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

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

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 Research on Cultural and Creative industries Cluster Development Based on Nash Equilibrium

Cultural and creative industry is the second largest pillar industry of the tertiary industry, with the characteristics of innovative, high value and strong correlation relationship. The development of industrial cluster is helpful to cultural communication and information transfer, so it can enhance the competitiveness of the creative industry. The key problems need to be solved are the development condition and development strategy of creative industry cluster. This paper builds a mathematical model of two areas and two enterprises to study how can the effect of location factor and aggregation influence the development of cultural and creative industries, and the result shows a series of optimal development forms of the cultural and creative industry under different conditions. Finally four piece of recommendations to promote the development of creative industry clusters have been put forward

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

Antiallergic effects of ethanol extract of Cnidium monnieri (L.) Cuss. on DNCB-induced atopic dermatitis in mice

Purpose: To study the anti-allergic effects of ethanol extract of Cnidium monnieri (L.) Cuss. on 2, 4-dinitrochlorobenzene (DNCB)-induced atopic dermatitis in mice.Method: Atopic dermatitis (AD) was induced by DNCB in Balb/c mice, and the mice randomly divided into normal group, negative control group, hydrocortisone group, and ethanol extract of Cnidium monnieri (L.) Cuss. (EECM) group. Ear swelling was determined by measuring the thicknesses of the left and right ears of each mouse. Spleen and thymus indices were calculated from spleen, thymus and body weight values. The levels of TNF-α and IgE in serum were determined by enzyme-linked immunosorbent assay (ELISA). Hematoxylin-eosin (H & E) staining and toluidine blue staining were used to evaluate pathological changes in ear tissue, while high performance liquid chromatography (HPLC) was performed to ascertain the bioactive compounds in EECM.Results: Compared with the negative control group, EECM significantly alleviated skin lesions, reduced thickness of ear swelling, and decreased spleen and thymus indexes of mice (p < 0.05). Moreover, EECM significantly reduced epidermal thickness (p < 0.01). However, EECM did not significantly alter the number of mast cells (p > 0.05). The expressions of TNF-α and IgE in serum were also significantly down-regulated (p < 0.01, p < 0.05). Results from HPLC revealed that the contents of bergapten, imperatorin and osthole in EECM were 0.73, 3.69 and 9.40 mg/g, respectively.Conclusion: EECM ameliorates AD in mice via inhibition of inflammation and by a mechanism that might be related to the regulation of TNF-α and IgE levels. The major bioactive constituents of EECM are osthole, imperatorin and bergapten. Thus, this plant extract has a potential to be developed for the treatment of of atopic dermatitis

Magnetic field-modulated exciton generation in organic semiconductors: an intermolecular quantum correlation effect

Magnetoelectroluminescence (MEL) of organic semiconductor has been experimentally tuned by adopting blended emitting layer consisting of both hole and electron transporting materials. A theoretical model considering intermolecular quantum correlation is proposed to demonstrate two fundamental issues: (1) two mechanisms, spin scattering and spin mixing, dominate the two different steps respectively in the process of the magnetic field modulated generation of exciton; (2) the hopping rate of carriers determines the intensity of MEL. Calculation successfully predicts the increase of singlet excitons in low field with little change of triplet exciton population.Comment: 16 pages, 4 figure