5 research outputs found
LoHoRavens: A Long-Horizon Language-Conditioned Benchmark for Robotic Tabletop Manipulation
The convergence of embodied agents and large language models (LLMs) has
brought significant advancements to embodied instruction following.
Particularly, the strong reasoning capabilities of LLMs make it possible for
robots to perform long-horizon tasks without expensive annotated
demonstrations. However, public benchmarks for testing the long-horizon
reasoning capabilities of language-conditioned robots in various scenarios are
still missing. To fill this gap, this work focuses on the tabletop manipulation
task and releases a simulation benchmark, \textit{LoHoRavens}, which covers
various long-horizon reasoning aspects spanning color, size, space, arithmetics
and reference. Furthermore, there is a key modality bridging problem for
long-horizon manipulation tasks with LLMs: how to incorporate the observation
feedback during robot execution for the LLM's closed-loop planning, which is
however less studied by prior work. We investigate two methods of bridging the
modality gap: caption generation and learnable interface for incorporating
explicit and implicit observation feedback to the LLM, respectively. These
methods serve as the two baselines for our proposed benchmark. Experiments show
that both methods struggle to solve some tasks, indicating long-horizon
manipulation tasks are still challenging for current popular models. We expect
the proposed public benchmark and baselines can help the community develop
better models for long-horizon tabletop manipulation tasks.Comment: 6 pages, 4 figures. The video and code of LoHoRavens are available at
https://cisnlp.github.io/lohoravens-webpage
Öncel I F-16 projesi ve Türk Havacılık sanayisine katkıları (1987-1995)
Ankara : İhsan Doğramacı Bilkent Üniversitesi İktisadi, İdari ve Sosyal Bilimler Fakültesi, Tarih Bölümü, 2014.This work is a student project of the The Department of History, Faculty of Economics, Administrative and Social Sciences, İhsan Doğramacı Bilkent University.by Erkoç, Seda
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