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Kellie Webster

Kellie Webster

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    Sparsely Activated Language Models are Efficient In-Context Learners
    Adams Yu
    Barret Richard Zoph
    Dmitry (Dima) Lepikhin
    Emma Wang
    Kun Zhang
    Liam B. Fedus
    Maarten Paul Bosma
    Marie Pellat
    Maxim Krikun
    Nan Du
    Simon Tong
    Tao Wang
    Toju Duke
    Yuanzhong Xu
    Zongwei Zhou
    (2022)
    Preview abstract Scaling language models with more data, compute and parameters has driven significant progress in natural language processing. For example, thanks to scaling, GPT-3 was able to achieve strong performance on few-shot learning. However, training these large dense models require significant amounts of computing resources. In this paper, we develop a family of sparsely activated mixture-of-expert language models named \glam (\textbf{G}eneralist \textbf{La}nguage \textbf{M}odel), which can have many more parameters but require significant less training cost than dense models. The largest \glam has 1.2 trillion parameters, which is approximately 7x larger than GPT-3 but can be trained more efficiently. With only 1/3 of energy consumption to train GPT-3, \glam achieves better overall performance on 29 zero-shot and one-shot NLP tasks. For example, \glam gets 75.0\% one-shot exact match accuracy on the TriviaQA test server, a significant improvement over 68.0\% obtained by GPT-3. View details
    Preview abstract Large language models (LLMs) have been shown to perform well in answering questions and in producing long-form texts such as stories and explanations, both in few-shot closed-book settings. While the former can be validated using well-known evaluation metrics, the latter is difficult to evaluate. To this end, we investigate the ability of LLMs to do both tasks at once -- to do question answering that requires long-form answers. Such questions tend to be multifaceted, i.e., they may have ambiguities and/or require information from multiple sources. To this end, we define query refinement prompts that encourage LLMs to explicitly express the multifacetedness in questions and generate long-form answers covering multiple facets of the question. Our experiments on two long-form question answering datasets, ASQA and AQuAMuSe, show that using our prompts allows us to outperform fully finetuned models in the closed book setting, as well as achieve results comparable to retrieve-then-generate open-book models. View details
    Preview abstract Large language models (LLMs) have shown impressive results across a variety of tasks while requiring little or no direct supervision. Further, there is mounting evidence that LLMs may have potential in information-seeking scenarios. We believe the ability of an LLM to attribute the text that it generates is likely to be crucial for both system developers and users in this setting. We propose and study Attributed QA as a key first step in the development of attributed LLMs. We develop a reproducable evaluation framework for the task, using human annotations as a gold standard and a correlated automatic metric that we show is suitable for development settings. We describe and benchmark a broad set of architectures for the task. Our contributions give some concrete answers to two key questions (How to measure attribution?, and How well do current state-of-the-art methods perform on attribution?), and give some hints as to how to address a third key question (How to build LLMs with attribution?). View details
    Preview abstract Question Answering (QA) tasks are used as benchmarks of general machine intelligence. Therefore, robust QA evaluation is critical, and metrics should indicate how models will answer _any_ question. However, major QA datasets have skewed distributions over gender, profession, and nationality. Despite that skew, models generalize---we find little evidence that accuracy is lower for people based on gender or nationality. Instead, there is more variation in question topic and question ambiguity. Adequately accessing the generalization of \abr{qa} systems requires more representative datasets. View details
    Preview abstract Research in natural language processing that focuses solely on binary genders can pose the serious danger of excluding communities and behaviors that are gender nonconforming. In this paper, we highlight the use of gender-inclusive language by proposing the task of rewriting gendered sentences in English to be gender-neutral using the \textit{singular they}. To this end, we train a Seq2Seq model for this task by creating a rewriting algorithm to generate a parallel dataset and evaluate performance on an annotated test set of 500 sentence-pairs (gendered to gender-neutral). Impressively, we are able to achieve over 99 BLEU and less than 1\% word error rate for both the algorithm and the model. Finally, we give some practical applications for this task, including machine translation and augmented writing. View details
