30 research outputs found
Human-in-the-loop Machine Translation with Large Language Model
The large language model (LLM) has garnered significant attention due to its
in-context learning mechanisms and emergent capabilities. The research
community has conducted several pilot studies to apply LLMs to machine
translation tasks and evaluate their performance from diverse perspectives.
However, previous research has primarily focused on the LLM itself and has not
explored human intervention in the inference process of LLM. The
characteristics of LLM, such as in-context learning and prompt engineering,
closely mirror human cognitive abilities in language tasks, offering an
intuitive solution for human-in-the-loop generation. In this study, we propose
a human-in-the-loop pipeline that guides LLMs to produce customized outputs
with revision instructions. The pipeline initiates by prompting the LLM to
produce a draft translation, followed by the utilization of automatic retrieval
or human feedback as supervision signals to enhance the LLM's translation
through in-context learning. The human-machine interactions generated in this
pipeline are also stored in an external database to expand the in-context
retrieval database, enabling us to leverage human supervision in an offline
setting. We evaluate the proposed pipeline using GPT-3.5-turbo API on five
domain-specific benchmarks for German-English translation. The results
demonstrate the effectiveness of the pipeline in tailoring in-domain
translations and improving translation performance compared to direct
translation. Additionally, we discuss the results from the following
perspectives: 1) the effectiveness of different in-context retrieval methods;
2) the construction of a retrieval database under low-resource scenarios; 3)
the observed domains differences; 4) the quantitative analysis of linguistic
statistics; and 5) the qualitative analysis of translation cases. The code and
data are available at https://github.com/NLP2CT/HIL-MT/.Comment: Accepted to MT Summit 202
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation
We introduce a new algorithm for multi-objective reinforcement learning
(MORL) with linear preferences, with the goal of enabling few-shot adaptation
to new tasks. In MORL, the aim is to learn policies over multiple competing
objectives whose relative importance (preferences) is unknown to the agent.
While this alleviates dependence on scalar reward design, the expected return
of a policy can change significantly with varying preferences, making it
challenging to learn a single model to produce optimal policies under different
preference conditions. We propose a generalized version of the Bellman equation
to learn a single parametric representation for optimal policies over the space
of all possible preferences. After an initial learning phase, our agent can
execute the optimal policy under any given preference, or automatically infer
an underlying preference with very few samples. Experiments across four
different domains demonstrate the effectiveness of our approach.Comment: Accepted in NeurIPS 201
A Survey on LLM-generated Text Detection: Necessity, Methods, and Future Directions
The powerful ability to understand, follow, and generate complex language
emerging from large language models (LLMs) makes LLM-generated text flood many
areas of our daily lives at an incredible speed and is widely accepted by
humans. As LLMs continue to expand, there is an imperative need to develop
detectors that can detect LLM-generated text. This is crucial to mitigate
potential misuse of LLMs and safeguard realms like artistic expression and
social networks from harmful influence of LLM-generated content. The
LLM-generated text detection aims to discern if a piece of text was produced by
an LLM, which is essentially a binary classification task. The detector
techniques have witnessed notable advancements recently, propelled by
innovations in watermarking techniques, zero-shot methods, fine-turning LMs
methods, adversarial learning methods, LLMs as detectors, and human-assisted
methods. In this survey, we collate recent research breakthroughs in this area
and underscore the pressing need to bolster detector research. We also delve
into prevalent datasets, elucidating their limitations and developmental
requirements. Furthermore, we analyze various LLM-generated text detection
paradigms, shedding light on challenges like out-of-distribution problems,
potential attacks, and data ambiguity. Conclusively, we highlight interesting
directions for future research in LLM-generated text detection to advance the
implementation of responsible artificial intelligence (AI). Our aim with this
survey is to provide a clear and comprehensive introduction for newcomers while
also offering seasoned researchers a valuable update in the field of
LLM-generated text detection. The useful resources are publicly available at:
https://github.com/NLP2CT/LLM-generated-Text-Detection
Layered Potassium Titanium Niobate/Reduced Graphene Oxide Nanocomposite as a Potassium-Ion Battery Anode
