59 research outputs found
Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages
Chain-of-thought (CoT) is capable of eliciting models to explicitly generate
reasoning paths, thus promoting reasoning accuracy and attracting increasing
attention. Specifically, zero-shot CoT achieves remarkable improvements in a
wide range of reasoning tasks by simply instructing the LLM with the prompt
"Let's think step by step!". Despite the success of zero-shot CoT, the existing
zero-shot prompting techniques remain limited to a single language, making it
challenging to generalize to other languages and hindering global development.
In this work, we introduce cross-lingual prompting (CLP), aiming to improve
zero-shot CoT reasoning across languages. Specifically, CLP consists of two
main components: (1) cross-lingual alignment prompting and (2) task-specific
solver prompting. The cross-lingual alignment prompting is responsible for
aligning representations across different languages, whereas the task-specific
solver prompting is used to generate the final chain of thoughts and results
for the reasoning task. In addition, we further introduce cross-lingual
self-consistent prompting (CLSP) to ensemble different reasoning paths across
languages. Our experimental evaluations on several benchmarks demonstrate that
CLP and CLSP significantly outperform the existing prompting methods and
achieve state-of-the-art performance. We hope this work will inspire further
breakthroughs in cross-lingual CoT.Comment: Accepted at EMNLP2023 Main Conferenc
A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding
Zero-shot dialogue understanding aims to enable dialogue to track the user's
needs without any training data, which has gained increasing attention. In this
work, we investigate the understanding ability of ChatGPT for zero-shot
dialogue understanding tasks including spoken language understanding (SLU) and
dialogue state tracking (DST). Experimental results on four popular benchmarks
reveal the great potential of ChatGPT for zero-shot dialogue understanding. In
addition, extensive analysis shows that ChatGPT benefits from the multi-turn
interactive prompt in the DST task but struggles to perform slot filling for
SLU. Finally, we summarize several unexpected behaviors of ChatGPT in dialogue
understanding tasks, hoping to provide some insights for future research on
building zero-shot dialogue understanding systems with Large Language Models
(LLMs).Comment: Technical Repor
The experience of long-stay patients in a forensic psychiatric hospital in China: a qualitative study
open access articleBackground
Long stay in forensic psychiatric hospitals is common in patients who are defined as “not criminally responsible on account of mental disorder”. However, little is known about how these patients experience and perceive the long stay within these settings. The aim of this study is to explore the perception and needs of long-stay patients in forensic psychiatric hospitals in China.
Methods
In-depth semi-structured interviews were conducted with 21 participants who had lived in the forensic psychiatry hospital for more than 8 years. We used thematic analysis strategies to analyse the qualitative data.
Results
Participants’ perceptions clustered seven themes: hopelessness, loneliness, worthlessness, low mood, sleep disturbances, lack of freedom, and lack of mental health intervention.
Conclusions
The views and opinions expressed by long-stay patients showed that psychological distress is prevailing in forensic psychiatric hospitals. Adequate and effective care and mental health interventions are recommended to be tailored for their special needs
End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions
End-to-end task-oriented dialogue (EToD) can directly generate responses in
an end-to-end fashion without modular training, which attracts escalating
popularity. The advancement of deep neural networks, especially the successful
use of large pre-trained models, has further led to significant progress in
EToD research in recent years. In this paper, we present a thorough review and
provide a unified perspective to summarize existing approaches as well as
recent trends to advance the development of EToD research. The contributions of
this paper can be summarized: (1) \textbf{\textit{First survey}}: to our
knowledge, we take the first step to present a thorough survey of this research
field; (2) \textbf{\textit{New taxonomy}}: we first introduce a unified
perspective for EToD, including (i) \textit{Modularly EToD} and (ii)
\textit{Fully EToD}; (3) \textbf{\textit{New Frontiers}}: we discuss some
potential frontier areas as well as the corresponding challenges, hoping to
spur breakthrough research in EToD field; (4) \textbf{\textit{Abundant
resources}}: we build a public website\footnote{We collect the related papers,
baseline projects, and leaderboards for the community at
\url{https://etods.net/}.}, where EToD researchers could directly access the
recent progress. We hope this work can serve as a thorough reference for the
EToD research community.Comment: Accepted at EMNLP202
GL-CLeF: A Global-Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding
Due to high data demands of current methods, attention to zero-shot
cross-lingual spoken language understanding (SLU) has grown, as such approaches
greatly reduce human annotation effort. However, existing models solely rely on
shared parameters, which can only perform implicit alignment across languages.
