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Simple Contrastive Learning with knowledge Graphs for Story Generation

Simple Contrastive Learning with Knowledge Graphs for Story ...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhu, Yu Pan, Rong School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350368741

Story generation stands as a crucial, yet formidable task, necessitating a profound grasp of subtle, often unspoken knowledge, along with context-specific cues to craft compelling narratives. The core challenges involve effectively harnessing this implicit knowledge and augmenting narrative diversity. Addressing these hurdles, this paper introduces SimCoS - an acronym for Simple Contrastive learning with knowledge graphs for Story generation. SimCoS significantly bolsters language models' performance in story generation by sharpening their ability to perceive and discern input nuances, aided by a specialized story knowledge graph. Moreover, SimCoS debuts a novel methodology for evaluating 'negative instances' using subgraphs, which enables it to calculate logical distances between inputs, thereby enhancing differentiation among diverse stories. Through both automatic and manual assessments, SimCoS has been shown to outperform leading counterparts in two story generation tasks, marking improvements of 13.4% and 4.5%, respectively. This advancement not only yields narratives of superior quality and logical coherence but also elevates their diversity. © 2025 IEEE.

关键词： contrastive learning knowledge graph story generation

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Retrieval-Augmented Code Generation for Universal Information Extraction 13th

Retrieval-Augmented Code Generation for Universal Informatio...

引用

13th international conference on natural language processing and Chinese Computing

作者： Guo, Yucan Li, Zixuan Jin, Xiaolong Liu, Yantao Zeng, Yutao Liu, Wenxuan Li, Xiang Yang, Pan Bai, Long Guo, Jiafeng Chen, Xueqi Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci & Technol Beijing Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Information Extraction (IE) aims to extract structural knowledge (e.g., entities, relations, events) from natural language texts. Recently, Large language Models (LLMs) with code-style prompts have demonstrated powerful capabilities in IE tasks. However, adopting code LLMs to conduct IE tasks still has two challenges: (1) It still lacks a unified code-style prompt for different IE tasks since existing methods use task-specific prompts for separate IE tasks. (2) It still lacks an effective in-context learning (ICL) method to encourage LLMs to conduct IE tasks precisely, considering some powerful LLMs are close-sourced and not trainable. Therefore, this paper proposes a code generation framework for Universal IE (UIE) tasks called Code4UIE. Specifically, for the first challenge, Code4UIE designs a unified code-style schema for various IE tasks via Python classes. By so doing, different IE tasks can be associated, and LLMs can learn from various IE tasks effectively. For the second challenge, Code4UIE adopts a retrieval-augmented mechanism to comprehensively utilize the ICL ability of LLMs. Extensive experiments on five representative IE tasks across nine datasets demonstrate the effectiveness of the Code4UIE framework.

关键词： Information Extraction Code Generation Large language Model Information Retrieval

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Instruct Large language Models to Generate Scientific Literature Survey Step by Step 13th

Instruct Large Language Models to Generate Scientific Litera...

引用

13th international conference on natural language processing and Chinese Computing

作者： Lai, Yuxuan Wu, Yupeng Wang, Yidan Hu, Wenpeng Zheng, Chen Open Univ China Beijing Peoples R China Minist Educ Engn Res Ctr Integrat & Applicat Digital Learning Beijing Peoples R China FloatMiracle Beijing Peoples R China PLA Acad Mil Sci Beijing Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Automatically generating scientific literature surveys is a valuable task that can significantly enhance research efficiency. However, the diverse and complex nature of information within a literature survey poses substantial challenges for generative models. In this paper, we design a series of prompts to systematically leverage large language models (LLMs), enabling the creation of comprehensive literature surveys through a step-by-step approach. Specifically, we design prompts to guide LLMs to sequentially generate the title, abstract, hierarchical headings, and the main content of the literature survey. We argue that this design enables the generation of the headings from a high-level perspective. During the content generation process, this design effectively harnesses relevant information while minimizing costs by restricting the length of both input and output content in LLM queries. Our implementation with Qwen-long achieved third place in the NLPCC 2024 Scientific Literature Survey Generation evaluation task, with an overall score only 0.03% lower than the second-place team. Additionally, our soft heading recall is 95.84%, the second best among the submissions. Thanks to the efficient prompt design and the low cost of the Qwen-long API, our method reduces the expense for generating each literature survey to 0.1 RMB, enhancing the practical value of our method.

