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Vision and language model (VLM) decoders are currently the best-performing architectures on multimodal tasks.
17. A Value for n-Person Games , pp. 307–318
L. S. Shapley · 1953
Earlier work this paper cites.
On the properties of neural machine translation: Encoder–decoder approaches
Kyunghyun Cho, Bart van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
Earlier work this paper cites.
Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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FOIL it! find one mismatch between image and language caption
Ravi Shekhar, Sandro Pezzelle, Yauhen Klimovich, Aurélie Herbelot, Moin Nabi, Enver Sangineto, and Raffaella Bernardi · 2017
Earlier work this paper cites.
Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
Earlier work this paper cites.
Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Earlier work this paper cites.
Faithful multimodal explanation for visual question answering
Jialin Wu and Raymond Mooney · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Uniter: Universal image-text representation learning
Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu · 2020
Earlier work this paper cites.
Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg · 2020
Earlier work this paper cites.
Vision-and-language or vision-for-language? on cross-modal influence in multimodal transformers
Stella Frank, Emanuele Bugliarello, and Desmond Elliott · 2021
Earlier work this paper cites.
Perceptual score: What data modalities does your model perceive?
Itai Gat, Idan Schwartz, and Alex Schwing · 2021
Earlier work this paper cites.
Multimodal few-shot learning with frozen language models
Maria Tsimpoukelli, Jacob L Menick, Serkan Cabi, SM Eslami, Oriol Vinyals, and Felix Hill · 2021
Earlier work this paper cites.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki · 2021
Earlier work this paper cites.
Measuring association between labels and free-text rationales
Sarah Wiegreffe, Ana Marasović, and Noah A. Smith · 2021
Earlier work this paper cites.
Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al · 2022
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MAGMA – multimodal augmentation of generative models through adapter-based finetuning
Constantin Eichenberg, Sidney Black, Samuel Weinbach, Letitia Parcalabescu, and Anette Frank · 2022
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VALSE: A task-independent benchmark for vision and language models centered on linguistic phenomena
Letitia Parcalabescu, Michele Cafagna, Lilitta Muradjan, Anette Frank, Iacer Calixto, and Albert Gatt · 2022
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Winoground: Probing vision and language models for visio-linguistic compositionality
Tristan Thrush, Ryan Jiang, Max Bartolo, Amanpreet Singh, Adina Williams, Douwe Kiela, and Candace Ross · 2022
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Benchmarking faithfulness: Towards accurate natural language explanations in vision-language tasks
Jakob Ambsdorf · 2023
MM-SHAP: A performance-agnostic metric for measuring multimodal contributions in vision and language models & tasks
Letitia Parcalabescu and Anette Frank · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Miles Turpin, Julian Michael, Ethan Perez, and Samuel R Bowman · 2023
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Harnessing the power of llms in practice: A survey on chatgpt and beyond
Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Shaochen Zhong, Bing Yin, and Xia Hu · 2023
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When and why vision-language models behave like bags-of-words, and what to do about it?
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Faithfulness tests for natural language explanations
Pepa Atanasova, Oana-Maria Camburu, Christina Lioma, Thomas Lukasiewicz, Jakob Grue Simonsen, and Isabelle Augenstein · 2023
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Openflamingo: An open-source framework for training large autoregressive vision-language models
Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Gadre, Shiori Sagawa, et al · 2023
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Measuring progress in fine-grained vision-and-language understanding
Emanuele Bugliarello, Laurent Sartran, Aishwarya Agrawal, Lisa Anne Hendricks, and Aida Nematzadeh · 2023
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Flashattention-2: Faster attention with better parallelism and work partitioning
Tri Dao · 2023
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Spqr: A sparse-quantized representation for near-lossless llm weight compression
Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, and Dan Alistarh · 2023
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
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Grounding language models to images for multimodal inputs and outputs
Jing Yu Koh, Ruslan Salakhutdinov, and Daniel Fried · 2023
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Mert Yuksekgonul, Federico Bianchi, Pratyusha Kalluri, Dan Jurafsky, and James Zou · 2023
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Instructblip: Towards general-purpose vision-language models with instruction tuning
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale N Fung, and Steven Hoi · 2024
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Evaluating linguistic capabilities of multimodal llms in the lens of few-shot learning
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Isobench: Benchmarking multimodal foundation models on isomorphic representations
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Beyond probabilities: Unveiling the misalignment in evaluating large language models
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MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
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On Measuring Faithfulness or Self-consistency of Natural Language Explanations
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
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Grok-1.5 vision
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mplug-owl3: Towards long image-sequence understanding in multi-modal large language models
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Vision-language models for vision tasks: A survey
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Judging llm-as-a-judge with mt-bench and chatbot arena
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