Papers

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Viewing 1-10 of 222 papers
  • Complexity-Based Prompting for Multi-Step Reasoning

    Yao Fu, Hao-Chun Peng, Ashish Sabharwal, Peter Clark, Tushar KhotICLR2023 We study the task of prompting large-scale language models to perform multi-step reasoning. Existing work shows that when prompted with a chain of thoughts (CoT), sequences of short sentences describing intermediate reasoning steps towards a final answer…
  • Do Embodied Agents Dream of Pixelated Sheep?: Embodied Decision Making using Language Guided World Modelling

    Kolby Nottingham, Prithviraj Ammanabrolu, Alane Suhr, Yejin Choi, Hanna Hajishirzi, Sameer Singh, Roy FoxarXiv2023 Reinforcement learning (RL) agents typically learn tabula rasa, without prior knowledge of the world, which makes learning complex tasks with sparse rewards difficult. If initialized with knowledge of high-level subgoals and transitions between subgoals, RL…
  • Does progress on ImageNet transfer to real-world datasets?

    Alexander W. Fang, Simon Kornblith, Ludwig SchmidtarXiv2023 Does progress on ImageNet transfer to real-world datasets? We investigate this question by evaluating ImageNet pre-trained models with varying accuracy (57% - 83%) on six practical image classification datasets. In particular, we study datasets collected with…
  • Reproducible scaling laws for contrastive language-image learning

    Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, J. JitsevarXiv2022 Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set size, model size, and compute, which offers valuable guidance as large-scale…
  • Continued Pretraining for Better Zero- and Few-Shot Promptability

    Zhaofeng Wu, Robert L. Logan IV, Pete Walsh, Akshita Bhagia, Dirk Groeneveld, Sameer Singh, Iz BeltagyEMNLP2022 Recently introduced language model prompting methods can achieve high accuracy in zero-and few-shot settings while requiring few to no learned task-specific parameters. Never-theless, these methods still often trail behind full model finetuning. In this work…
  • Exploring The Landscape of Distributional Robustness for Question Answering Models

    Anas Awadalla, Mitchell Wortsman, Gabriel Ilharco, Sewon Min, Ian H. Magnusson, Hannaneh Hajishirzi, Ludwig SchmidtFindings of EMNLP2022 We conduct a large empirical evaluation to investigate the landscape of distributional robustness in question answering. Our investigation spans over 350 models and 16 question answering datasets, including a di-verse set of architectures, model sizes, and…
  • Hyperdecoders: Instance-specific decoders for multi-task NLP

    Hamish Ivison, Matthew E. PetersFindings of EMNLP2022 We investigate input-conditioned hypernetworks for multi-tasking in NLP, generating parameter-efficient adaptations for a decoder using a hypernetwork conditioned on the output of an encoder. This approach produces a unique decoder for every input instance…
  • GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation

    Daniel Khashabi, Gabriel Stanovsky, Jonathan Bragg, Nicholas Lourie, Jungo Kasai, Yejin Choi, Noah A. Smith, Daniel S. WeldEMNLP2022 While often assumed a gold standard, effective human evaluation of text generation remains an important, open area for research. We revisit this problem with a focus on pro-ducing consistent evaluations that are reproducible —over time and across different…
  • How Much Does Attention Actually Attend? Questioning the Importance of Attention in Pretrained Transformers

    Michael Hassid, Hao Peng, Daniel Rotem, Jungo Kasai, Ivan Montero, Noah Smith, Roy SchwartzEMNLP Findings2022 The attention mechanism is considered the backbone of the widely-used Transformer architecture. It contextualizes the input by computing input-specific attention matrices. We find that this mechanism, while powerful and elegant, is not as important as…
  • In-Context Learning for Few-Shot Dialogue State Tracking

    Yushi Hu, Chia-Hsuan Lee, Tianbao Xie, Tao Yu, Noah A. Smith, Mari OstendorfEMNLP Findings2022 Collecting and annotating task-oriented dialogues is time-consuming and costly. Thus, zero and few shot learning for dialogue tasks presents an exciting opportunity. In this work, we propose an in-context (IC) learning framework for zero-shot and few-shot…