144 research outputs found
CODE-MIXING AS A BILINGUAL INSTRUCTIONAL STRATEGY IN EFL CONTEXT
This research investigated the code-mixing technique from perspectives of teacher and student in university teaching contexts, more specifically the use of L1 (Chinese) in L2 (English) in Chinese university English education programs. Through the analysis of results of semi-structured interview, several themes emerged: (1) from the student’s perspective, the use of code-mixing at classes helps her to understand the lesson better, while she also performed resistance to the overuse of code-mixing; (2) from the perspective of the teacher, the use of code-mixing helps her to address the complex or difficult points more easily to the class; (3) the use of code-mixing influences not only linguistic competence , but also cognitive and sociocultural aspects of the learner. The results demonstrate that using bilingual instruction significantly enhance the way both teacher and student use English. 
Unpacking the Ethical Value Alignment in Big Models
Big models have greatly advanced AI's ability to understand, generate, and
manipulate information and content, enabling numerous applications. However, as
these models become increasingly integrated into everyday life, their inherent
ethical values and potential biases pose unforeseen risks to society. This
paper provides an overview of the risks and challenges associated with big
models, surveys existing AI ethics guidelines, and examines the ethical
implications arising from the limitations of these models. Taking a normative
ethics perspective, we propose a reassessment of recent normative guidelines,
highlighting the importance of collaborative efforts in academia to establish a
unified and universal AI ethics framework. Furthermore, we investigate the
moral inclinations of current mainstream LLMs using the Moral Foundation
theory, analyze existing alignment algorithms, and outline the unique
challenges encountered in aligning ethical values within them. To address these
challenges, we introduce a novel conceptual paradigm for aligning the ethical
values of big models and discuss promising research directions for alignment
criteria, evaluation, and method, representing an initial step towards the
interdisciplinary construction of the ethically aligned AI
This paper is a modified English version of our Chinese paper
https://crad.ict.ac.cn/cn/article/doi/10.7544/issn1000-1239.202330553, intended
to help non-Chinese native speakers better understand our work
From Instructions to Intrinsic Human Values -- A Survey of Alignment Goals for Big Models
Big models, exemplified by Large Language Models (LLMs), are models typically
pre-trained on massive data and comprised of enormous parameters, which not
only obtain significantly improved performance across diverse tasks but also
present emergent capabilities absent in smaller models. However, the growing
intertwining of big models with everyday human lives poses potential risks and
might cause serious social harm. Therefore, many efforts have been made to
align LLMs with humans to make them better follow user instructions and satisfy
human preferences. Nevertheless, `what to align with' has not been fully
discussed, and inappropriate alignment goals might even backfire. In this
paper, we conduct a comprehensive survey of different alignment goals in
existing work and trace their evolution paths to help identify the most
essential goal. Particularly, we investigate related works from two
perspectives: the definition of alignment goals and alignment evaluation. Our
analysis encompasses three distinct levels of alignment goals and reveals a
goal transformation from fundamental abilities to value orientation, indicating
the potential of intrinsic human values as the alignment goal for enhanced
LLMs. Based on such results, we further discuss the challenges of achieving
such intrinsic value alignment and provide a collection of available resources
for future research on the alignment of big models.Comment: 20 pages, 5 figure
The Use of Switching Point and Protection Levels to Improve Revenue Performance in Order‐Driven Production Systems
In a multiproduct order‐driven production system, an organization has to decide how to selectively accept orders and allocate capacity to these orders so as to maximize total profit (TP). In this article, we incorporate the novel concept of switching point in developing three capacity‐allocation with switching point heuristics (CASPa‐c). Our analysis indicates that all three CASP heuristics outperform the first‐come‐first‐served model and Barut and Sridharan's dynamic capacity‐allocation process (DCAP) model. The best model, CASPb, has an 8% and 6% average TP improvement over DCAP using the split lot and whole lot policies, respectively. In addition, CASPb performs particularly well under operating conditions of tight capacity and large price differences between product classes. The introduction of a switching point, which has not been found in previous capacity‐allocation heuristics, provides for a better balance between forward and backward allocation of available capacity and plays a significant role in improving TP.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/112181/1/j.1540-5915.2011.00320.x.pd
Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizes
In this paper, we move towards combining large parametric models with
non-parametric prototypical networks. We propose prototypical fine-tuning, a
novel prototypical framework for fine-tuning pretrained language models (LM),
which automatically learns a bias to improve predictive performance for varying
data sizes, especially low-resource settings. Our prototypical fine-tuning
approach can automatically adjust the model capacity according to the number of
data points and the model's inherent attributes. Moreover, we propose four
principles for effective prototype fine-tuning towards the optimal solution.
