94 research outputs found
Question Decomposition Tree for Answering Complex Questions over Knowledge Bases
Knowledge base question answering (KBQA) has attracted a lot of interest in
recent years, especially for complex questions which require multiple facts to
answer. Question decomposition is a promising way to answer complex questions.
Existing decomposition methods split the question into sub-questions according
to a single compositionality type, which is not sufficient for questions
involving multiple compositionality types. In this paper, we propose Question
Decomposition Tree (QDT) to represent the structure of complex questions.
Inspired by recent advances in natural language generation (NLG), we present a
two-staged method called Clue-Decipher to generate QDT. It can leverage the
strong ability of NLG model and simultaneously preserve the original questions.
To verify that QDT can enhance KBQA task, we design a decomposition-based KBQA
system called QDTQA. Extensive experiments show that QDTQA outperforms previous
state-of-the-art methods on ComplexWebQuestions dataset. Besides, our
decomposition method improves an existing KBQA system by 12% and sets a new
state-of-the-art on LC-QuAD 1.0.Comment: Accepted by AAAI202
The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommendations
Large Language Models (LLMs) have seen widespread deployment in various
real-world applications. Understanding these biases is crucial to comprehend
the potential downstream consequences when using LLMs to make decisions,
particularly for historically disadvantaged groups. In this work, we propose a
simple method for analyzing and comparing demographic bias in LLMs, through the
lens of job recommendations. We demonstrate the effectiveness of our method by
measuring intersectional biases within ChatGPT and LLaMA, two cutting-edge
LLMs. Our experiments primarily focus on uncovering gender identity and
nationality bias; however, our method can be extended to examine biases
associated with any intersection of demographic identities. We identify
distinct biases in both models toward various demographic identities, such as
both models consistently suggesting low-paying jobs for Mexican workers or
preferring to recommend secretarial roles to women. Our study highlights the
importance of measuring the bias of LLMs in downstream applications to
understand the potential for harm and inequitable outcomes.Comment: Accepted to EAAMO 202
Transformation Model With Constraints for High Accuracy of 2D-3D Building Registration in Aerial Imagery
This paper proposes a novel rigorous transformation model for 2D-3D registration to address the difficult problem of obtaining a sufficient number of well-distributed ground control points (GCPs) in urban areas with tall buildings. The proposed model applies two types of geometric constraints, co-planarity and perpendicularity, to the conventional photogrammetric collinearity model. Both types of geometric information are directly obtained from geometric building structures, with which the geometric constraints are automatically created and combined into the conventional transformation model. A test field located in downtown Denver, Colorado, is used to evaluate the accuracy and reliability of the proposed method. The comparison analysis of the accuracy achieved by the proposed method and the conventional method is conducted. Experimental results demonstrated that: (1) the theoretical accuracy of the solved registration parameters can reach 0.47 pixels, whereas the other methods reach only 1.23 and 1.09 pixels; (2) the RMS values of 2D-3D registration achieved by the proposed model are only two pixels along the x and y directions, much smaller than the RMS values of the conventional model, which are approximately 10 pixels along the x and y directions. These results demonstrate that the proposed method is able to significantly improve the accuracy of 2D-3D registration with much fewer GCPs in urban areas with tall buildings
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