Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models
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Song-Chun Zhu | Y. Wu | Kai-Wei Chang | Michel Galley | Hao Cheng | Jianfeng Gao | Baolin Peng | Pan Lu
[1] Yong Jae Lee,et al. Visual Instruction Tuning , 2023, ArXiv.
[2] Chunyuan Li,et al. Instruction Tuning with GPT-4 , 2023, ArXiv.
[3] Bodhisattwa Prasad Majumder,et al. Self-Refine: Iterative Refinement with Self-Feedback , 2023, 2303.17651.
[4] Xu Tan,et al. HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace , 2023, ArXiv.
[5] Hongsheng Li,et al. LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention , 2023, ArXiv.
[6] Faisal Ahmed,et al. MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action , 2023, ArXiv.
[7] Marco Tulio Ribeiro,et al. ART: Automatic multi-step reasoning and tool-use for large language models , 2023, ArXiv.
[8] Carl Vondrick,et al. ViperGPT: Visual Inference via Python Execution for Reasoning , 2023, 2023 IEEE/CVF International Conference on Computer Vision (ICCV).
[9] Chenfei Wu,et al. Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models , 2023, ArXiv.
[10] Shima Imani,et al. MathPrompter: Mathematical Reasoning using Large Language Models , 2023, ACL.
[11] Naman Goyal,et al. LLaMA: Open and Efficient Foundation Language Models , 2023, ArXiv.
[12] Michel Galley,et al. Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback , 2023, ArXiv.
[13] Luke Zettlemoyer,et al. Toolformer: Language Models Can Teach Themselves to Use Tools , 2023, NeurIPS.
[14] Alexander J. Smola,et al. Multimodal Chain-of-Thought Reasoning in Language Models , 2023, ArXiv.
[15] Kai-Wei Chang,et al. A Survey of Deep Learning for Mathematical Reasoning , 2022, ArXiv.
[16] William W. Cohen,et al. Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks , 2022, ArXiv.
[17] Aniruddha Kembhavi,et al. Visual Programming: Compositional visual reasoning without training , 2022, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Jamie Callan,et al. PAL: Program-aided Language Models , 2022, ICML.
[19] Alexander M. Rush,et al. BLOOM: A 176B-Parameter Open-Access Multilingual Language Model , 2022, ArXiv.
[20] Oyvind Tafjord,et al. LILA: A Unified Benchmark for Mathematical Reasoning , 2022, EMNLP.
[21] Heng Ji,et al. Code4Struct: Code Generation for Few-Shot Structured Prediction from Natural Language , 2022, ArXiv.
[22] Andrew M. Dai,et al. Scaling Instruction-Finetuned Language Models , 2022, ArXiv.
[23] Song-Chun Zhu,et al. Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning , 2022, ICLR.
[24] Dan Iter,et al. Generate rather than Retrieve: Large Language Models are Strong Context Generators , 2022, ICLR.
[25] Song-Chun Zhu,et al. Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering , 2022, NeurIPS.
[26] Peter R. Florence,et al. Inner Monologue: Embodied Reasoning through Planning with Language Models , 2022, CoRL.
[27] J. Dean,et al. Emergent Abilities of Large Language Models , 2022, Trans. Mach. Learn. Res..
[28] S. Gu,et al. Large Language Models are Zero-Shot Reasoners , 2022, NeurIPS.
[29] Andrew M. Dai,et al. PaLM: Scaling Language Modeling with Pathways , 2022, J. Mach. Learn. Res..
[30] Dale Schuurmans,et al. Chain of Thought Prompting Elicits Reasoning in Large Language Models , 2022, NeurIPS.
[31] Eric Nyberg,et al. Open Domain Question Answering with A Unified Knowledge Interface , 2021, ACL.
[32] Qian Liu,et al. TAPEX: Table Pre-training via Learning a Neural SQL Executor , 2021, ICLR.
[33] Jason Weston,et al. Internet-Augmented Dialogue Generation , 2021, ACL.
[34] Jeff Wu,et al. WebGPT: Browser-assisted question-answering with human feedback , 2021, ArXiv.
[35] Song-Chun Zhu,et al. IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning , 2021, NeurIPS Datasets and Benchmarks.
[36] Wojciech Zaremba,et al. Evaluating Large Language Models Trained on Code , 2021, ArXiv.
[37] Song-Chun Zhu,et al. Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning , 2021, ACL.
[38] Navin Goyal,et al. Are NLP Models really able to Solve Simple Math Word Problems? , 2021, NAACL.
[39] Wonjae Kim,et al. ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision , 2021, ICML.
[40] Cho-Jui Hsieh,et al. What Does BERT with Vision Look At? , 2020, ACL.
[41] Mark Chen,et al. Language Models are Few-Shot Learners , 2020, NeurIPS.
[42] Hannaneh Hajishirzi,et al. UnifiedQA: Crossing Format Boundaries With a Single QA System , 2020, FINDINGS.
[43] Cho-Jui Hsieh,et al. VisualBERT: A Simple and Performant Baseline for Vision and Language , 2019, ArXiv.
[44] Zhou Yu,et al. Deep Modular Co-Attention Networks for Visual Question Answering , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Peng Gao,et al. Dynamic Fusion With Intra- and Inter-Modality Attention Flow for Visual Question Answering , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Chuang Gan,et al. Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding , 2018, NeurIPS.
[47] Trevor Darrell,et al. Explainable Neural Computation via Stack Neural Module Networks , 2018, ECCV.
[48] Byoung-Tak Zhang,et al. Bilinear Attention Networks , 2018, NeurIPS.
[49] Lei Zhang,et al. Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[50] Li Fei-Fei,et al. Inferring and Executing Programs for Visual Reasoning , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[51] Trevor Darrell,et al. Learning to Reason: End-to-End Module Networks for Visual Question Answering , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[52] Dan Klein,et al. Learning to Compose Neural Networks for Question Answering , 2016, NAACL.
[53] Dan Klein,et al. Neural Module Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[54] Ronald J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.