472 research outputs found
Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering
Generative question answering (QA) models generate answers to questions
either solely based on the parameters of the model (the closed-book setting) or
additionally retrieving relevant evidence (the open-book setting). Generative
QA models can answer some relatively complex questions, but the mechanism
through which they do so is still poorly understood. We perform several studies
aimed at better understanding the multi-hop reasoning capabilities of
generative QA models. First, we decompose multi-hop questions into multiple
corresponding single-hop questions, and find marked inconsistency in QA models'
answers on these pairs of ostensibly identical question chains. Second, we find
that models lack zero-shot multi-hop reasoning ability: when trained only on
single-hop questions, models generalize poorly to multi-hop questions. Finally,
we demonstrate that it is possible to improve models' zero-shot multi-hop
reasoning capacity through two methods that approximate real multi-hop natural
language (NL) questions by training on either concatenation of single-hop
questions or logical forms (SPARQL). In sum, these results demonstrate that
multi-hop reasoning does not emerge naturally in generative QA models, but can
be encouraged by advances in training or modeling techniques.Comment: COLING 202
Recent progresses in outcome-dependent sampling with failure time data
An outcome-dependent sampling (ODS) design is a retrospective sampling scheme where one observes the primary exposure variables with a probability that depends on the observed value of the outcome variable. When the outcome of interest is failure time, the observed data are often censored. By allowing the selection of the supplemental samples depends on whether the event of interest happens or not and oversampling subjects from the most informative regions, ODS design for the time-to-event data can reduce the cost of the study and improve the efficiency. We review recent progresses and advances in research on ODS designs with failure time data. This includes researches on ODS related designs like case–cohort design, generalized case–cohort design, stratified case–cohort design, general failure-time ODS design, length-biased sampling design and interval sampling design
Fault Tolerant Free Gait and Footstep Planning for Hexapod Robot Based on Monte-Carlo Tree
Legged robots can pass through complex field environments by selecting gaits
and discrete footholds carefully. Traditional methods plan gait and foothold
separately and treat them as the single-step optimal process. However, such
processing causes its poor passability in a sparse foothold environment. This
paper novelly proposes a coordinative planning method for hexapod robots that
regards the planning of gait and foothold as a sequence optimization problem
with the consideration of dealing with the harshness of the environment as leg
fault. The Monte Carlo tree search algorithm(MCTS) is used to optimize the
entire sequence. Two methods, FastMCTS, and SlidingMCTS are proposed to solve
some defeats of the standard MCTS applicating in the field of legged robot
planning. The proposed planning algorithm combines the fault-tolerant gait
method to improve the passability of the algorithm. Finally, compared with
other planning methods, experiments on terrains with different densities of
footholds and artificially-designed challenging terrain are carried out to
verify our methods. All results show that the proposed method dramatically
improves the hexapod robot's ability to pass through sparse footholds
environment
- …