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

Social Capital, Socioeconomic Status and Self-efficacy

This study internalized social capital on the basis of traditional study of the influence of economic factors on self-efficacy, and studied the relationship among the family socio-economic status, social capital and self-efficacy. Based on the theoretical analysis, with first-hand data collection and using multiple regression models, the paper studied the intermediate effect of social capital in the relationship between the socioeconomic status and self-efficacy. We draw on the following conclusions: (1) The family socio-economic status as well as all its dimensions (father’s degree of education, mother’s degree of education, the total annual income of the family, father’s occupation, mother’s occupation) is significantly positively related to social capital and all the dimensions of its proxy variable (peer support, kinship support and general support of others); (2) There is a significant positive correlation between the family socio-economic status as well as all its dimensions and self-efficacy; the socio-economic status, with its dimensions, is the predictive variable of self-efficacy; (3) Social capital, with dimensions of its proxy variable, is positively correlated with self-efficacy and has predictive effect on self-efficacy (4) Social capital plays a significant intermediate role between socio-economic status and self-efficacy, and the mediating effect size is about 51.75%

Agreeableness, Extraversion, Stressor and Physiological Stress Response

Based on the theoretical analysis, with first-hand data collection and using multiple regression models, this study explored the relationship between agreeableness, extraversion, stressor and stress response and figured out interactive effect of agreeableness, extraversion, and stressor on stress response. We draw on the following conclusions: (1) the interaction term of stressor (work) and agreeableness can negatively predict physiological stress response; (2) the interaction term of stressor (health) and agreeableness can negatively predict physiological stress response; (3) the interaction term of stressor (family) and agreeableness can negatively predict physiological stress response; (4) the interaction term of stressor (social) and agreeableness can negatively predict physiological stress response; (5) the interaction term of stressor (work) and extraversion can negatively predict physiological stress response; (6) the interaction term of stressor (health) and extraversion can negatively predict physiological stress response; (7) the interaction term of stressor (family) and extraversion can negatively predict physiological stress response; (8) the interaction term of stressor (social) and extraversion can negatively predict physiological stress response

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

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

Comprehensive characterizing of vortex phases in type-II superconductor YBa2Cu3O7-x by a magnetoelectric technique

The vortex phases in type-II superconductors are very important since they determine many magnetic and electric properties of the parent compound. However, a universal tool to characterize the vortex phases is still lacking. We demonstrate in a type-II superconductors YBa2Cu3O7-x polycrystal sample that its vortex phases and phase boundaries can be comprehensively studied by a magnetoelectric technique. In this method, a thin piezoelectric material 0.7Pb(Mg1/3Nb2/3)O3-0.3PbTiO3(PMN-PT) is mechanically bonded with YBa2Cu3O7-x to form a laminate structure and act as a strain gauge. The phase diagram of the YBa2Cu3O7-x polycrystalline was explored by this method. Surprisingly, it can accurately estimate the Hc1, irreversible line, Hc2 and distinguish among vortex glass, vortex liquid, non-vortex states. Moreover, it can probe the dynamic response under different frequencies and observe the threshold phenomena of vortex liquid phase. It can even account for the density of vortices in the vortex solid phase. Our technique is readily extended to investigate the vortex phases in other type-II superconductors.Comment: 23 pages, 6 figure

Study of Financial Literacy and Interpersonal Influence on Self-Evaluation Bias-An Empirical Analysis with Chinese Sample

Based on the theoretical analysis, with first-hand data collection and using multiple regression models, this study explored the relationship between financial literacy, interpersonal influence and self-evaluation bias and figured out interactive effect of financial literacy, interpersonal influence on self-evaluation bias. We draw on the following conclusions: (1) Three financial literacy factors (sophisticated financial literacy, basic financial literacy and numeracy) entered into the regression equation on self-evaluation bias, with a predictive power of 14.8%. (2) The interaction term of financial literacy and coworkers/classmates’ influence can negatively predict self-evaluation bias. (3) The interaction term of financial literacy and family members’ influence can negatively predict self-evaluation bias

Study of Risk Preference, Investment Experience and Interpersonal Influence-An Empirical Analysis with Chinese Sample

This study internalized investment experience on the basis of study of the influence of interpersonal influence on risk preference, and studied the relationship among the interpersonal influence, investment experience and risk preference. Based on the theoretical analysis, with first-hand data collection and using multiple regression models, the paper studied the intermediate effect of investment experience in the relationship between the interpersonal influence and risk preference. We draw on the following conclusions: ① Investment experience plays a significant intermediate role between friends’ influence and risk preference, and the mediating effect size is about 57.61%. ② Investment experience is a full intermediate variable between family’s influence and risk preference. ③ Investment experience plays a significant intermediate role between coworkers/classmates’ influence and risk preference, and the mediating effect size is about 55.09%