With graphite currently leading as the most viable anode for potassium-ion batteries (KIBs), other materials have been left relatively under-examined. Transition metal oxides are among these, with many positive attributes such as synthetic maturity, long-term cycling stability and fast redox kinetics. Therefore, to address this research deficiency we report herein a layered potassium titanium niobate KTiNbO5 (KTNO) and its rGO nanocomposite (KTNO/rGO) synthesised via solvothermal methods as a high-performance anode for KIBs. Through effective distribution across the electrically conductive rGO, the electrochemical performance of the KTNO nanoparticles was enhanced. The potassium storage performance of the KTNO/rGO was demonstrated by its first charge capacity of 128.1 mAh g-1 and reversible capacity of 97.5 mAh g-1 after 500 cycles at 20 mA g-1, retaining 76.1% of the initial capacity, with an exceptional rate performance of 54.2 mAh g-1 at 1 A g-1. Furthermore, to investigate the attributes of KTNO in-situ XRD was performed, indicating a low-strain material. Ex-situ X-ray photoelectron spectra further investigated the mechanism of charge storage, with the titanium showing greater redox reversibility than the niobium. This work suggests this low-strain nature is a highly advantageous property and well worth regarding KTNO as a promising anode for future high-performance KIBs
Is personal growth initiative associated with later life satisfaction in Chinese college students? A 15‐week prospective analysis
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151959/1/ajsp12386.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151959/2/ajsp12386_am.pd
Observation of Flat Band and Van Hove Singularity in Non-superconducting Nitrogen-doped Lutetium Hydride
Hydrogen-rich materials offer a compelling avenue towards room temperature
superconductivity, albeit under ultra-high pressure. However, the experimental
investigation of the electronic band structure remains elusive, due to the
inherent instability of most of the hydrogen-rich materials upon pressure
release. Very recently, nitrogen-doped lutetium hydride was claimed to host
room temperature superconductivity under near ambient pressure but was
disproven by following works. Upon decompression, nitrogen doped lutetium
hydride manifests a stable metallic phase with dark blue color. Moreover, high
temperature superconductivity has been reported in lutetium hydrides Lu4H23
(~71 K) under around 200 GPa. These properties engender an unprecedented
opportunity, allowing for the experimental investigation of the electronic band
structure intrinsic to hydrogen-rich material. In this work, using angle
resolved photoemission spectroscopy to investigate the non-superconducting
nitrogen doped lutetium hydride, we observed significant flat band and Van Hove
singularity marginally below the Fermi level. These salient features,
identified as critical elements, proffer potential amplifiers for the
realization of heightened superconductivity, as evidenced by prior research.
Our results not only unveil a confluence of potent strong correlation effects
and anisotropy within the Lu-H-N compound, but also provide a prospect for
engineering high temperature superconductivity through the strategic
manipulation of flat band and the VHS, effectively tailoring their alignment
with the Fermi energy.Comment: 26 pages, 9 figure
From mind to machine: neural circuits, learning algorithms, and beyond
This thesis explores diverse topics within computational neuroscience and machine learning. The work begins by examining the organization of biological neuronal circuits reconstructed by electron microscopy. First, our study of neural connectivity patterns in the mouse primary visual cortex illustrates the necessity for refined understanding of the non-random features of cortical connections, challenging conventional perspectives. Second, in the larval zebrafish hindbrain, our novel discovery highlights overrepresented three-cycles of neuron, an observation unprecedented in electron microscopy-reconstructed neuronal wiring diagrams. Additionally, I present an exhaustive compilation of motif statistics and network characteristics for the complete adult Drosophila brain. These efforts collectively enrich our understanding of the intricate wiring diagram of neurons, offering new insights into the organizational principles of biological brains.
In the second part of the thesis, I introduce three distinct machine learning algorithms. The first algorithm, a biologically plausible unsupervised learning algorithm, is implemented within artificial neural networks using Hebbian feedforward and anti-Hebbian lateral connections. The theoretical discourse explores the duality and convergence of the learning process, connecting with the generalized concept of the "correlation game" principle. The second algorithm presents a novel multi-objective reinforcement learning approach, adept at managing real-world scenarios where multiple potentially conflicting criteria must be optimized without predefined importance weighting. This innovation allows the trained neural network model to generate policies that align optimally with user-specified preferences across the entire space of preference. The third algorithm employs a cognitive science-inspired learning principle for dialog systems. The designed system engages in negotiation with others, skillfully inferring the intent of the other party and predicting how its responses may influence the opponent's mental state.
Collectively, these contributions shed light on the complexities of neural circuit organization and offer new methodologies in machine learning. By examining intelligence from both biological and computational perspectives, the thesis presents insights and reference points for future research, contributing to our growing understanding of intelligence