DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection | Paper Citation Record

Observation 156914e9-0875-4a1f-b661-2b34e636fb97 · outbound

This paper cites Dtllm-vlt: Diverse text generation for visual language tracking based on llm,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Dtllm-vlt: Diverse text generation for visual language tracking based on llm,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.280687Z

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Observation 07b8d3a0-13f9-4825-9d30-fff4ee4bb992 · outbound

This paper cites Layoutllm-t2i: Eliciting layout guidance from llm for text-to-image generation,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Layoutllm-t2i: Eliciting layout guidance from llm for text-to-image generation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.271014Z

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Observation 6b3fbfb2-c287-4b3a-8136-2091caab10b7 · outbound

This paper cites Contrastive prefer- ence optimization: Pushing the boundaries of llm performance in machine translation,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Contrastive prefer- ence optimization: Pushing the boundaries of llm performance in machine translation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.262003Z

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Observation 6c29e67a-01b0-49dd-baaf-a650e8db2e2d · outbound

This paper cites Designing heterogeneous llm agents for financial sentiment analysis,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Designing heterogeneous llm agents for financial sentiment analysis,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.253147Z

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Observation 345ba1ab-49d3-4239-96c6-724d1a968c86 · outbound

This paper cites Toolqa: A dataset for llm question answering with external tools,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Toolqa: A dataset for llm question answering with external tools,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.244011Z

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Observation 313bd9b9-3393-48a1-b5e6-11a84bc6bf22 · outbound

This paper cites P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.235709Z

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Observation c093b9bd-c325-48b0-ae7a-760f2d94eee0 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection The power of scale for parameter-efficient prompt tuning,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T23:22:20.500828Z

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Observation a90539e0-0cf1-42b3-87bb-aa4d33a18375 · outbound

This paper cites Parameter-efficient transfer learning with diff pruning,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Parameter-efficient transfer learning with diff pruning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.222698Z

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Observation 1530f6a0-8edb-4a1f-823b-48e691a8d4e1 · outbound

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

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection LoRA: Low-rank adaptation of large language models,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.215221Z

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Observation d4ddf4e3-7d07-4b4a-80b8-9381cb9140aa · outbound

This paper cites Autolora: Automatically tuning matrix ranks in low-rank adaptation based on meta learning,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Autolora: Automatically tuning matrix ranks in low-rank adaptation based on meta learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.207701Z

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Observation 528359a7-f464-4943-8b99-d6f6ccd1e8fc · outbound

This paper cites Adaptive budget allocation for parameter-efficient fine-tuning,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Adaptive budget allocation for parameter-efficient fine-tuning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.200276Z

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Observation f60bc43d-b76c-44fa-915c-c46f230ce661 · outbound

This paper cites Alora: Allocating low-rank adaptation for fine-tuning large language models,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Alora: Allocating low-rank adaptation for fine-tuning large language models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.193093Z

Source-reported events for the cited work

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Observation cb97b640-3f7e-4d4e-8995-cf21e2fcac97 · outbound

This paper cites DoRA: Enhancing Parameter-Efficient Fine-Tuning with Dynamic Rank Distribution.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection DoRA: Enhancing Parameter-Efficient Fine-Tuning with Dynamic Rank Distribution

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T23:22:20.518775Z

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Observation 6e110f34-5562-4b07-bc7c-60e1f2167ae3 · outbound

This paper cites LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T23:22:20.522370Z

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Observation 7cd543ea-000a-42f8-bde7-325810eacfea · outbound

This paper cites Unveiling lora intrinsic ranks via salience analysis,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Unveiling lora intrinsic ranks via salience analysis,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.185590Z

Source-reported events for the cited work

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Observation 0dbb49a7-8355-4ff2-a2c4-1908b4a96ef7 · outbound

This paper cites GLUE: A multi-task benchmark and analysis platform for natural language understanding,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection GLUE: A multi-task benchmark and analysis platform for natural language understanding,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.177320Z

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Observation da6d89b5-cf11-4f39-b2eb-03c5215de3bd · outbound

This paper cites Efficient parametrization of multi-domain deep neural networks,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Efficient parametrization of multi-domain deep neural networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.169031Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 07c532fd-fae1-4a99-8134-3e8fc7341a66 · outbound

This paper cites Velora: Memory efficient training using rank-1 sub-token projections,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Velora: Memory efficient training using rank-1 sub-token projections,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.160273Z

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Observation 8e903b95-d8d8-4f35-8db7-45fc0cfa28d4 · outbound

This paper cites Hifi: High-information attention heads hold for parameter-efficient model adaptation,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Hifi: High-information attention heads hold for parameter-efficient model adaptation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.151976Z

