In this thesis, we investigated the relevance, faithfulness, and succinctness
aspects of Long Form Question Answering (LFQA). LFQA aims to generate an
in-depth, paragraph-length answer for a given question, to help bridge the gap
between real scenarios and the existing open-domain QA models which can only
extract short-span answers. LFQA is quite challenging and under-explored. Few
works have been done to build an effective LFQA system. It is even more
challenging to generate a good-quality long-form answer relevant to the query
and faithful to facts, since a considerable amount of redundant, complementary,
or contradictory information will be contained in the retrieved documents.
Moreover, no prior work has been investigated to generate succinct answers. We
are among the first to research the LFQA task. We pioneered the research
direction to improve the answer quality in terms of 1) query-relevance, 2)
answer faithfulness, and 3) answer succinctness.

本论文研究了长篇问答系统的相关性、可靠性和简洁性，旨在改进问题相关性、答案可靠性和简洁性等方面的答案质量。

生成式长篇问答：相关性、忠实度和简洁性

Generative Long-form Question Answering: Relevance, Faithfulness and Succinctness

We propose a perspective on knowledge compilation which calls for analyzing
different compilation approaches according to two key dimensions: the
succinctness of the target compilation language, and the class of queries and
transformations that the language supports in polytime. We then provide a
knowledge compilation map, which analyzes a large number of existing target
compilation languages according to their succinctness and their polytime
transformations and queries. We argue that such analysis is necessary for
placing new compilation approaches within the context of existing ones. We also
go beyond classical, flat target compilation languages based on CNF and DNF,
and consider a richer, nested class based on directed acyclic graphs (such as
OBDDs), which we show to include a relatively large number of target
compilation languages.

本文提供了一种知识编译的角度，分析了不同的编译方法（依据目标编译语言的简洁性与语言在多项式时间内支持的查询和转换的类别），并提供了一张知识编译图使新的编译方法能在已有方法的背景下进行分析。