796 research outputs found
Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking
Zero-shot Dialogue State Tracking (DST) addresses the challenge of acquiring
and annotating task-oriented dialogues, which can be time-consuming and costly.
However, DST extends beyond simple slot-filling and requires effective updating
strategies for tracking dialogue state as conversations progress. In this
paper, we propose ParsingDST, a new In-Context Learning (ICL) method, to
introduce additional intricate updating strategies in zero-shot DST. Our
approach reformulates the DST task by leveraging powerful Large Language Models
(LLMs) and translating the original dialogue text to JSON through semantic
parsing as an intermediate state. We also design a novel framework that
includes more modules to ensure the effectiveness of updating strategies in the
text-to-JSON process. Experimental results demonstrate that our approach
outperforms existing zero-shot DST methods on MultiWOZ, exhibiting significant
improvements in Joint Goal Accuracy (JGA) and slot accuracy compared to
existing ICL methods. Our code has been released.Comment: Accepted to the Findings of EMNLP 2023 (Short Paper
Data augmentation and semi-supervised learning for deep neural networks-based text classifier
User feedback is essential for understanding user needs. In this paper, we use free-text obtained from a survey on sleep-related issues to build a deep neural networks-based text classifier. However, to train the deep neural networks model, a lot of labelled data is needed. To reduce manual data labelling, we propose a method which is a combination of data augmentation and pseudo-labelling: data augmentation is applied to labelled data to increase the size of the initial train set and then the trained model is used to annotate unlabelled data with pseudo-labels. The result shows that the model with the data augmentation achieves macro-averaged f1 score of 65.2% while using 4,300 training data, whereas the model without data augmentation achieves macro-averaged f1 score of 68.2% with around 14,000 training data. Furthermore, with the combination of pseudo-labelling, the model achieves macro-averaged f1 score of 62.7% with only using 1,400 training data with labels. In other words, with the proposed method we can reduce the amount of labelled data for training while achieving relatively good performance
SimuQ: A Framework for Programming Quantum Hamiltonian Simulation with Analog Compilation
Quantum Hamiltonian simulation, which simulates the evolution of quantum
systems and probes quantum phenomena, is one of the most promising applications
of quantum computing. Recent experimental results suggest that
Hamiltonian-oriented analog quantum simulation would be advantageous over
circuit-oriented digital quantum simulation in the Noisy Intermediate-Scale
Quantum (NISQ) machine era. However, programming analog quantum simulators is
much more challenging due to the lack of a unified interface between hardware
and software. In this paper, we design and implement SimuQ, the first framework
for quantum Hamiltonian simulation that supports Hamiltonian programming and
pulse-level compilation to heterogeneous analog quantum simulators.
Specifically, in SimuQ, front-end users specify the target quantum system with
Hamiltonian Modeling Language, and the Hamiltonian-level programmability of
analog quantum simulators is specified through a new abstraction called the
abstract analog instruction set (AAIS) and programmed in AAIS Specification
Language by hardware providers. Through a solver-based compilation, SimuQ
generates executable pulse schedules for real devices to simulate the evolution
of desired quantum systems, which is demonstrated on superconducting (IBM),
neutral-atom (QuEra), and trapped-ion (IonQ) quantum devices. Moreover, we
demonstrate the advantages of exposing the Hamiltonian-level programmability of
devices with native operations or interaction-based gates and establish a small
benchmark of quantum simulation to evaluate SimuQ's compiler with the above
analog quantum simulators.Comment: 34 pages, 15 figures, 3 tables. Appears in POPL 2024. The code is
available at https://github.com/PicksPeng/SimuQ. A website is available at
https://pickspeng.github.io/SimuQ
Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints
Adaptive Computation (AC) has been shown to be effective in improving the
efficiency of Open-Domain Question Answering (ODQA) systems. However, current
AC approaches require tuning of all model parameters, and training
state-of-the-art ODQA models requires significant computational resources that
may not be available for most researchers. We propose Adaptive Passage Encoder,
an AC method that can be applied to an existing ODQA model and can be trained
efficiently on a single GPU. It keeps the parameters of the base ODQA model
fixed, but it overrides the default layer-by-layer computation of the encoder
with an AC policy that is trained to optimise the computational efficiency of
the model. Our experimental results show that our method improves upon a
state-of-the-art model on two datasets, and is also more accurate than previous
AC methods due to the stronger base ODQA model. All source code and datasets
are available at https://github.com/uclnlp/APE.Comment: 7 pages, 1 figure, to be published in ACL-IJCNLP 202
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