101 research outputs found
Research on Business Model of Self-operated Import E-commerce Based on Value Network ——A Case Study of KAOLA.COM
From the perspective of value network, the paper takes KAOLA.COM as an example to analyze the business model of self-operated cross-border e-commerce. It tries to propose a research framework including value proposition, value creation, value realization and value support based on the summary of relevant theory. The paper analyses the development status and the value business model of KAOLA.COM,and puts forward some advices optimizing the value network of China's self-operated cross-border e-commerce,it includes : (1)segment customer , combining online and offline.(2) control the genuine strictly, strengthening the supply chain management.(3) expand the category, achieving the third party income.(4) improve customer relationship, accelerating layout of overseas warehouse. The purpose of the research is to provide reference for optimization and development of self-operated cross-border e-commerce. Keywords: Value network; Self – operated cross-border e-commerce; Business model; KAOLA.COM
Assessing and predicting small industrial enterprises’ credit ratings:A fuzzy decision-making approach
Corporate credit-rating assessment plays a crucial role in helping financial institutions make their lending decisions and in reducing the financial constraints of small enterprises. This paper presents a new approach for small industrial enterprises’ credit-rating assessment using fuzzy decision-making methods, and tests it using real bank loan data from 1,820 small industrial enterprises in China. The procedure of the proposed rating approach includes (1) using triangular fuzzy numbers to quantify the qualitative evaluation indicators; (2) adopting a correlation analysis, univariate analysis and stepping backwards feature selection method to select the input features; (3) employing the best-worst method (BWM) combined with the entropy weight method (EWM), the fuzzy c-means algorithm and the technique for order of preference by similarity to ideal solution (TOPSIS) to classify small enterprises into rating classes; and (4) applying the lattice degree of nearness to predict a new loan applicant’s rating. We also conduct a 10-fold cross-validation to evaluate the predictive performance of our proposed approach. The predictive results demonstrate that our proposed data-processing and feature selection approaches have better accuracy than the alternative approaches in predicting default, offering bankers a new valuable rating system to assist their decision making
TSST: A Benchmark and Evaluation Models for Text Speech-Style Transfer
Text style is highly abstract, as it encompasses various aspects of a
speaker's characteristics, habits, logical thinking, and the content they
express. However, previous text-style transfer tasks have primarily focused on
data-driven approaches, lacking in-depth analysis and research from the
perspectives of linguistics and cognitive science. In this paper, we introduce
a novel task called Text Speech-Style Transfer (TSST). The main objective is to
further explore topics related to human cognition, such as personality and
emotion, based on the capabilities of existing LLMs. Considering the objective
of our task and the distinctive characteristics of oral speech in real-life
scenarios, we trained multi-dimension (i.e. filler words, vividness,
interactivity, emotionality) evaluation models for the TSST and validated their
correlation with human assessments. We thoroughly analyze the performance of
several large language models (LLMs) and identify areas where further
improvement is needed. Moreover, driven by our evaluation models, we have
released a new corpus that improves the capabilities of LLMs in generating text
with speech-style characteristics. In summary, we present the TSST task, a new
benchmark for style transfer and emphasizing human-oriented evaluation,
exploring and advancing the performance of current LLMs.Comment: Working in progres
MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications
Large Language Models (LLMs) have demonstrated remarkable performance across
various natural language tasks, marking significant strides towards general
artificial intelligence. While general artificial intelligence is leveraged by
developing increasingly large-scale models, there could be another branch to
develop lightweight custom models that better serve certain domains, taking
into account the high cost of training and deploying LLMs and the scarcity of
resources. In this paper, we present MindLLM, a novel series of bilingual
lightweight large language models, trained from scratch, alleviating such
burdens by offering models with 1.3 billion and 3 billion parameters. A
thorough account of experiences accrued during large model development is
given, covering every step of the process, including data construction, model
architecture, evaluation, and applications. Such insights are hopefully
valuable for fellow academics and developers. MindLLM consistently matches or
surpasses the performance of other open-source larger models on some public
benchmarks. We also introduce an innovative instruction tuning framework
tailored for smaller models to enhance their capabilities efficiently.
Moreover, we explore the application of MindLLM in specific vertical domains
such as law and finance, underscoring the agility and adaptability of our
lightweight models.Comment: Working in progres
RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling
Recent advances in large-scale pre-training such as GPT-3 allow seemingly
high quality text to be generated from a given prompt. However, such generation
systems often suffer from problems of hallucinated facts, and are not
inherently designed to incorporate useful external information. Grounded
generation models appear to offer remedies, but their training typically relies
on rarely-available parallel data where information-relevant documents are
provided for context. We propose a framework that alleviates this data
constraint by jointly training a grounded generator and document retriever on
the language model signal. The model learns to reward retrieval of the
documents with the highest utility in generation, and attentively combines them
using a Mixture-of-Experts (MoE) ensemble to generate follow-on text. We
demonstrate that both generator and retriever can take advantage of this joint
training and work synergistically to produce more informative and relevant text
in both prose and dialogue generation.Comment: accepted by AAAI-22, camera ready versio
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