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检索条件"任意字段=13th International Conference on Natural Language Processing and Chinese Computing"

共 1317 条记录，以下是31-40 订阅

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Nominal Compound Chain Extraction Enhanced by Chain-of-thought Information 13th

Nominal Compound Chain Extraction Enhanced by Chain-of-Thoug...

引用

13th international conference on natural language processing and chinese computing

作者： Li, Chenyang Zhang, Long Guo, Hui Zheng, Qiusheng Cent China Normal Univ Fac Artificial Intelligence Educ Wuhan Peoples R China Zhongyuan Univ Technol Sch Cyberspace Secur Zhengzhou Peoples R China

ISBN: (纸本)9789819794423;9789819794430

In traditional lexical chain extraction tasks, researchers typically focus on identifying simple lexical items based on surface grammatical relations, often overlooking compound words with underlying semantic frameworks. To address this limitation, the task of Nominal Compound Chain Extraction (NCCE) has emerged. this task aims to identify and cluster nominal compounds sharing the same semantic theme, thereby providing richer semantic information and facilitating a deeper understanding of the latent themes within documents. In this study, we fine-tune the large language model Qwen2-0.5b, employ data augmentation techniques, and introduce Chain-of-thought (CoT) information from large models as an auxiliary aid, significantly enhancing the model's document comprehension capabilities.

关键词： Nominal Compound chain Large language Model Data Augmentation Chain-of-thought

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LLM Assists Hypothesis Generation and Testing for Deliberative Questions 13th

LLM Assists Hypothesis Generation and Testing for Deliberati...

引用

13th international conference on natural language processing and chinese computing

作者： Wang, Fuchun Zhou, Xian Hu, Wenpeng Luo, Zhunchen Luo, Wei Bai, Xiaoying PLA Acad Mil Sci Informat Res Ctr Mil Sci Beijing 100080 Peoples R China

ISBN: (纸本)9789819794331;9789819794348

In the present era, the proliferation and widespread adoption of Large language Models (LLMs) have substantially elevated the capacity for understanding and reasoning in various textual tasks. Yet, there is a current shortage of effective methodologies for tackling Deliberative Questions, which require thoughtful consideration and may encompass evolving scenarios. To bridge this gap, this paper introduces a systematic approach that employs relevant impact analysis to build a cognitive framework, whose results guide LLMs to assist generate hypotheses and testing, known as Abductive Reasoning, for addressing deliberative questions, with the goal of fostering a deliberative thinking mindset. Our comprehensive evaluation on deliberative questions gathered from Reddit and Zhihu showed that pipeline generates superior answers in 65.5% and 73.6% of the cases on our datasets in ChatGPT and QwenMAX view, particularly excelling with an 88.3% dominance rate in addressing reasoning-type questions and and 84.4% in predictive-type questions.

关键词： Deliberative Question Large language Model Abductive Reasoning

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Regularized Continual Learning for Large-Scale language Models via Probing 13th

Regularized Continual Learning for Large-Scale Language Mode...

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13th international conference on natural language processing and chinese computing

作者： Song, Xingshen Ren, Tianxiang Deng, Jinsheng Natl Univ Def Technol Changsha 410073 Peoples R China

ISBN: (纸本)9789819794362;9789819794379

the rapid development of large-scale language models has garnered widespread interest from both academia and industry. Efficiently applying those models across various domains is now posing a challenge to researchers. High training costs and the relative scarcity of domain-specific data have rendered continual learning on general pretrained language models as one preferable approach. In this paper, we provide a comprehensive analysis and modification of these continual learning strategies for large language models, as they were initially designed for encoderonly architectures. then a probing algorithm for the token representation shift was proposed to better alleviate forgetting. Additionally, corresponding evaluation metrics were modified for quantitative analysis of our methods. through the experiment across three different domains, we verified the effectiveness of continual learning and probing algorithms on recent models. Results showed that knowledge distillation outperforms other methods in cross-domain continual learning. Moreover, the introduction of probing can further enhance the accuracy with a relatively small calculation budget.

关键词： Large-scale language model Continual learning Probing Classifier

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Resolving Unseen Rumors with Retrieval-Augmented Large language Models 13th

Resolving Unseen Rumors with Retrieval-Augmented Large Langu...