    Preview abstract Machine translation systems with inadequate document understanding can make errors when translating dropped or neutral pronouns into languages with gendered pronouns (e.g., English). Predicting the underlying gender of these pronouns is difficult since it is not marked textually and must instead be inferred from coreferent mentions in the context. We propose a novel cross-lingual pivoting technique for automatically producing high-quality gender labels, and show that this data can be used to fine-tune a BERT classifier with 92% F1 for Spanish dropped feminine pronouns, compared with 30-51% for neural machine translation models and 54-71% for a non-fine-tuned BERT model. We augment a neural machine translation model with labels from our classifier to improve pronoun translation, while still having parallelizable translation models that translate a sentence at a time. View details
    Preview abstract Gender bias has been shown to affect many tasks applications in NLU. In the setting of machine translation (MT), research has primarily focused on measuring bias via synthetic datasets. We present an automatic method for identifying gender biases in MT using a novel-application of BERT-generated sentence perturbations. Using this method, we compile a dataset to serve as a benchmark for evaluating gender bias in MT across a diverse range of languages. Our dataset further serves to highlight the limitations of the current task definition which requires a single translation be produced, even in the presence of underspecified input. View details
    Underspecification Presents Challenges for Credibility in Modern Machine Learning
    Alexander Nicholas D'Amour
    Dan Moldovan
    Babak Alipanahi
    Alex Beutel
    Christina Chen
    Jon Deaton
    Jacob Eisenstein
    Shaobo Hou
    Ghassen Jerfel
    Yian Ma
    Akinori Mitani
    Andrea Montanari
    Christopher Nielsen
    Thomas Osborne
    Rajiv Raman
    Kim Ramasamy
    Rory Abbott Sayres
    Martin Gamunu Seneviratne
    Shannon Sequeira
    Harini Suresh
    Victor Veitch
    Journal of Machine Learning Research (2020)
    Preview abstract ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures. An ML pipeline is underspecified when it can return many predictors with equivalently strong held-out performance in the training domain. Underspecification is common in modern ML pipelines, such as those based on deep learning. Predictors returned by underspecified pipelines are often treated as equivalent based on their training domain performance, but we show here that such predictors can behave very differently in deployment domains. This ambiguity can lead to instability and poor model behavior in practice, and is a distinct failure mode from previously identified issues arising from structural mismatch between training and deployment domains. We show that this problem appears in a wide variety of practical ML pipelines, using examples from computer vision, medical imaging, natural language processing, clinical risk prediction based on electronic health records, and medical genomics. Our results show the need to explicitly account for underspecification in modeling pipelines that are intended for real-world deployment in any domain. View details
    How to Write a Bias Statement: Recommendations for Submissions to the Workshop on Gender Bias in NLP
    Christian Hardmeier
    Marta R. Costa-jussà
    Will Radford
    Su Lin Blodgett
    arXiv (2020)
    Preview abstract At the Workshop on Gender Bias in NLP (GeBNLP), we'd like to encourage authors to give explicit consideration to the wider aspects of bias and its social implications. For the 2020 edition of the workshop, we therefore requested that all authors include an explicit \emph{bias statement} in their work to clarify how their work relates to the social context in which NLP systems are used. The programme committee of the workshops included a number of reviewers with a background in the humanities and social sciences, in addition to NLP experts doing the bulk of the reviewing. Each paper was assigned one of those reviewers, and they were asked to pay specific attention to the provided bias statements in their reviews. This initiative was well received by the authors who submitted papers to the workshop, several of whom said they received useful suggestions and literature hints from the bias reviewers. We are therefore planning to keep this feature of the review process in future editions of the workshop. This document was originally published as a blog post on the web site of GeBNLP 2020. View details