We present Global--Local Contrastive Learning Framework (GL-CLeF) to address
this shortcoming. Specifically, we employ contrastive learning, leveraging
bilingual dictionaries to construct multilingual views of the same utterance,
then encourage their representations to be more similar than negative example
pairs, which achieves to explicitly aligned representations of similar
sentences across languages. In addition, a key step in GL-CLeF is a proposed
Local and Global component, which achieves a fine-grained cross-lingual
transfer (i.e., sentence-level Local intent transfer, token-level Local slot
transfer, and semantic-level Global transfer across intent and slot).
Experiments on MultiATIS++ show that GL-CLeF achieves the best performance and
successfully pulls representations of similar sentences across languages
closer.Comment: Accepted at ACL2022 Main Conferenc
MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System
Multi-modal sarcasm detection has attracted much recent attention.
Nevertheless, the existing benchmark (MMSD) has some shortcomings that hinder
the development of reliable multi-modal sarcasm detection system: (1) There are
some spurious cues in MMSD, leading to the model bias learning; (2) The
negative samples in MMSD are not always reasonable. To solve the aforementioned
issues, we introduce MMSD2.0, a correction dataset that fixes the shortcomings
of MMSD, by removing the spurious cues and re-annotating the unreasonable
samples. Meanwhile, we present a novel framework called multi-view CLIP that is
capable of leveraging multi-grained cues from multiple perspectives (i.e.,
text, image, and text-image interaction view) for multi-modal sarcasm
detection. Extensive experiments show that MMSD2.0 is a valuable benchmark for
building reliable multi-modal sarcasm detection systems and multi-view CLIP can
significantly outperform the previous best baselines.Comment: Accepted by ACL2023 Finding
Through the Lens of Core Competency: Survey on Evaluation of Large Language Models
From pre-trained language model (PLM) to large language model (LLM), the
field of natural language processing (NLP) has witnessed steep performance
gains and wide practical uses. The evaluation of a research field guides its
direction of improvement. However, LLMs are extremely hard to thoroughly
evaluate for two reasons. First of all, traditional NLP tasks become inadequate
due to the excellent performance of LLM. Secondly, existing evaluation tasks
are difficult to keep up with the wide range of applications in real-world
scenarios. To tackle these problems, existing works proposed various benchmarks
to better evaluate LLMs. To clarify the numerous evaluation tasks in both
academia and industry, we investigate multiple papers concerning LLM
evaluations. We summarize 4 core competencies of LLM, including reasoning,
knowledge, reliability, and safety. For every competency, we introduce its
definition, corresponding benchmarks, and metrics. Under this competency
architecture, similar tasks are combined to reflect corresponding ability,
while new tasks can also be easily added into the system. Finally, we give our
suggestions on the future direction of LLM's evaluation
Flexible Coherent Optical Access: Architectures, Algorithms, and Demonstrations
To cope with the explosive bandwidth demand, significant progress has been
made in the ITU-T standardization sector to define a higher-speed passive
optical network (PON) with a 50Gb/s line rate. Recently, 50G PON becomes mature
gradually, which means it is time to discuss beyond 50G PON. For ensuring an
acceptable optical power budget, beyond 50G PON will potentially use coherent
technologies, which can simultaneously promote the applications of flexible
multiple access such as time/frequency-domain multiple access (TFDMA). In this
paper, we will introduce the architectures, algorithms, and demonstrations for
TFDMA-based coherent PON. The system architectures based on an ultra-simple
coherent transceiver and specific signal spectra are designed to greatly reduce
the cost of ONUs. Meanwhile, fast and low-complexity digital signal processing
(DSP) algorithms are proposed for dealing with upstream and downstream signals.
Based on the architectures and algorithms, we experimentally demonstrate the
first real-time TFDMA-based coherent PON, which can support at most 256 end
users, and peak line rates of 100Gb/s and 200Gb/s in the upstream and
downstream scenarios, respectively. In conclusion, the proposed technologies
for the coherent PON make it more possible to be applied in the future beyond
50G PON.Comment: The paper has been submitted to the Journal of Lightwave Technolog
Fatigue and fracture in Inconel 718-copper-Inconel 718 explosion-bonded composites
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1990.Title as it appears in the M.I.T. Graduate List, Feb. 1990: Fatigue and fracture in explosion-bonded Inconel 718-copper-Inconel 718 composites.Includes bibliographical references.by Chikuang Chen.Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1990
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