Information extraction (IE) aims to extract structural knowledge (such as entities, relations, and events) from plain natural language texts. Recently, generative Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation, allowing for generalization across various domains and tasks. As a result, numerous works have been proposed to harness abilities of LLMs and offer viable solutions for IE tasks based on a generative paradigm. To conduct a comprehensive systematic review and exploration of LLM efforts for IE tasks, in this study, we survey the most recent advancements in this field. We first present an extensive overview by categorizing these works in terms of various IE subtasks and learning paradigms, then we empirically analyze the most advanced methods and discover the emerging trend of IE tasks with LLMs. Based on thorough review conducted, we identify several insights in technique and promising research directions that deserve further exploration in future studies. We maintain a public repository and consistently update related resources at: \url{https://github.com/quqxui/Awesome-LLM4IE-Papers}.

Large Language Models for Generative Information Extraction: A Survey

This study aims to explore thematic influences on theme park visitors' satisfaction through user-generated data. To this end, we first used an unsupervised machine learning method, structural topic modeling, and analyzed 112,000 reviews post by visitors to Shanghai Disney Resort from June 16, 2016 to March 4, 2022. Our findings are of great significance for reflecting consumer behavior through user-generated data. Specifically, we find that visitors' satisfaction is highly related to service in the theme park and their playing feeling, and early tourists pay more attention to the experience of specific playing items while later tourists focus on the overall playing experience. In addition, an empirical study is conducted by treating the ratings associated with each review as dependent variable and each topic represented by comments as independent variables, which shows that the relationship between the customer reviews and ratings by tourists becomes less pronounced over time. In other words, as time goes, customers review can reflect their subjective feelings or experience, but the rating is not. We discover the “dynamics” of user-generated data over time and gain a better understanding of the aspects and concerns of visitors' satisfaction over time. The findings of the study contribute to the literature on tourism, service, and consumer behavior while also providing valuable practical implications. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cb.2157

Exploring thematic influences on theme park visitors' satisfaction: An empirical study on Disneyland China

Large language models (LLMs) have achieved superior performance in powering text-based AI agents, endowing them with decision-making and reasoning abilities akin to humans. Concurrently, there is an emerging research trend focused on extending these LLM-powered AI agents into the multimodal domain. This extension enables AI agents to interpret and respond to diverse multimodal user queries, thereby handling more intricate and nuanced tasks. In this paper, we conduct a systematic review of LLM-driven multimodal agents, which we refer to as large multimodal agents ( LMAs for short). First, we introduce the essential components involved in developing LMAs and categorize the current body of research into four distinct types. Subsequently, we review the collaborative frameworks integrating multiple LMAs , enhancing collective efficacy. One of the critical challenges in this field is the diverse evaluation methods used across existing studies, hindering effective comparison among different LMAs . Therefore, we compile these evaluation methodologies and establish a comprehensive framework to bridge the gaps. This framework aims to standardize evaluations, facilitating more meaningful comparisons. Concluding our review, we highlight the extensive applications of LMAs and propose possible future research directions. Our discussion aims to provide valuable insights and guidelines for future research in this rapidly evolving field. An up-to-date resource list is available at https://github.com/jun0wanan/awesome-large-multimodal-agents.

Large Multimodal Agents: A Survey

Role of virtual reality authentic experience on affective responses: moderating role virtual reality attachment

Prompt-tuning was proposed to bridge the gap between pretraining and downstream tasks, and it has achieved promising results in Relation Extraction (RE) tasks in recent years. Although the existing prompt-based RE methods have outperformed the methods based on the fine-tuning paradigm, these methods require domain experts to design prompt templates, making them hard to generalize. In this paper, we proposed a Generative context-Aware Prompt-tuning method (GAP) to address these limitations. Our method consists of three crucial modules: (1) a pretrained prompt generator module that extracts or generates the relation triggers from the context and embeds them into the prompt tokens, (2) an in-domain adaptive pretraining module that further trains the Pretrained Language Models (PLMs) to promote the adaptability of the model, and (3) a joint contrastive loss that prevents PLMs from generating results unrelated to the relation labels while optimizing our model more effectively. We observed that the context-enhanced prompt tokens generated by GAP can better guide PLMs to make accurate relationship predictions. And the in-domain pretraining can effectively inject domain knowledge to enhance the robustness of the model. We conduct experiments on four public RE datasets under the supervised and few-shot settings. The experimental results have demonstrated the superiority of GAP over existing benchmark methods and GAP shows remarkable improvements in few-shot settings, with average F1 score enhancements of 3.5%, 2.7%, and 3.4% on the TACRED, TACREV, and Re-TACRED datasets, respectively. Furthermore, GAP still achieved state-of-the-art (SOTA) performance in supervised settings. 