关键词： Automatic literature survey Large language model Prompt design

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Quantum Linguistics in Patient-Clinical Trial Relations for Improved Healthcare

Quantum Linguistics in Patient-Clinical Trial Relations for ...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Arora, Chandani Sandhu, Ramandeep Islam, Sardar M. N. Lovely Professional University School of Computer Science & Engineering Phagwara India Victoria University Applied Informatics Research Melbourne Australia

ISBN: (纸本)9798331508685

The healthcare industry is undergoing a fundamental revolution with the integration of Quantum natural language processing techniques for matching patient-clinical trials. This research envisions a paradigm where patients and clinical trials collaborate, changing the way healthcare is supplied and medical research is conducted. Apart from this, the research focuses on engaging patients in clinical trials. Patient involvement refers to the process by which patients and their healthcare providers collaborate to make decisions regarding their health. Within the framework of clinical studies, this may entail participants providing input on study designs or monitoring their data through wearable technology and smartphone apps. Quantum NLP has the potential to enhance patient outcomes and advance medical knowledge more quickly by enabling more precise and effective clinical trials. By setting the framework for future applications of quantum NLP, this study expands the conversation on the connection between quantum computing and healthcare. © 2025 IEEE.

关键词： Clinical research

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Improving Open-Ended Referring Expression Comprehension via Dual-language Constraints

Improving Open-Ended Referring Expression Comprehension via ...

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Lei, Chenglong Yang, Chao Zheng, Jianbo Hunan University Changsha China

ISBN: (纸本)9798350368741

Open-ended referring expression comprehension focuses on locating the text query within an image via scene knowledge, requiring complex reasoning across the triplet of the image, scene knowledge, and the text query. However, most existing methods struggle to integrate scene knowledge while performing a single-object prediction. To address this issue, we propose VG-DLC, a model that progressively uses scene knowledge and open-ended referring expressions as constraints for reasoning and grounding. Specifically, we first align scene knowledge with the referring expression and the image in sequence, which supports effective relational reasoning and implicitly constrains the open-ended content to corresponding image regions. Next, we explicitly align the referring expression with the image and employ it as a query vector, constraining it to a unique region to achieve a single end-to-end prediction. Extensive experiments on the SK-VG dataset validate the effectiveness of our method, demonstrating that VG-DLC outperforms existing end-to-end approaches. © 2025 IEEE.

关键词： language Constraint Open-ended Referring Expression Comprehension Visual Grounding

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Extending Token Computation for LLM Reasoning 24

Extending Token Computation for LLM Reasoning

引用

8th international conference on Computer Science and Artificial Intelligence, CSAI 2024

作者： Li, Bingli Vargas, Danilo Vasconcellos Laboratory of Intelligent Systems The Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Fukuoka Japan

ISBN: (纸本)9798400718182

Large language Models (LLMs) are pivotal in advancing natural language processing but often struggle with complex reasoning tasks due to inefficient attention distributions. In this paper, we explore the effect of increased computed tokens on LLM performance and introduce a novel method for extending computed tokens in the Chain-of-Thought (CoT) process, utilizing attention mechanism optimization. By fine-tuning an LLM on a domain-specific, highly structured dataset, we analyze attention patterns across layers, identifying inefficiencies caused by non-semantic tokens with outlier high attention scores. To address this, we propose an algorithm that emulates early layer attention patterns across downstream layers to re-balance skewed attention distributions and enhance knowledge abstraction. Our findings demonstrate that our approach not only facilitates a deeper understanding of the internal dynamics of LLMs but also significantly improves their reasoning capabilities, particularly in non-STEM domains. Our study lays the groundwork for further innovations in LLM design, aiming to create more powerful, versatile, and responsible models capable of tackling a broad range of real-world applications. © © 2024 Copyright held by the owner/author(s).

关键词： Semantics

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AI Chatbots as Virtual Teaching Assistants 27th

AI Chatbots as Virtual Teaching Assistants

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27th international conference on Interactive Collaborative Learning, ICL 2024

作者： Kasmaee, Ali Shiraee Mahyar, Hamidreza Faculty of Engineering McMaster University HamiltonON Canada

ISBN: (纸本)9783031835193

Recent advancements in artificial intelligence (AI), particularly in natural language processing (NLP), have led to the development of sophisticated chatbots like ChatGPT. These chatbots excel in answering questions and can significantly improve the learning experience by providing immediate feedback and support to students. They enable continuous learning outside traditional classrooms by understanding and responding to questions with human-like proficiency. However, their responses are limited to the information they were trained on. In this study, we employ the Retrieval-Augmented Generation technique to enable these chatbots to access and retrieve relevant information from external knowledge sources (such as course material) to improve the generation capabilities of these models as virtual teaching assistants. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Chatbots

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Chatbot for Start-Ups and Entrepreneurs with Machine Learning and natural language processing Techniques

Chatbot for Start-Ups and Entrepreneurs with Machine Learnin...

