Despite the successes of pretrained language models, there are still few
high-quality, general-purpose QA systems that are freely available. In
response, we present Macaw, a versatile, generative question-answering (QA)
system that we are making available to the community. Macaw is built on
UnifiedQA, itself built on T5, and exhibits strong performance, zero-shot, on a
wide variety of topics, including outperforming GPT-3 by over 10% (absolute) on
Challenge300, a suite of 300 challenge questions, despite being an order of
magnitude smaller (11 billion vs. 175 billion parameters). In addition, Macaw
allows different permutations ("angles") of its inputs and outputs to be used,
for example Macaw can take a question and produce an answer; or take an answer
and produce a question; or take an answer and question, and produce
multiple-choice options. We describe the system, and illustrate a variety of
question types where it produces surprisingly good answers, well outside the
training setup. We also identify question classes where it still appears to
struggle, offering insights into the limitations of pretrained language models.
Macaw is freely available, and we hope that it proves useful to the community.
Macaw is available at this https URL

介绍了一种基于预训练语言模型的通用生成式问答系统 ——Macaw，该系统可以灵活处理问题和答案的输入输出，并在大量主题上表现良好，但仍存在一些问题需要解决。

Macaw 实现通用问题解答

General-Purpose Question-Answering with Macaw

This paper introduces the offline meta-reinforcement learning (offline
meta-RL) problem setting and proposes an algorithm that performs well in this
setting. Offline meta-RL is analogous to the widely successful supervised
learning strategy of pre-training a model on a large batch of fixed,
pre-collected data (possibly from various tasks) and fine-tuning the model to a
new task with relatively little data. That is, in offline meta-RL, we
meta-train on fixed, pre-collected data from several tasks in order to adapt to
a new task with a very small amount (less than 5 trajectories) of data from the
new task. By nature of being offline, algorithms for offline meta-RL can
utilize the largest possible pool of training data available and eliminate
potentially unsafe or costly data collection during meta-training. This setting
inherits the challenges of offline RL, but it differs significantly because
offline RL does not generally consider a) transfer to new tasks or b) limited
data from the test task, both of which we face in offline meta-RL. Targeting
the offline meta-RL setting, we propose Meta-Actor Critic with Advantage
Weighting (MACAW), an optimization-based meta-learning algorithm that uses
simple, supervised regression objectives for both the inner and outer loop of
meta-training. On offline variants of common meta-RL benchmarks, we empirically
find that this approach enables fully offline meta-reinforcement learning and
achieves notable gains over prior methods.

本文介绍了离线元强化学习设置，并提出了一个能在该设置中表现优异的算法。我们提出了用于内外循环的简单监督回归目标的基于优化的元学习算法，称为 Meta-Actor Critic with Advantage Weighting (MACAW)。在常见的元 RL 基准的离线变量上，我们通过实验发现该方法能够实现完全离线元强化学习，并且比之前的方法有显着的提高。

基于优势权重的离线元强化学习

Offline Meta-Reinforcement Learning with Advantage Weighting

Conversational information seeking (CIS) has been recognized as a major
emerging research area in information retrieval. Such research will require
data and tools, to allow the implementation and study of conversational
systems. This paper introduces Macaw, an open-source framework with a modular
architecture for CIS research. Macaw supports multi-turn, multi-modal, and
mixed-initiative interactions, and enables research for tasks such as document
retrieval, question answering, recommendation, and structured data exploration.
It has a modular design to encourage the study of new CIS algorithms, which can
be evaluated in batch mode. It can also integrate with a user interface, which
allows user studies and data collection in an interactive mode, where the back
end can be fully algorithmic or a wizard of oz setup. Macaw is distributed
under the MIT License.

本文介绍了一种模块化架构的开源框架 Macaw，用于实现和研究会话式信息检索的多轮、多模态和混合交互，并支持文档检索、问答、推荐和结构化数据探索等任务的算法研究和批量模式评估，可以与用户接口进行集成，其后端可以是完全算法化或巫师 Oz 设置。