    Preview abstract Building equitable and inclusive technologies demands paying attention to how social attitudes towards persons with disabilities are represented within technology. Representations perpetuated by NLP models often inadvertently encode undesirable social biases from the data on which they are trained. In this paper, first we present evidence of such undesirable biases towards mentions of disability in two different NLP models: toxicity prediction and sentiment analysis. Next, we demonstrate that neural embeddings that are critical first steps in most NLP pipelines also contain undesirable biases towards mentions of disabilities. We then expose the topical biases in the social discourse about some disabilities which may explain such biases in the models; for instance, terms related to gun violence, homelessness, and drug addiction are over-represented in discussions about mental illness. View details
    Preview abstract Large pre-trained models have revolutionized natural language understanding. However, researchers have found they can encode correlations undesired in many applications, like \emph{surgeon} being associated more with \emph{he} than \emph{she}. We explore such \emph{gendered correlations} as a case study, to learn how we can configure and train models to mitigate the risk of encoding unintended associations. We find that it is important to define correlation metrics, since they can reveal differences among models with similar accuracy. Large models have more capacity to encode gendered correlations, but this can be mitigated with general dropout regularization. Counterfactual data augmentation is also effective, and can even reduce correlations not explicitly targeted for mitigation, potentially making it useful beyond gender too. Both techniques yield models with comparable accuracy to unmitigated analogues, and still resist re-learning correlations in fine-tuning. View details
    GAP Shared Task Overview
    Marta R. Costa-jussà
    Christian Hardmeier
    Will Radford
    (2019)
    Preview abstract We overview a shared task we ran using the GAP coreference challenge, including the logistics of running a task with 263 active participants and the modeling trends we observed. We found that fine-tuning BERT with gender balanced data produced a fair model, which serves as a recommendation to the community about one way to approach fairness in NLP modeling. View details
    Preview abstract Persons with disabilities face many barriers to participation in society, and the rapid advancement of technology creates ever more. Achieving fair opportunity and justice for people with disabilities demands paying attention not just to accessibility, but also to the attitudes towards, and representations of, disability that are implicit in machine learning (ML) models that are pervasive in how one engages with the society. However such models often inadvertently learn to perpetuate undesirable social biases from the data on which they are trained. This can result, for example, in models for classifying text producing very different predictions for {\em I stand by a person with mental illness}, and {\em I stand by a tall person}. We present evidence of such social biases in existing ML models, along with an analysis of biases in a dataset used for model development. View details
    Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns
    Transactions of the Association for Computational Linguistics, vol. 6 (2018), pp. 605-618
    Preview abstract Coreference resolution is an important task for natural language understanding, and the resolution of ambiguous pronouns a longstanding challenge. Nonetheless, existing corpora do not capture ambiguous pronouns in sufficient volume or diversity to accurately indicate the practical utility of models. Furthermore, we find gender bias in existing corpora and systems favoring masculine entities. To address this, we present and release GAP, a gender-balanced labeled corpus of 8,908 ambiguous pronoun–name pairs sampled to provide diverse coverage of challenges posed by real-world text. We explore a range of baselines that demonstrate the complexity of the challenge, the best achieving just 66.9% F1. We show that syntactic structure and continuous neural models provide promising, complementary cues for approaching the challenge. View details
    A Challenge Set and Methods for Noun-Verb Ambiguity
    Ali Elkahky
    Emily Pitler
    Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2562-2572
    Preview abstract English part-of-speech taggers regularly make egregious errors related to noun-verb ambiguity, despite having achieved 97%+ accuracy on the WSJ Penn Treebank since 2002. These mistakes have been difficult to quantify and make taggers less useful to downstream tasks such as translation and text-to-speech synthesis. This paper creates a new dataset of over 30,000 naturally-occurring non-trivial examples of noun-verb ambiguity. Taggers within 1% of each other when measured on the WSJ have accuracies ranging from 57% to 75% accuracy on this challenge set. Enhancing the strongest existing tagger with contextual word embeddings and targeted training data improves its accuracy to 89%, a 14% absolute (52% relative) improvement. Downstream, using just this enhanced tagger yields a 28% reduction in error over the prior best learned model for homograph disambiguation for text-to-speech synthesis. View details
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