10 research outputs found
GEMv2 : Multilingual NLG benchmarking in a single line of code
Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using leaderboards, but the evaluation choices become sub-optimal as better alternatives arise. This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims. To make following best model evaluation practices easier, we introduce GEMv2. The new version of the Generation, Evaluation, and Metrics Benchmark introduces a modular infrastructure for dataset, model, and metric developers to benefit from each others work. GEMv2 supports 40 documented datasets in 51 languages. Models for all datasets can be evaluated online and our interactive data card creation and rendering tools make it easier to add new datasets to the living benchmark.Peer reviewe
GEMv2 : Multilingual NLG benchmarking in a single line of code
Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using leaderboards, but the evaluation choices become sub-optimal as better alternatives arise. This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims. To make following best model evaluation practices easier, we introduce GEMv2. The new version of the Generation, Evaluation, and Metrics Benchmark introduces a modular infrastructure for dataset, model, and metric developers to benefit from each others work. GEMv2 supports 40 documented datasets in 51 languages. Models for all datasets can be evaluated online and our interactive data card creation and rendering tools make it easier to add new datasets to the living benchmark.Peer reviewe
Neural Language Generation for Content Adaptation: Explainable, Efficient Low-Resource Text Simplification and Evaluation
There are rich opportunities to reduce the language complexity of professional content (either human-written or computer-generated) and make it accessible to a broad audience. As a sub-task of natural language generation (NLG), text simplification has considerable potential to improve the fairness and transparency of text information systems.
Recent approaches to text simplification usually complete the task in an end-to-end fashion, employing neural machine translation models in a monolingual setting regardless of the type of simplifications to be done. These models are limited on the one hand due to the absence of large-scale parallel (complex → simple) monolingual training data, and on the other hand due to the lack of interpretability of their black-box procedures. Furthermore, despite fast development of algorithms, there is an urgency to fill the huge gap in evaluating NLG systems in general (including text simplification systems). Indeed, given no clear model of text quality and no agreed objective criterion for comparing the “goodness of texts”, the evaluation of NLG systems is inherently difficult. The present work addresses these problems: i) sample-efficient approaches to NLG that improve the fairness and transparency of text information systems by adapting their content to the literacy level of the target audience, ii) systematic analysis of evaluation metrics for NLG models informed by theory and empirical evidence.
In particular, we show that text simplification can be decomposed into a compact pipeline of tasks to ensure the transparency and explainability of the process; low-resource text simplification can be framed from a task and domain adaptation perspective which can be decomposed into multiple adaptation steps via meta-learning and transfer learning; and evaluators for NLG can be evaluated at scale and compared with human judgements. Beyond the problem of low-resource text simplification, the methodology proposed in this dissertation (explainable decomposition, chain of adaptations to new tasks and domains, and meta-evaluation) may benefit other research areas related to generative artificial intelligence (AI).PhDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/178028/1/garbacea_1.pd
Why is constrained neural language generation particularly challenging?
Recent advances in deep neural language models combined with the capacity of
large scale datasets have accelerated the development of natural language
generation systems that produce fluent and coherent texts (to various degrees
of success) in a multitude of tasks and application contexts. However,
controlling the output of these models for desired user and task needs is still
an open challenge. This is crucial not only to customizing the content and
style of the generated language, but also to their safe and reliable deployment
in the real world. We present an extensive survey on the emerging topic of
constrained neural language generation in which we formally define and
categorize the problems of natural language generation by distinguishing
between conditions and constraints (the latter being testable conditions on the
output text instead of the input), present constrained text generation tasks,
and review existing methods and evaluation metrics for constrained text
generation. Our aim is to highlight recent progress and trends in this emerging
field, informing on the most promising directions and limitations towards
advancing the state-of-the-art of constrained neural language generation
research.Comment: This survey is specifically focused on constrained neural language
generation. For a more general survey of NLG literature, please see "Neural
language generation: Formulation, methods, and evaluation" at
arXiv:2007.1578
The GEM Benchmark:Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG),
its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly
evolving ecosystem of automated metrics, datasets, and human evaluation
standards. Due to this moving target, new models often still evaluate on
divergent anglo-centric corpora with well-established, but flawed, metrics.
This disconnect makes it challenging to identify the limitations of current
models and opportunities for progress. Addressing this limitation, GEM provides
an environment in which models can easily be applied to a wide set of tasks and
in which evaluation strategies can be tested. Regular updates to the benchmark
will help NLG research become more multilingual and evolve the challenge
alongside models. This paper serves as the description of the data for which we
are organizing a shared task at our ACL 2021 Workshop and to which we invite
the entire NLG community to participate
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Language models demonstrate both quantitative improvement and new qualitative
capabilities with increasing scale. Despite their potentially transformative
impact, these new capabilities are as yet poorly characterized. In order to
inform future research, prepare for disruptive new model capabilities, and
ameliorate socially harmful effects, it is vital that we understand the present
and near-future capabilities and limitations of language models. To address
this challenge, we introduce the Beyond the Imitation Game benchmark
(BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442
authors across 132 institutions. Task topics are diverse, drawing problems from
linguistics, childhood development, math, common-sense reasoning, biology,
physics, social bias, software development, and beyond. BIG-bench focuses on
tasks that are believed to be beyond the capabilities of current language
models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense
transformer architectures, and Switch-style sparse transformers on BIG-bench,
across model sizes spanning millions to hundreds of billions of parameters. In
addition, a team of human expert raters performed all tasks in order to provide
a strong baseline. Findings include: model performance and calibration both
improve with scale, but are poor in absolute terms (and when compared with
rater performance); performance is remarkably similar across model classes,
though with benefits from sparsity; tasks that improve gradually and
predictably commonly involve a large knowledge or memorization component,
whereas tasks that exhibit "breakthrough" behavior at a critical scale often
involve multiple steps or components, or brittle metrics; social bias typically
increases with scale in settings with ambiguous context, but this can be
improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo:
https://github.com/google/BIG-benc