Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning | Paper Citation Record

Observation 4a7d0024-858d-4fbf-a12d-d9b496af4e5a · outbound

This paper cites In: International Conference on Learning Representations (2023).

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: International Conference on Learning Representations (2023)

Reference 1

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Observation 2e0bd53e-96da-4cc8-9a63-7efccc8e4a6d · outbound

This paper cites Flamingo: a Visual Language Model for Few-Shot Learning.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Flamingo: a Visual Language Model for Few-Shot Learning

Reference 2

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

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 3

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This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 4

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This paper cites In: In- ternational Conference on Machine Learning (2009).

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: In- ternational Conference on Machine Learning (2009)

Reference 5

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This paper cites PaLI: A Jointly-Scaled Multilingual Language-Image Model.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning PaLI: A Jointly-Scaled Multilingual Language-Image Model

Reference 6

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This paper cites PaLI-X: On Scaling up a Multilingual Vision and Language Model.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning PaLI-X: On Scaling up a Multilingual Vision and Language Model

Reference 7

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This paper cites InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

Reference 8

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Observation 19a03779-ebce-4734-9b69-e3428b5439e9 · outbound

This paper cites InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning

Reference 9

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Observation 373c864e-d433-4f13-9320-f32242baf30b · outbound

This paper cites AlpaGasus: Training A Better Alpaca with Fewer Data.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning AlpaGasus: Training A Better Alpaca with Fewer Data

Reference 10

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This paper cites In: International Conference on Learning Representations (2018).

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: International Conference on Learning Representations (2018)

Reference 11

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This paper cites VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding

Reference 12

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Observation f5825c0b-38bf-4afd-98aa-7cedb14c99cd · outbound

This paper cites In: Advances in Neural Information Processing Systems (Datasets and Benchmarks Track) (2024).

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: Advances in Neural Information Processing Systems (Datasets and Benchmarks Track) (2024)

Reference 13

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Observation d4a21d37-845d-45bd-b016-72ac107c4e97 · outbound

This paper cites DataComp-LM: In search of the next generation of training sets for language models.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning DataComp-LM: In search of the next generation of training sets for language models

Reference 14

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This paper cites In: International Conference on Machine Learning (2019).

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: International Conference on Machine Learning (2019)

Reference 15

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Automated Curriculum Learning for Neural Networks

Reference 16

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This paper cites Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers

Reference 17

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Observation 3b28dfa2-f385-4a92-adee-73897fee69df · outbound

This paper cites Training Compute-Optimal Large Language Models.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Training Compute-Optimal Large Language Models

Reference 18

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Observation 374932b4-d66f-4f53-8136-af54dd112864 · outbound

This paper cites Language Is Not All You Need: Aligning Perception with Language Models.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Language Is Not All You Need: Aligning Perception with Language Models

Reference 19

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This paper cites In: International Conference on Artificial Intelligence and Statistics (2019) 16 R.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: International Conference on Artificial Intelligence and Statistics (2019) 16 R

Reference 20

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This paper cites In: International Conference on Machine Learning (2018).

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: International Conference on Machine Learning (2018)

Reference 21

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding

Reference 22

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Scaling Laws for Neural Language Models

Reference 23

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: Advances in Neural Information Processing Systems (2019)

Reference 24

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: Advances in Neural Information Processing Systems (2010)

Reference 25

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Features of Gaia DR3 Spectroscopic Binaries I. Tidal circularization of Main-Sequence Stars

Reference 26

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning LLaVA-OneVision: Easy Visual Task Transfer

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This paper cites Multi-Agent Environments for Vehicle Routing Problems.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Multi-Agent Environments for Vehicle Routing Problems

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Observation 4a7feb1b-a207-4580-901a-1a6bc3e9a635 · outbound

This paper cites In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2024).

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2024)

Reference 29

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

Reference 30

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning VisionTrap: Vision-Augmented Trajectory Prediction Guided by Textual Descriptions

Reference 32

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Improved Baselines with Visual Instruction Tuning

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Visual Instruction Tuning

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

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This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 36

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This paper cites arXiv preprint arXiv:2402.10452 (2024).

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning arXiv preprint arXiv:2402.10452 (2024)

Reference 37

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models

Reference 38

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This paper cites What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 39

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: ACL Short Papers (2010)

Reference 40

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning GPT-4 Technical Report

Reference 41

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Technical report (2023) Goal-Driven Data Optimization for Multimodal Instruction Tuning 17

Reference 42

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Deep Learning on a Data Diet: Finding Important Examples Early in Training

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: North American Chapter of the Association for Computational Linguistics (2024)

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This paper cites In: International Conference on Machine Learn- ing (2021).

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: International Conference on Machine Learn- ing (2021)

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This paper cites In: International Conference on Machine Learning (2018).

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: International Conference on Machine Learning (2018)

Reference 46

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This paper cites In: International Conference on Learning Representations (2018).

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: International Conference on Learning Representations (2018)

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This paper cites Computer Sciences Technical Report 1648, University of Wisconsin–Madison (2009).

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Computer Sciences Technical Report 1648, University of Wisconsin–Madison (2009)

Reference 48

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning MovieChat: From Dense Token to Sparse Memory for Long Video Understanding

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Advances in Neural Information Processing Systems (2023)

Reference 50

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Technical report (2023)

Reference 51

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This paper cites Impact of QCD sum rules coupling constants on neutron stars structure.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Impact of QCD sum rules coupling constants on neutron stars structure

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This paper cites In: International Conference on Learning Representations (2018).

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: International Conference on Learning Representations (2018)

Reference 53

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: Proceedings of the IEEE/CVF International Conference on Computer Vision

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Self-Consistency Improves Chain of Thought Reasoning in Language Models

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This paper cites M$^3$IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning M$^3$IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning

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Observation 930f9f12-1871-45d9-895a-cfd10f3f5128 · outbound

This paper cites In: International Conference on Lan- guage Resources and Evaluation (2020).

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: International Conference on Lan- guage Resources and Evaluation (2020)

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Observation e39ccf5c-5ad5-4945-9d88-75828525e7ab · outbound

This paper cites LongViTU: Instruction Tuning for Long-Form Video Understanding.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning LongViTU: Instruction Tuning for Long-Form Video Understanding

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This paper cites Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World

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This paper cites In: International Conference on Machine Learning (2024).

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: International Conference on Machine Learning (2024)

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This paper cites CV@R penalized portfolio optimization with biased stochastic mirror descent.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning CV@R penalized portfolio optimization with biased stochastic mirror descent

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This paper cites mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

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This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning CoCa: Contrastive Captioners are Image-Text Foundation Models

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Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selection

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This paper cites Adaptive Human Trajectory Prediction via Latent Corridors.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Adaptive Human Trajectory Prediction via Latent Corridors

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This paper cites LLaVA-Video: Video Instruction Tuning With Synthetic Data.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning LLaVA-Video: Video Instruction Tuning With Synthetic Data

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This paper cites MLVU: Benchmarking Multi-task Long Video Understanding.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning MLVU: Benchmarking Multi-task Long Video Understanding

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This paper cites UltraEdit: Instruction-based Fine-Grained Image Editing at Scale.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning UltraEdit: Instruction-based Fine-Grained Image Editing at Scale

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Observation 53c617f2-ffc3-49d0-990d-86bc26ec02a8 · outbound

This paper cites Advances in Neural Information Processing Systems (2023).

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Advances in Neural Information Processing Systems (2023)

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

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