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Observation fd2120ea-e1b8-4715-9a11-f4447c58949e · outbound

This paper cites Bitfit: Simple parameter-efficient fine-tuning for transformer- based masked language-models,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Bitfit: Simple parameter-efficient fine-tuning for transformer- based masked language-models,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.143575Z

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Observation 4e5bda04-9ed4-4bdb-a057-09ae1b3682f0 · outbound

This paper cites What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T23:22:20.543117Z

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Observation ac3e3331-17ec-4e35-94c5-52199248e275 · outbound

This paper cites VeRA: Vector-based random matrix adaptation,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection VeRA: Vector-based random matrix adaptation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.135141Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9237c61f-3f78-46e5-9ffa-612e79581e0e · outbound

This paper cites LoRA+: Efficient low rank adaptation of large models,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection LoRA+: Efficient low rank adaptation of large models,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.126500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4a76f080-a544-4e7b-82ae-3c25df6ed50f · outbound

This paper cites DoRA: Weight-decomposed low-rank adaptation,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection DoRA: Weight-decomposed low-rank adaptation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.117568Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cec22598-bb7b-4f5b-9ed1-d482b5c15c0b · outbound

This paper cites Qlora: Efficient finetuning of quantized llms,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Qlora: Efficient finetuning of quantized llms,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.109804Z

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Observation 577d88d8-f4c6-46c6-b4ab-ec6f386af1b0 · outbound

This paper cites Tied-lora: Enhancing parameter efficiency of lora with weight tying,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Tied-lora: Enhancing parameter efficiency of lora with weight tying,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.101685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 254ac9b7-a447-4767-b19a-70158068e712 · outbound

This paper cites Lora+: Efficient low rank adaptation of large models,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Lora+: Efficient low rank adaptation of large models,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.093072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 114a2b6c-2305-4ba0-a18f-be93885a6f2b · outbound

This paper cites LoRA-pro: Are low-rank adapters properly optimized?.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection LoRA-pro: Are low-rank adapters properly optimized?

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.085081Z

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Observation f1f867d2-76bd-4a84-993e-6bb42b449032 · outbound

This paper cites Hydralora: An asymmetric lora architecture for efficient fine-tuning,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Hydralora: An asymmetric lora architecture for efficient fine-tuning,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.077103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f0fca4b5-255a-44eb-b3ab-d917ae877c9a · outbound

This paper cites Sparse low-rank adaptation of pre-trained language models,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Sparse low-rank adaptation of pre-trained language models,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.069202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 053e2748-19e1-4562-98af-8d689db1f614 · outbound

This paper cites Gradient descent provably optimizes over-parameterized neural networks,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Gradient descent provably optimizes over-parameterized neural networks,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.060782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4bb6158b-e3eb-4dc6-9836-c29a00652103 · outbound

This paper cites Visualizing the loss landscape of neural nets,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Visualizing the loss landscape of neural nets,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T23:22:20.570583Z

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Observation 10fb0e32-78d2-467f-965a-f3749589e2e6 · outbound

This paper cites Meco: zero-shot nas with one data and single forward pass via minimum eigenvalue of correlation,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Meco: zero-shot nas with one data and single forward pass via minimum eigenvalue of correlation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.047116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9181c037-48c7-4bee-9a86-649d6d2d6509 · outbound

This paper cites an unresolved cited work.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Unresolved cited work

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T23:22:20.575357Z

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Observation 520853a7-e6e5-4b15-82a9-41ec1dfbc421 · outbound

This paper cites Generalization bounds of stochastic gradient descent for wide and deep neural networks,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Generalization bounds of stochastic gradient descent for wide and deep neural networks,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.032705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6f40befa-3e4a-44e0-af8c-68f17b509cf4 · outbound

This paper cites Generalization properties of nas under activation and skip connection search,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Generalization properties of nas under activation and skip connection search,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.023626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 080bc6aa-f9b6-4507-8730-2f1a03d25a76 · outbound

This paper cites DeBERTav3: Improving deBERTa using ELECTRA-style pre-training with gradient-disentangled embedding sharing,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection DeBERTav3: Improving deBERTa using ELECTRA-style pre-training with gradient-disentangled embedding sharing,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.013578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6bd1a59f-f17c-4269-b1a0-6db4cdae15f6 · outbound

This paper cites GLUE: A multi-task benchmark and analysis platform for natural language understanding,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection GLUE: A multi-task benchmark and analysis platform for natural language understanding,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:21.004605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1647e718-579b-44f4-a6ad-6db063ead0c6 · outbound

