Abductive Reasoning is a task of inferring the most plausible hypothesis
given a set of observations. In literature, the community has approached to
solve this challenge by classifying/generating a likely hypothesis that does
not contradict with a past observation and future observation. Some of the most
well-known benchmarks that tackle this problem are aNLI and aNLG (pronounced as
alpha-NLI and alpha-NLG). In this report, I review over some of the
methodologies that were attempted to solve this challenge, re-implement the
baseline models, and analyze some of the weaknesses that current approaches
have. The code and the re-implemented results are available at this link.

本文为解决 “Abductive Reasoning” 提出了多种方法，再现了基准模型。通过此报告，发现目前的方法仍有一些局限性。

语言模型是否能够执行假设式常识推理？

Can Language Models perform Abductive Commonsense Reasoning?

We present a model of sense-making that greatly facilitates the collaboration
between an intelligent analyst and a knowledge-based agent. It is a general
model grounded in the science of evidence and the scientific method of
hypothesis generation and testing, where sense-making hypotheses that explain
an observation are generated, relevant evidence is then discovered, and the
hypotheses are tested based on the discovered evidence. We illustrate how the
model enables an analyst to directly instruct the agent to understand
situations involving the possible production of weapons (e.g., chemical warfare
agents) and how the agent becomes increasingly more competent in understanding
other situations from that domain (e.g., possible production of
centrifuge-enriched uranium or of stealth fighter aircraft).

本文提供了一种基于证据和科学方法的通用模型，能够帮助智能分析师和基于知识的代理之间的协作，通过生成解释性假设、发现相关证据，及基于模型测试假设，从而使代理能够理解和应对各种情况，例如包括可能涉及生产武器的情况，以及从该领域获得的其他相关情况。

人机协同感知共享模型

Shared Model of Sense-making for Human-Machine Collaboration

Suppose we have many copies of an unknown $n$-qubit state $\rho$. We measure
some copies of $\rho$ using a known two-outcome measurement $E_{1}$, then other
copies using a measurement $E_{2}$, and so on. At each stage $t$, we generate a
current hypothesis $\sigma_{t}$ about the state $\rho$, using the outcomes of
the previous measurements. We show that it is possible to do this in a way that
guarantees that $|\operatorname{Tr}(E_{i} \sigma_{t}) -
\operatorname{Tr}(E_{i}\rho) |$, the error in our prediction for the next
measurement, is at least $\varepsilon$ at most $\operatorname{O}\!\left(n /
\varepsilon^2 \right) $ times. Even in the "non-realizable" setting---where
there could be arbitrary noise in the measurement outcomes---we show how to
output hypothesis states that do significantly worse than the best possible
states at most $\operatorname{O}\!\left(\sqrt {Tn}\right) $ times on the first
$T$ measurements. These results generalize a 2007 theorem by Aaronson on the
PAC-learnability of quantum states, to the online and regret-minimization
settings. We give three different ways to prove our results---using convex
optimization, quantum postselection, and sequential fat-shattering
dimension---which have different advantages in terms of parameters and
portability.

该研究论文研究了如何通过量子状态的测量来生成假设，以指导下一次测量的选取，即减少答案预测失误率。