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检索条件"任意字段=International Conference on Natural Language Processing and Knowledge Engineering"

共 26991 条记录，以下是11-20 订阅

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Evaluation and Analysis of the Chinese Semantic Dependency Understanding Ability of Large language Models 13th

Evaluation and Analysis of the Chinese Semantic Dependency U...

引用

13th international conference on natural language processing and Chinese Computing

作者： Shen, Zizhuo Li, Wei Shao, Yanqiu Beijing Language & Culture Univ Beijing Peoples R China

ISBN: (纸本)9789819794362;9789819794379

Semantic Dependency Graph is a framework for representing deep semantic knowledge through flexible graph structures. While recent works indicate that large language models (LLMs) have impressive language and knowledge understanding abilities, it remains unclear whether they can understand this deep semantic knowledge. To explore this problem, we design four prompt-style probing tasks from aspects of semantic structure and semantic relations to adapt the inherent abilities of LLMs. To ensure thorough evaluation, we conduct extensive experiments in both in-context learning (ICL) and supervised fine-tuning (SFT) scenarios. Our findings indicate that the understanding of deep semantic knowledge requires larger parameter scale, especially the understanding of high-order semantic structure knowledge and semantic relation knowledge. Furthermore, our experiments reveal that while LLMs perform well on the in-domain (ID) test set via SFT, their generalization ability on out-of-domain (OOD) test set remains inadequate.

关键词： Semantic Dependency Graph Large language Model Evaluation

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Evaluating Class Membership Relations in knowledge Graphs Using Large language Models

Evaluating Class Membership Relations in Knowledge Graphs Us...

引用

21st international conference on The Semantic Web (ESWC)

作者： Allen, Bradley P. Groth, Paul T. Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9783031789519;9783031789526

A backbone of knowledge graphs are their class membership relations, which assign entities to a given class. As part of the knowledge engineering process, we propose a new method for evaluating the quality of these relations by processing descriptions of a given entity and class using a zero-shot chain-of-thought classifier that uses a natural language intensional definition of a class. We evaluate the method using two publicly available knowledge graphs, Wikidata and CaLiGraph, and 7 large language models. Using the gpt-4-0125-preview large language model, the method's classification performance achieves a macro-averaged F1-score of 0.830 on data from Wikidata and 0.893 on data from CaLiGraph. Moreover, a manual analysis of the classification errors shows that 40.9% of errors were due to the knowledge graphs, with 16.0% due to missing relations and 24.9% due to incorrectly asserted relations. These results show how large language models can assist knowledge engineers in the process of knowledge graph refinement. The code and data are available on Github (https://***/bradleypallen/evaluating-kg-class-memberships-using-llms).

关键词： knowledge engineering large language models knowledge graph refinement natural language generation

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Research on large language model news recommendation technology based on mixed prompt 24

Research on large language model news recommendation technol...

引用

3rd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Wei, Han Qu, Dan Xu, Minchen Peng, Sisi Guo, Zhigang Chen, Gang Information Engineering University Henan Zhengzhou China

ISBN: (纸本)9798400707308

With the emergence of massive news every day leading to information overload, it is difficult for people to select the content they are really interested in from numerous news articles. The language knowledge and strong generalization ability of the large model itself provide the possibility of intelligent news recommendation. This paper does not fine-tune the big language model, but uses the prompt engineering technology of the big language model to design a hybrid prompt engineering instruction combining prompt words, context learning and thought chain prompt to stimulate the recommendation performance of the big language model, and realizes the intelligent news recommendation of the big language model. © 2024 Copyright held by the owner/author(s).

关键词： engineering education

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knowledge-Enhanced Utterance Domain Classification with Keywords-Assisted Concept Denoising Network 13th

Knowledge-Enhanced Utterance Domain Classification with Keyw...

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13th international conference on natural language processing and Chinese Computing

作者： Huang, Peijie Huang, Boxi Xu, Yuhong Chen, Weiting Li, Jia South China Agr Univ Coll Math & Informat Guangzhou Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Utterance Domain Classification (UDC) is essential for Spoken language Understanding (SLU), a task analogous to short text classification. Short texts are often challenging to understand due to their lack of context, necessitating the enrichment of their semantic representation with supplementary information such as concepts from external knowledge bases. However, the inclusion of concepts introduces noise, making the selection of valuable concepts challenging. This paper proposes a UDC method employing keyword-guided signals to enhance the purity of external knowledge. We use two keyword extraction strategies to construct two types of keywords. A keyword-assisted concept denoising module addresses the concept noise problem, and a knowledge injection module is designed to better integrate concepts into the model. Experimental results on two Chinese SLU datasets demonstrate that our model achieves state-of-the-art performance.