GAP: A novel Generative context-Aware Prompt-tuning method for relation extraction

Warmth trumps competence? Uncovering the influence of multimodal AI anthropomorphic interaction experience on intelligent service evaluation: Insights from the high-evoked automated social presence

AI-empowered music processing is a diverse field that encompasses dozens of tasks, ranging from generation tasks (e.g., timbre synthesis) to comprehension tasks (e.g., music classification). For developers and amateurs, it is very difficult to grasp all of these task to satisfy their requirements in music processing, especially considering the huge differences in the representations of music data and the model applicability across platforms among various tasks. Consequently, it is necessary to build a system to organize and integrate these tasks, and thus help practitioners to automatically analyze their demand and call suitable tools as solutions to fulfill their requirements. Inspired by the recent success of large language models (LLMs) in task automation, we develop a system, named MusicAgent, which integrates numerous music-related tools and an autonomous workflow to address user requirements. More specifically, we build 1) toolset that collects tools from diverse sources, including Hugging Face, GitHub, and Web API, etc. 2) an autonomous workflow empowered by LLMs (e.g., ChatGPT) to organize these tools and automatically decompose user requests into multiple sub-tasks and invoke corresponding music tools. The primary goal of this system is to free users from the intricacies of AI-music tools, enabling them to concentrate on the creative aspect. By granting users the freedom to effortlessly combine tools, the system offers a seamless and enriching music experience.

pdf/musicagent-an-ai-agent-for-music-understanding-and-5a91le8w6j.pdf

MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models

Sequence generation demonstrates promising performance in recent information extraction efforts, by incorporating large-scale pre-trained Seq2Seq models. This paper investigates the merits of employing sequence generation in relation extraction, finding that with relation names or synonyms as generation targets, their textual semantics and the correlation (in terms of word sequence pattern) among them affect model performance. We then propose Relation Extraction with Label Augmentation (RELA), a Seq2Seq model with automatic label augmentation for RE. By saying label augmentation, we mean prod semantically synonyms for each relation name as the generation target. Besides, we present an in-depth analysis of the Seq2Seq model's behavior when dealing with RE. Experimental results show that RELA achieves competitive results compared with previous methods on four RE datasets.

pdf/sequence-generation-with-label-augmentation-for-relation-3kd85pep.pdf

Sequence Generation with Label Augmentation for Relation Extraction

Based on the quality level that consumers can discover at various stages, the literature summary divides organic food attributes into three categories: trust, search, and experience. This paper deeply analyzes the internal relationship among the search attribute, trust attribute, and perceived quality and the mechanism of effect on customer trust. After distributing and collecting 310 consumers’ valid questionnaires, the research hypotheses were empirically tested utilizing a structural equation model and mediation effect test. The research results indicate that: (1) The food safety attribute and nutritional content attribute in the organic food trust attribute have positive effects on the perceived quality and customer trust. (2) The price and label in the organic food search attribute positively affect the perceived quality, i.e., the price harms customer trust, while the label has no significant effect on customer trust. Perceived quality plays a mediating role between the trust attributes, search attribute, and customer trust, i.e., the price and label indirectly affect customer trust through perceived quality. (3) The perceived quality of organic food positively affects customer trust. The results provide an important theoretical basis for enterprises to implement effective strategies to enhance consumers’ trust in organic food.

/pdf/research-on-the-influence-mechanism-of-organic-food-3nlkd2gc.pdf

Research on the Influence Mechanism of Organic Food Attributes on Customer Trust

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