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