关键词： Utterance domain classification Spoken language understanding knowledge-enhanced methods

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RAVL: A Retrieval-Augmented Visual language Model Framework for knowledge-Based Visual Question Answering 13th

RAVL: A Retrieval-Augmented Visual Language Model Framework ...

引用

13th international conference on natural language processing and Chinese Computing

作者： Chai, Naiquan Zou, Dongsheng Liu, Jiyuan Wang, Hao Yang, Yuming Song, Xinyi Chongqing Univ Sch Comp Sci Chongqing Peoples R China

ISBN: (纸本)9789819794362;9789819794379

knowledge-based visual question answering (VQA) requires external knowledge in addition to the image content to answer questions. Recent studies convert images to text descriptions and then generate answers or acquire implicit knowledge using a large language model (LLM). These methods achieve encouraging results with the strong knowledge retrieval and reasoning capabilities of LLMs. However, methods that incorporate LLMs are limited by the discrepancies between images and their text descriptions presented to LLMs. To address this challenge, we present RAVL, a retrieval-augmented visual language model (VLM) framework for knowledge-based VQA. Specifically, we first fine-tune a VLM on the knowledge-based VQA task with inputs consisting of retrieved knowledge and image-question pairs to adapt the VLM to inputs with retrieved knowledge. After that, we adapt the retrieval module to the fine-tuned VLM using supervision signals provided by the VLM, enabling the retrieved knowledge to improve the VLM perplexity. RAVL overcomes the limitation of visual information loss and improves the effectiveness of VLMs with external knowledge. We conduct experiments on OK-VQA dataset and our method achieves 65.73% accuracy, surpassing the previous state-of-the-art method (+3.63%).

关键词： knowledge-based visual question answering Retrieval augmentation Visual language model

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ParaFusion-Extended: Large Scale Paraphrase Dataset Integrating Lexico-Phrasal knowledge 30th

ParaFusion-Extended: Large Scale Paraphrase Dataset Integrat...

引用

30th international conference on Computational and Experimental engineering and Sciences

作者： Jayawardena, Lasal Yapa, Prasan Robert Gordon Univ Garthdee HouseGarthdee Rd Aberdeen AB10 7AQ Scotland Inst Informat Technol 57 Ramakrishna Rd Colombo 00600 Sri Lanka

ISBN: (纸本)9783031774911;9783031774898;9783031774881

Paraphrasing, the art of rephrasing text while retaining its original meaning, lies at the core of natural language understanding and generation. With the rise of demand for more domain-specialized models, high-quality data is more valued than ever;this includes paraphrasing. ParaFusion-Extend (PFE) is a large-scale dataset driven by Large language Models incorporating lexical and phrasal knowledge. The dataset is curated to contain high-quality diverse paraphrase pairs and also separate knowledge bases that could be used for research work and data augmentation models. We show that PFE offers around at least a 30% increase in syntactic and lexical diversity compared to the original data sources that are commonly used. We demonstrate the effectiveness of PFE on several downstream tasks such as few-shot learning and training on sentence embeddings. We utilize a gold-standard evaluation scheme, which is further strengthened by human evaluation that shows the potential of PFE in advancing paraphrase generation.

关键词： paraphrase generation natural language processing natural language generation knowledge representation large language models data augmentation sentence embeddings few-shot learning

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ProSide: knowledge Projector and Sideway for Pre-trained language Models 13th

ProSide: Knowledge Projector and Sideway for Pre-trained Lan...

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13th international conference on natural language processing and Chinese Computing

作者： He, Chaofan Lu, Gewei Shen, Liping Shanghai Jiao Tong Univ AI Inst Dept Comp Sci & Engn X LANCE LabMoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Research indicates that incorporating external knowledge into pre-trained language models (PLMs) can enhance their performance on knowledge-driven downstream tasks. However, most approaches either require retraining the model or hardly maintain complete information of knowledge graphs (KGs). In this paper, we introduce a simple but effective ProSide module for PLMs, which includes two components: knowledge Projector and knowledge Sideway. knowledge Projector transforms knowledge representation from entity embedding in KG space to semantic space, while knowledge Sideway retains complete KG information in sideway modules through pre-training. Our ProSide can accommodate different frozen language models, without the need for retraining them. Results on three common knowledge-driven tasks demonstrate that our ProSide method enhances model performance and reaches state-of-the-art level. Additionally, we provide further analysis and case studies to illustrate the mechanism from the perspective of representation space. The code and models are publicly available at Github (Code and models are publicly available at https://***/hcffffff/ProSide).