Experimental results across various datasets show that our work achieves
significant performance improvements under various low-resource settings, as
well as comparable and usually better performances in high-resource scenarios.Comment: Published as a conference paper at AAAI 202
Value FULCRA: Mapping Large Language Models to the Multidimensional Spectrum of Basic Human Values
The rapid advancement of Large Language Models (LLMs) has attracted much
attention to value alignment for their responsible development. However, how to
define values in this context remains a largely unexplored question. Existing
work mainly follows the Helpful, Honest, Harmless principle and specifies
values as risk criteria formulated in the AI community, e.g., fairness and
privacy protection, suffering from poor clarity, adaptability and transparency.
Inspired by basic values in humanity and social science across cultures, this
work proposes a novel basic value alignment paradigm and introduces a value
space spanned by basic value dimensions. All LLMs' behaviors can be mapped into
the space by identifying the underlying values, possessing the potential to
address the three challenges. To foster future research, we apply the
representative Schwartz's Theory of Basic Values as an initialized example and
construct FULCRA, a dataset consisting of 5k (LLM output, value vector) pairs.
Our extensive analysis of FULCRA reveals the underlying relation between basic
values and LLMs' behaviors, demonstrating that our approach not only covers
existing mainstream risks but also anticipates possibly unidentified ones.
Additionally, we present an initial implementation of the basic value
evaluation and alignment, paving the way for future research in this line
CAMP:Co-Attention Memory Networks for Diagnosis Prediction in Healthcare
Diagnosis prediction, which aims to predict future health information of patients from historical electronic health records (EHRs), is a core research task in personalized healthcare. Although some RNN-based methods have been proposed to model sequential EHR data, these methods have two major issues. First, they cannot capture fine-grained progression patterns of patient health conditions. Second, they do not consider the mutual effect between important context (e.g., patient demographics) and historical diagnosis. To tackle these challenges, we propose a model called Co-Attention Memory networks for diagnosis Prediction (CAMP), which tightly integrates historical records, fine-grained patient conditions, and demographics with a three-way interaction architecture built on co-attention. Our model augments RNNs with a memory network to enrich the representation capacity. The memory network enables analysis of fine-grained patient conditions by explicitly incorporating a taxonomy of diseases into an array of memory slots. We instantiate the READ/WRITE operations of the memory network so that the memory cooperates effectively with the patient demographics through co-attention mechanism. Experiments on real-world datasets demonstrate that CAMP consistently performs better than state-of-the-art methods
Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations
The significant progress of large language models (LLMs) provides a promising
opportunity to build human-like systems for various practical applications.
However, when applied to specific task domains, an LLM pre-trained on a
general-purpose corpus may exhibit a deficit or inadequacy in two types of
domain-specific knowledge. One is a comprehensive set of domain data that is
typically large-scale and continuously evolving. The other is specific working
patterns of this domain reflected in the data. The absence or inadequacy of
such knowledge impacts the performance of the LLM. In this paper, we propose a
general paradigm that augments LLMs with DOmain-specific KnowledgE to enhance
their performance on practical applications, namely DOKE. This paradigm relies
on a domain knowledge extractor, working in three steps: 1) preparing effective
knowledge for the task; 2) selecting the knowledge for each specific sample;
and 3) expressing the knowledge in an LLM-understandable way. Then, the
extracted knowledge is incorporated through prompts, without any computational
cost of model fine-tuning. We instantiate the general paradigm on a widespread
application, i.e. recommender systems, where critical item attributes and
collaborative filtering signals are incorporated. Experimental results
demonstrate that DOKE can substantially improve the performance of LLMs in
specific domains
- …