Despite their recent popularity and well-known advantages, dense retrievers
still lag behind sparse methods such as BM25 in their ability to reliably match
salient phrases and rare entities in the query and to generalize to
out-of-domain data. It has been argued that this is an inherent limitation of
dense models. We rebut this claim by introducing the Salient Phrase Aware
Retriever (SPAR), a dense retriever with the lexical matching capacity of a
sparse model. We show that a dense Lexical Model {\Lambda} can be trained to
imitate a sparse one, and SPAR is built by augmenting a standard dense
retriever with {\Lambda}. Empirically, SPAR shows superior performance on a
range of tasks including five question answering datasets, MS MARCO passage
retrieval, as well as the EntityQuestions and BEIR benchmarks for out-of-domain
evaluation, exceeding the performance of state-of-the-art dense and sparse
retrievers. The code and models of SPAR are available at:
this https URL

介绍了一种称为 Salient Phrase Aware Retriever (SPAR) 的密集型检索器，在保留稀疏模型的词法匹配能力的同时，通过加入类似于稀疏模型的词汇模型 Lambda 来提高其检索性能。SPAR 在多个任务上表现出卓越性能，包括问答数据集，MS MARCO 段落检索以及 EntityQuestions 和 BEIR 评估。

突出短语感知的密集检索：密集检索器是否能模仿稀疏检索器？

Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a  Sparse One?

Counterfactual explanations are usually obtained by identifying the smallest
change made to an input to change a prediction made by a fixed model (hereafter
called sparse methods). Recent work, however, has revitalized an old insight:
there often does not exist one superior solution to a prediction problem with
respect to commonly used measures of interest (e.g. error rate). In fact, often
multiple different classifiers give almost equal solutions. This phenomenon is
known as predictive multiplicity (Breiman, 2001; Marx et al., 2019). In this
work, we derive a general upper bound for the costs of counterfactual
explanations under predictive multiplicity. Most notably, it depends on a
discrepancy notion between two classifiers, which describes how differently
they treat negatively predicted individuals. We then compare sparse and data
support approaches empirically on real-world data. The results show that data
support methods are more robust to multiplicity of different models. At the
same time, we show that those methods have provably higher cost of generating
counterfactual explanations under one fixed model. In summary, our theoretical
and empiricaln results challenge the commonly held view that counterfactual
recommendations should be sparse in general.

本文研究了如何以更低的成本生成对于多个分类器表现相等情况下对于分类结果的对抗讲解，在理论和实证研究中，对于解释成本与解释鲁棒性进行了概念的阐述，发现基于数据支持的方法相较于仅仅基于稀疏方法更加鲁棒，但是它们在成本方面会有所提高。因此，缘由推荐为稀疏方法在多分类器表现差异大的情况下不宜一概而论。

针对预测多样性的反事实解释

On Counterfactual Explanations under Predictive Multiplicity

Sparse methods for supervised learning aim at finding good linear predictors
from as few variables as possible, i.e., with small cardinality of their
supports. This combinatorial selection problem is often turned into a convex
optimization problem by replacing the cardinality function by its convex
envelope (tightest convex lower bound), in this case the L1-norm. In this
paper, we investigate more general set-functions than the cardinality, that may
incorporate prior knowledge or structural constraints which are common in many
applications: namely, we show that for nondecreasing submodular set-functions,
the corresponding convex envelope can be obtained from its \lova extension, a
common tool in submodular analysis. This defines a family of polyhedral norms,
for which we provide generic algorithmic tools (subgradients and proximal
operators) and theoretical results (conditions for support recovery or
high-dimensional inference). By selecting specific submodular functions, we can
give a new interpretation to known norms, such as those based on
rank-statistics or grouped norms with potentially overlapping groups; we also
define new norms, in particular ones that can be used as non-factorial priors
for supervised learning.

本文研究了一种新的稀疏学习方法，提出了一种新的凸优化方法，利用一些子模函数的拓展得到了一类较通用的多面规范，并提供算法工具和理论结果。