关键词： knowledge Graph Pre-trained language Models Parameter Efficient Fine-tuning Method

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LaiDA: Linguistics-Aware In-Context Learning with Data Augmentation for Metaphor Components Identification 13th

LaiDA: Linguistics-Aware In-Context Learning with Data Augme...

引用

13th international conference on natural language processing and Chinese Computing

作者： Liu, Hongde He, Chenyuan Meng, Feiyang Niu, Changyong Jia, Yuxiang Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Metaphor Components Identification (MCI) contributes to enhancing machine understanding of metaphors, thereby advancing downstream natural language processing tasks. However, the complexity, diversity, and dependency on context and background knowledge pose significant challenges for MCI. Large language models (LLMs) offer new avenues for accurate comprehension of complex natural language texts due to their strong semantic analysis and extensive commonsense knowledge. In this research, a new LLM-based framework is proposed, named Linguistics-aware In-context Learning with Data Augmentation (LaiDA). Specifically, ChatGPT and supervised fine-tuning are utilized to tailor a high-quality dataset. LaiDA incorporates a simile dataset for pre-training. A graph attention network encoder generates linguistically rich feature representations to retrieve similar examples. Subsequently, LLM is fine-tuned with prompts that integrate linguistically similar examples. LaiDA ranked 2nd in Subtask 2 of NLPCC2024 Shared Task 9, demonstrating its effectiveness. Code and data are available at https://***/WXLJZ/LaiDA.

关键词： Metaphor Components Identification Linguistics-aware In-context Learning Data Augmentation Large language Models

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Sparse Mixture of Experts language Models Excel in knowledge Distillation 13th

Sparse Mixture of Experts Language Models Excel in Knowledge...

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13th international conference on natural language processing and Chinese Computing

作者： Xu, Haiyang Liu, Haoxiang Gong, Wei Wang, Hai Deng, Xianjun Univ Sci & Technol China Hefei 230026 Anhui Peoples R China Alibaba Grp Hangzhou Peoples R China Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9789819794362;9789819794379

knowledge distillation is an effective method for reducing the computational overhead of large language models. However, recent optimization efforts in distilling large language models have primarily focused on loss functions and training methodologies, with limited attention given to structural improvements of student models. This is largely due to the challenges posed by cross-architecture distillation and the substantial computational resources required for modifying model structures. To address these issues, we introduce a novel method that integrates a sparse mixture of experts (MoE) architecture with low-rank adaptation (LoRA). This combination not only bolsters the capabilities of the student model but also facilitates knowledge distillation using MoE without the necessity of continued pretraining. Experimental results indicate that our approach enhances the model's capabilities compared to dense model distillation, achieving superior performance across a multitude of tasks. We will release our code at https://***/sprogxhy/***.

关键词： Mixture of experts knowledge distillation language models

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Deep Reasoning of Large Models Based on knowledge Graph 16

Deep Reasoning of Large Models Based on Knowledge Graph

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2024 16th international conference on Graphics and Image processing, ICGIP 2024

作者： Chenghao, Cao Du, Zhenlong Li, Xiaoli College of Computer and Information Engineering Nanjing Tech University China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Although large language models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination issues in scenarios requiring deep reasoning. Incorporating external knowledge into LLM reasoning can alleviate some of these issues. This paper proposes an efficient graph search method that integrates knowledge graphs. A knowledge graph is firstly constructed from the original document. This graph is then partitioned into multiple relevant communities using a community detection algorithm, and natural language summaries for each community are generated using an LLM. During the search phase, the LLM performs beam search on the knowledge graph, iteratively exploring multiple potential reasoning paths on the graph until it determines that it can answer the question based on the current reasoning path. Experiments demonstrate that our search method has lower computational costs and better generalizability, outperforming even large LLMs like GPT-4 in certain vertical domains. © 2025 SPIE.

关键词： knowledge graph

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