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Paloma: A Benchmark for Evaluating Language Model Fit
Ian Magnusson, Akshita Bhagia, Valentin Hofmann, Luca Soldaini, A. Jha, Oyvind Tafjord, Dustin Schwenk, Evan Pete Walsh, Yanai Elazar, Kyle Lo, Dirk Groeneveld, Iz Beltagy, Hanna Hajishirzi, Noah A. Smith, Kyle Richardson, Jesse DodgearXiv • 2023 Language models (LMs) commonly report perplexity on monolithic data held out from training. Implicitly or explicitly, this data is composed of domains$\unicode{x2013}$varying distributions of language. Rather than assuming perplexity on one distribution…Catwalk: A Unified Language Model Evaluation Framework for Many Datasets
Dirk Groeneveld, Anas Awadalla, Iz Beltagy, Akshita Bhagia, Ian Magnusson, Hao Peng, Oyvind Tafjord, Pete Walsh, Kyle Richardson, Jesse DodgearXiv.org • 2023 The success of large language models has shifted the evaluation paradigms in natural language processing (NLP). The community's interest has drifted towards comparing NLP models across many tasks, domains, and datasets, often at an extreme scale. This imposes…IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions
Wenhao Yu, Meng Jiang, Peter Clark, Ashish SabharwalEMNLP • 2023 Although counterfactual reasoning is a fundamental aspect of intelligence, the lack of large-scale counterfactual open-domain question-answering (QA) benchmarks makes it difficult to evaluate and improve models on this ability. To address this void, we…Self-Refine: Iterative Refinement with Self-Feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, K. Hermann, S. Welleck, A. Yazdanbakhsh, Peter ClarkNeurIPS • 2023 Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback…A Logic for Expressing Log-Precision Transformers
William Merrill, Ashish SabharwalNeurIPS • 2023 One way to interpret the reasoning power of transformer-based language models is to describe the types of logical rules they can resolve over some input text. Recently, Chiang et al. (2023) showed that finite-precision transformers can be equivalently…How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
Yizhong Wang, Hamish Ivison, Pradeep Dasigi, Jack Hessel, Tushar Khot, Khyathi Raghavi Chandu, David Wadden, Kelsey MacMillan, Noah A. Smith, Iz Beltagy, Hanna HajishirziNeurIPS • 2023 In this work we explore recent advances in instruction-tuning language models on a range of open instruction-following datasets. Despite recent claims that open models can be on par with state-of-the-art proprietary models, these claims are often accompanied…Editing Common Sense in Transformers
Anshita Gupta*, Debanjan Mondal*, Akshay Krishna Sheshadri*, Wenlong Zhao, Xiang Lorraine Li*, Sarah Wiegreffe*, Niket Tandon*EMNLP • 2023 Editing model parameters directly in Transformers makes updating open-source transformer-based models possible without re-training. However, these editing methods have only been evaluated on statements about encyclopedic knowledge with a single correct answer…Increasing Probability Mass on Answer Choices Does Not Always Improve Accuracy
Sarah Wiegreffe, Matthew Finlayson, Oyvind Tafjord, Peter Clark, Ashish SabharwalEMNLP • 2023 When pretrained language models (LMs) are applied to discriminative tasks such as multiple-choice questions, they place probability mass on vocabulary tokens that aren't among the given answer choices. Spreading probability mass across multiple surface forms…Language Models with Rationality
Nora Kassner, Oyvind Tafjord, Ashish Sabharwal, Kyle Richardson, Hinrich Schütze, Peter ClarkEMNLP • 2023 While large language models (LLMs) are proficient at question-answering (QA), the dependencies between their answers and other "beliefs" they may have about the world are typically unstated, and may even be in conflict. Our goal is to uncover such…Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals
Yanai Elazar, Bhargavi Paranjape, Hao Peng, Sarah Wiegreffe, Khyathi Raghavi, Vivek Srikumar, Sameer Singh, Noah A. SmitharXiv • 2023 The inevitable appearance of spurious correlations in training datasets hurts the generalization of NLP models on unseen data. Previous work has found that datasets with paired inputs are prone to correlations between a specific part of the input (e.g., the…