Analysis on the Symbolic Effect of Calligraphy Landscape of Tourism Scenic Spots: The Case of China

Calligraphy landscape is a kind of special cultural landscape and symbols with Chinese characteristics, and has become an important type of tourism landscape in scenic spots. This paper takes calligraphy landscape of two famous tourism scenic spots in Guilin city of China as a study case, measures its symbolic effect with 5-point Likert scale. The result shows that calligraphy landscape receives high cognition and appreciation interest from tourists. Calligraphy landscape has certain symbolic effect on tourists in the dimensions of calligraphy appreciation, cultural symbol and landscape metaphor. Significant differences of symbolic effect of calligraphy landscape exist in the dimension of calligraphy appreciation, but not in the dimensions of cultural symbol and landscape metaphor. Symbolic effect research of calligraphy landscape provides theoretical guidance to revealing the relationship among people’s sense of place, environmental behavior and calligraphy landscape. The study has practical reference significance for designing calligraphy landscape and creating cultural atmosphere in tourism scenic spot

(Section A: Planning Strategies and Design Concepts)

Studies have shown that the city size distribution is in line with the power law distribution. By testing the city size distribution of cities in certain administrative levels in sub-national administrative areas in China, it was found that compared with power law distribution, the triangle law distribution put forward can better fit the city size distribution characteristics. The triangle law means the city size distribution structure is shaped like the city administrative division structure. That is, cities of the highest administrative level have far bigger size than other cities, and the city size distribution law of cities in the next administrative level is in accordance with the normal distribution. The triangle law hypothesis is put forward by the analysis of city size growth logic in China, and the institutional influence was considered as the main influencing factor. The results show that the city administrative system has probably shed light on the city size distribution. Further analysis shows the triangle law is more applicable in areas with higher population and fewer next levelled cities. Lastly, by new parameters extracted from the triangle law, the city size distribution characteristics of different regions in China are analysed

Discover, Explanation, Improvement: Automatic Slice Detection Framework for Natural Language Processing

Current natural language processing (NLP) models such as BERT and RoBERTa have achieved high overall performance, but they often make systematic errors due to bias or certain difficult features to learn. Thus research on slice detection models (SDM) which automatically identifies underperforming groups of datapoints has gradually caught more attention, which aims at both understanding model behaviors and providing insights for future model training and designing. However, there is little systematic research on SDM and quantitative evaluation of its assessment for NLP models. Our paper fills this gap by proposing "Discover, Explanation, Improvement" framework that discovers coherent and underperforming groups of datapoints and unites datapoints of each slice under human-understandable concepts; it also provides comprehensive evaluation tasks and the corresponding quantitative metrics, which enable convenient comparison for future works. Results show that our framework can accurately select error-prone datapoints with informative semantic features that summarize error patterns, based on which it directly boosts model performance by an average of 2.85 points based on trained models without tuning any parameters across multiple datasets.Comment: 15 pages, 5 figure

OpenAGI: When LLM Meets Domain Experts

Human intelligence has the remarkable ability to assemble basic skills into complex ones so as to solve complex tasks. This ability is equally important for Artificial Intelligence (AI), and thus, we assert that in addition to the development of large, comprehensive intelligent models, it is equally crucial to equip such models with the capability to harness various domain-specific expert models for complex task-solving in the pursuit of Artificial General Intelligence (AGI). Recent developments in Large Language Models (LLMs) have demonstrated remarkable learning and reasoning abilities, making them promising as a controller to select, synthesize, and execute external models to solve complex tasks. In this project, we develop OpenAGI, an open-source AGI research platform, specifically designed to offer complex, multi-step tasks and accompanied by task-specific datasets, evaluation metrics, and a diverse range of extensible models. OpenAGI formulates complex tasks as natural language queries, serving as input to the LLM. The LLM subsequently selects, synthesizes, and executes models provided by OpenAGI to address the task. Furthermore, we propose a Reinforcement Learning from Task Feedback (RLTF) mechanism, which uses the task-solving result as feedback to improve the LLM's task-solving ability. Thus, the LLM is responsible for synthesizing various external models for solving complex tasks, while RLTF provides feedback to improve its task-solving ability, enabling a feedback loop for self-improving AI. We believe that the paradigm of LLMs operating various expert models for complex task-solving is a promising approach towards AGI. To facilitate the community's long-term improvement and evaluation of AGI's ability, we open-source the code, benchmark, and evaluation methods of the OpenAGI project at https://github.com/agiresearch/OpenAGI.Comment: 18 pages, 6 figures, 7 table

GenRec: Large Language Model for Generative Recommendation

In recent years, large language models (LLM) have emerged as powerful tools for diverse natural language processing tasks. However, their potential for recommender systems under the generative recommendation paradigm remains relatively unexplored. This paper presents an innovative approach to recommendation systems using large language models (LLMs) based on text data. In this paper, we present a novel LLM for generative recommendation (GenRec) that utilized the expressive power of LLM to directly generate the target item to recommend, rather than calculating ranking score for each candidate item one by one as in traditional discriminative recommendation. GenRec uses LLM's understanding ability to interpret context, learn user preferences, and generate relevant recommendation. Our proposed approach leverages the vast knowledge encoded in large language models to accomplish recommendation tasks. We first we formulate specialized prompts to enhance the ability of LLM to comprehend recommendation tasks. Subsequently, we use these prompts to fine-tune the LLaMA backbone LLM on a dataset of user-item interactions, represented by textual data, to capture user preferences and item characteristics. Our research underscores the potential of LLM-based generative recommendation in revolutionizing the domain of recommendation systems and offers a foundational framework for future explorations in this field. We conduct extensive experiments on benchmark datasets, and the experiments shows that our GenRec has significant better results on large dataset