ladder side- tuning for parameter and memory efficient transfer learn- ing

Reference 91

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Dynamic sparsity is channel-level sparsity learner

Reference 92

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions

Reference 93

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Language models are super mario: Absorbing abilities from homologous models as a free lunch

Reference 94

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This paper cites Unified Vision and Language Prompt Learning.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Unified Vision and Language Prompt Learning

Reference 95

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Contin- ual learning through synaptic intelligence

Reference 96

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This paper cites Investigating the catastrophic forgetting in multimodal large language model fine-tuning.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Investigating the catastrophic forgetting in multimodal large language model fine-tuning

Reference 97

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Vision-language models for vision tasks: A survey

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:14:24.922038Z

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 99

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Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Tip-adapter: Training-free clip-adapter for better vision- language modeling

Reference 100

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Pith citing papers

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Reference 275

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Reference 94

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