引用

2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Jegan, P. Sidharth, N. Naren Akilandeswari, V. Perumalraja, R. Velammal College of Engineering and Technology Department of Cse Madurai India Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798331509828

In the ever-digitalized environment, entrepreneurs encounter challenges to engage their customers while managing business operations. The study investigates the development of a chatbot specifically for entrepreneurs designed to enhance customer interactions and support. Based on the technique of natural language processing and machine learning, this chatbot can understand and explain hundreds of questions with personalized assistance tailored to user interactions. The study emphasizes that contextual comprehension and adaptability should be integral elements in the design of a chatbot to please the diverse needs of entrepreneurs. Various data from business scenarios-especially customer feedback and market trends-explain how the chatbot is constantly improved to maintain responsiveness and vitality. The study appraised the chatbot's performance through metrics of user satisfaction and engagement rates, significantly pointing out its potential to streamline communication, reduce response times, and enhance the total customer experience. Through the integration of this innovative solution in their operations, entrepreneurs have the possibility to drive responsiveness and agility, leading to business growth and strong customer relationships in a competitive market. © 2025 IEEE.

关键词： Sales

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Tokenization's Potential for Santhali language processing 3rd

Tokenization's Potential for Santhali Language Processing

引用

3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Ohm, Anand Kumar Panday, Aman Kumar Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of CSE National Institute of Technology Jamshedpur Jharkhand India

ISBN: (纸本)9789819743582

Santhali is one of the popular local languages and is mainly spoken by tribal people of States like Jharkhand, Odisha, and West Bengal, as well as a few other parts of the country. Tokenization is one of the major challenges in natural language processing (NLP). Tokenization reduces the length of sentences and paragraphs. There are various methods in use to tokenize different languages depending on the writing and structure of the language. Tokenization is important in many tasks like machine translation, text summarization, chatbot creation, and spam detection. In natural language processing (NLP) most of the Indian spoken languages have high resource availability and have improved technologically in various fields. Still, a low-resource language like SANTHALI does not make much of its existence in technology. The present work is based on the tokenization of Santhali. All available tokenizers to tokenize in the Santhali language have been explored. Ol-Chiki font has been used for the present study. A detailed comparison of tokenization has been represented in the manuscript in the present study. It has been observed that for Santhali a writing system is alphabetic and most of the characters are made of real-world symbols. spaCy model performs better than other available tokenizer models based on the mechanism of the Santhali language. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： natural language processing systems

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KAnoCLIP: Zero-Shot Anomaly Detection through knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration

KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driv...

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Li, Chengyuan Zhou, Suyang Kong, Jieping Qi, Lei Xue, Hui College of Software Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798350368741

Zero-shot anomaly detection (ZSAD) identifies anomalies without needing training samples from the target dataset, essential for scenarios with privacy concerns or limited data. Vision-language models like CLIP show potential in ZSAD but have limitations: relying on manually crafted fixed textual descriptions or anomaly prompts is time-consuming and prone to semantic ambiguity, and CLIP struggles with pixel-level anomaly segmentation, focusing more on global semantics than local details. To address these limitations, We introduce KAnoCLIP, a novel ZSAD framework that leverages vision-language models. KAnoCLIP combines general knowledge from a Large language Model (GPT-3.5) and fine-grained, image-specific knowledge from a Visual Question Answering system (Llama3) via knowledge-Driven Prompt Learning (KnPL). KnPL uses a knowledge-driven (KD) loss function to create learnable anomaly prompts, removing the need for fixed text prompts and enhancing generalization. KAnoCLIP includes the CLIP visual encoder with V-V attention (CLIP-VV), Bi-Directional Cross-Attention for Multi-Level Cross-Modal Interaction (Bi-CMCI), and Conv-Adapter. These components preserve local visual semantics, improve local cross-modal fusion, and align global visual features with textual information, enhancing pixel-level anomaly detection. KAnoCLIP achieves state-of-the-art performance in ZSAD across 12 industrial and medical datasets, demonstrating superior generalization compared to existing methods. © 2025 IEEE.

关键词： Prompt Learning Vision-language Models Zero-shot Anomaly Detection

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