This paper cites SQuAD: 100,000+ questions for machine comprehension of text,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection SQuAD: 100,000+ questions for machine comprehension of text,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.995590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0b63be53-6c84-4277-b5e4-641fc40efaa7 · outbound

This paper cites Know what you don’t know: Unanswerable questions for SQuAD,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Know what you don’t know: Unanswerable questions for SQuAD,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.986174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 979ad495-1e65-4ea5-be0b-6a0304004ae4 · outbound

This paper cites The Llama 3 Herd of Models.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection The Llama 3 Herd of Models

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T23:22:20.592640Z

Source-reported events for the cited work

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Observation 8aa3c1db-8218-4bb4-9d66-18c33b7cf9d2 · outbound

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

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T23:22:20.595794Z

Source-reported events for the cited work

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Observation e46c3201-d875-472c-b862-927997b779d5 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T23:22:20.598963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6f5cf2e-fb0a-40ac-a31b-03946e616a48 · outbound

This paper cites Magnus and H.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Magnus and H

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.977087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a087289f-35a4-4850-8180-8758eda8cab7 · outbound

This paper cites an unresolved cited work.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Unresolved cited work

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T23:22:20.605297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6016e1fe-1ddb-426c-bf66-3a628c417a7b · outbound

This paper cites Parameter-efficient transfer learning for nlp,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Parameter-efficient transfer learning for nlp,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T23:22:20.608084Z

Source-reported events for the cited work

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Observation ced63b0d-d566-4804-8710-5eef14af44bb · outbound

This paper cites Adapterfusion: Non-destructive task composition for transfer learning,.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Adapterfusion: Non-destructive task composition for transfer learning,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.958798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 330972af-c10f-428f-8ba0-43810babcf15 · outbound

This paper cites (vii) LoRA+ [27] employs different learning rates to update the low-rank matrices.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection (vii) LoRA+ [27] employs different learning rates to update the low-rank matrices

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.950846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 317b0b82-7ff3-407c-b8a8-eaa542980a97 · outbound

This paper cites Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.943300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 63ff2f14-3d0d-4045-a338-f0caa06613bb · outbound

This paper cites Limitations.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Limitations

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.935745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7e7417d2-a811-4b0e-8e78-4af49850f0e3 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include theoretical results.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Guidelines: • The answer NA means that the paper does not include theoretical results

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.928575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5e53a778-8094-4001-9c94-30bbde956e13 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Guidelines: • The answer NA means that the paper does not include experiments

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.921263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e465f94a-3f96-46e0-8801-e1524d9b9f25 · outbound

This paper cites All the datasets we used are public.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection All the datasets we used are public

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.913776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 795902c1-0969-48fb-9718-27833f040db0 · outbound

This paper cites For additional experimental settings on various benchmarks, we provided the details in Appendix D.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection For additional experimental settings on various benchmarks, we provided the details in Appendix D

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.905746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4130467a-18b2-4dd4-b945-f879bdd14d96 · outbound

This paper cites We have run our experiments multiple times with different seeds and provided the mean and standard deviation values in Section 5.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection We have run our experiments multiple times with different seeds and provided the mean and standard deviation values in Section 5

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.896773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 62148de5-31bd-4745-8cee-528b8d32e83a · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Guidelines: • The answer NA means that the paper does not include experiments

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.887280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 486e6e0b-819d-4484-9e05-c2da5beee003 · outbound

This paper cites Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.878372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ec0e2f8d-c2ad-4590-89cf-271b04a3a04c · outbound

This paper cites Guidelines: • The answer NA means that there is no societal impact of the work performed.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.869739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6e885e51-4b6d-4fdb-ab4a-db9212839b9e · outbound

This paper cites Guidelines: • The answer NA means that the paper poses no such risks.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Guidelines: • The answer NA means that the paper poses no such risks

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.860142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f48657ea-6c94-4feb-8943-0b2275d8a2df · outbound

This paper cites We have properly cited these resources in our paper.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection We have properly cited these resources in our paper

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.850484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7ebbf6da-f897-4ccc-93e8-522af3ade967 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not release new assets.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Guidelines: • The answer NA means that the paper does not release new assets

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.841199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 22c03add-6f7f-4cc3-8f90-2d1945eb44b3 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.832249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f95ab010-44fc-4c54-a638-f564dfdfc86a · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.823863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d1f7ad08-7e5a-4870-8964-f4cbdd9dfc4f · outbound

This paper cites Answer: [NA] Justification: We utilize LLM only for grammar and formatting purposes.

DiffoRA: Enabling Parameter-Efficient Fine-Tuning via Differential Module Selection Answer: [NA] Justification: We utilize LLM only for grammar and formatting purposes

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:22:20.813831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers