8 research outputs found
Do Transformer Attention Heads Provide Transparency in Abstractive Summarization?
Learning algorithms become more powerful, often at the cost of increased
complexity. In response, the demand for algorithms to be transparent is
growing. In NLP tasks, attention distributions learned by attention-based deep
learning models are used to gain insights in the models' behavior. To which
extent is this perspective valid for all NLP tasks? We investigate whether
distributions calculated by different attention heads in a transformer
architecture can be used to improve transparency in the task of abstractive
summarization. To this end, we present both a qualitative and quantitative
analysis to investigate the behavior of the attention heads. We show that some
attention heads indeed specialize towards syntactically and semantically
distinct input. We propose an approach to evaluate to which extent the
Transformer model relies on specifically learned attention distributions. We
also discuss what this implies for using attention distributions as a means of
transparency.Comment: To appear at FACTS-IR 2019, SIGI
IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022
Human intelligence has the remarkable ability to adapt to new tasks and
environments quickly. Starting from a very young age, humans acquire new skills
and learn how to solve new tasks either by imitating the behavior of others or
by following provided natural language instructions. To facilitate research in
this direction, we propose IGLU: Interactive Grounded Language Understanding in
a Collaborative Environment. The primary goal of the competition is to approach
the problem of how to develop interactive embodied agents that learn to solve a
task while provided with grounded natural language instructions in a
collaborative environment. Understanding the complexity of the challenge, we
split it into sub-tasks to make it feasible for participants.
This research challenge is naturally related, but not limited, to two fields
of study that are highly relevant to the NeurIPS community: Natural Language
Understanding and Generation (NLU/G) and Reinforcement Learning (RL).
Therefore, the suggested challenge can bring two communities together to
approach one of the crucial challenges in AI. Another critical aspect of the
challenge is the dedication to perform a human-in-the-loop evaluation as a
final evaluation for the agents developed by contestants.Comment: arXiv admin note: text overlap with arXiv:2110.0653
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