Papers

Learn more about AI2's Lasting Impact Award
Viewing 11-20 of 758 papers
  • Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks

    Akari Asai, Matt Gardner, Hannaneh HajishirziNAACL2022 Retrieval-augmented generation models have shown state-of-the-art performance across many knowledge-intensive NLP tasks such as open-domain question answering and fact verification. These models are trained to generate a final output given retrieved passages…
  • FaVIQ: FAct Verification from Information-seeking Questions

    Jungsoo Park, Sewon Min, Jaewoo Kang, Luke Zettlemoyer, Hannaneh HajishirziACL2022 Despite significant interest in developing general purpose fact checking models, it is challenging to construct a large-scale fact verification dataset with realistic real-world claims. Existing claims are either authored by crowdworkers, thereby introducing…
  • MetaICL: Learning to Learn In Context

    Sewon Min, M. Lewis, Luke Zettlemoyer, Hannaneh HajishirziNAACL2022 We introduce MetaICL (Meta-training for In-Context Learning), a new meta-training framework for few-shot learning where a pretrained language model is tuned to do in-context learning on a large set of training tasks. This meta-training enables the model to…
  • Noisy Channel Language Model Prompting for Few-Shot Text Classification

    Sewon Min, Michael Lewis, Hannaneh Hajishirzi, Luke ZettlemoyerACL2022 We introduce a noisy channel approach for language model prompting in few-shot text classification. Instead of computing the likelihood of the label given the input (referred as direct models), channel models compute the conditional probability of the input…
  • Robust fine-tuning of zero-shot models

    Mitchell Wortsman, Gabriel Ilharco, Mike Li, Jong Wook Kim, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig SchmidtCVPR2022
    Best Paper Finalist
    Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve…
  • Impact of Warmer Sea Surface Temperature on the Global Pattern of Intense Convection: Insights From a Global Storm Resolving Model

    K. Cheng, L. Harris, C. Bretherton, T. Merlis, M. Bolot, Linjiong Zhou, Alex Kaltenbaugh, S. Clark, S. FueglistalerGeophysical Research Letters2022 Intense convection (updrafts exceeding 10 m s−1) plays an essential role in severe weather and Earth's energy balance. Despite its importance, how the global pattern of intense convection changes in response to warmed climates remains unclear, as simulations…
  • Linear Adversarial Concept Erasure

    Shauli Ravfogel, Michael Twiton, Yoav Goldberg, Ryan CotterellICML2022 We formulate the problem of identifying and erasing a linear subspace that corresponds to a given concept, in order to prevent linear predictors from recovering the concept. We model this problem as a constrained, linear minimax game, and show that existing…
  • Dyna-bAbI: unlocking bAbI’s potential with dynamic synthetic benchmarking

    Ronen Tamari, Kyle Richardson, Aviad Sar-Shalom, Noam Kahlon, Nelson H S Liu, Reut Tsarfaty, Dafna Shahaf SEM2022 While neural language models often perform surprisingly well on natural language understanding (NLU) tasks, their strengths and limitations remain poorly understood. Controlled synthetic tasks are thus an increasingly important resource for diagnosing model…
  • Aligning to Social Norms and Values in Interactive Narratives

    Prithviraj Ammanabrolu, Liwei Jiang, Maarten Sap, Hannaneh Hajishirzi, Yejin ChoiNAACL2022 We focus on creating agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games—environments wherein an agent perceives and interacts with a world through natural language. Such interactive agents are…
  • Embedding Recycling for Language Models

    Jon Saad-Falcon, Amanpreet Singh, Luca Soldaini, Mike D'Arcy, Arman Cohan, Doug DowneyarXiv2022 Training and inference with large neural models is expensive. However, for many application domains, while new tasks and models arise frequently, the underlying doc-uments being modeled remain mostly un-changed. We study how to decrease computational cost in…