引用

13th international conference on natural language processing and chinese computing

作者： Chen, Lei Wei, Zhongyu Fudan Univ Sch Data Sci Shanghai Peoples R China Fudan Univ Res Inst Intelligent & Complex Syst Shanghai Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Social media has become the primary source of information for individuals, yet much of this information remains unverified. the rise of generative artificial intelligence has further accelerated the creation of unverified content. Adaptive rumor resolution systems are imperative for maintaining information integrity and public trust. Traditional methods have relied on encoder-based frameworks to enhance rumor representation and propagation characteristics. However, these models are often small in scale and lack generalizability for unforeseen events. Recent advances in Large language Models show promise but are unreliable in discerning truth from falsehood. Our work leverages LLMs by creating a testbed for predicting unprecedented rumors and designing a retrieval-augmented framework that integrates historical knowledge and collective intelligence. Experiments on two real-world datasets demonstrate the effectiveness of our proposed framework.

关键词： Rumor Detection Retrieval-Augmented LLMs

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Explore the Textual Perception Ability on the Images for Multimodal Large language Models 13th

Explore the Textual Perception Ability on the Images for Mul...

引用

13th international conference on natural language processing and chinese computing

作者： Kuang, Jiayi Ouyang, Jiarui Shen, Ying Sun Yat Sen Univ Guangzhou Guangdong Peoples R China

ISBN: (纸本)9789819794423;9789819794430

chinese Spell Checking or chinese Spelling Correction (CSC) in writing assistants has significantly enhanced users' text quality. Multimodal CSC expands this task into the multimodal domain and introduces a new research challenge: understanding textual information at the image level. the traditional two-stage strategy, which directly maps images to text before performing checking and correction, often faces issues such as the many-to-one mapping problem of fake characters and misrecognizing fake characters as correct ones. To address these issues, we propose a unified one-stage framework based on the emerging Multimodal Large language Models (MLLMs) to achieve a discrete semantic understanding of text directly at the image level, thus alleviating the image-to-text mapping problem. Additionally, we introduce an adaptation strategy for MLLM in the multimodal CSC task, enhancing its few-shot learning capability through in-context learning with prompt design. We evaluate different MLLM base models and verify the effectiveness of this one-stage framework, demonstrating performance that matches or even surpasses fine-tuned baseline models without fine-tuning. Furthermore, we conduct a series of analyses that provide insights into visual perception and text correction.

关键词： Multimodal chinese Spell Checking Multimodal Large language Models

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Overview of the NLPCC 2024 Shared Task 10: Regulating Large language Models 13th

Overview of the NLPCC 2024 Shared Task 10: Regulating Large ...

引用

13th international conference on natural language processing and chinese computing

作者： Wang, Chenxi Xu, Ziwen Wang, Mengru Chen, Xiang Deng, Shumin Zhang, Ningyu Zhejiang Univ Hangzhou Peoples R China Natl Univ Singapore NUS NCS Joint Lab Singapore Singapore

ISBN: (纸本)9789819794423;9789819794430

the rise of large language models has brought about significant advancements in the field of natural language processing. However, these models often have the potential to generate content that can be hallucinatory or toxic. To this end, we organize NLPCC 2024 Shared Task 10, i.e., Regulating Large language Models, which includes two subtasks: Multimodal Hallucination Detection for Multimodal Large language Models and Detoxifying Large language Models. In the first task, we construct a fine-grained and human-calibrated benchmark for multimodal hallucination detection, named MHaluBench, which contains 1270 training data, 600 validation data and 300 test data. the second task draws on the SafeEdit benchmark, containing 4050 training data, 2700 validation data and 540 test data. the aim is to design and implement strategies to prevent large language models from generating toxic content. this paper presents details of the shared tasks, datasets, evaluation metrics and evaluation results.

关键词： Large language Model Hallucination Detoxifying

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Multilingual Temporal Answer Grounding in Video Corpus with Enhanced Visual-Textual Integration 13th

Multilingual Temporal Answer Grounding in Video Corpus with ...

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13th international conference on natural language processing and chinese computing

作者： Ma, Tianxing Hu, Yueyue Jiang, Shuang Yin, Zhenhao Zang, Tianning Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China

ISBN: (纸本)9789819794423;9789819794430

this paper presents the experiment scheme of Team IIEleven in the NLPCC 2024 shared task, Multilingual Medical Instructional Video Question Answering (MMIVQA) challenge, Track 3: Multilingual Temporal Answer Grounding in Video Corpus (mTAGVC). the objective of the mTAGVC task is to identify video spans most relevant to the given questions from a large multilingual medical instructional video corpus. We propose an Multilingual Visual-Textual Span Enhancement (MVTSE) method, which simultaneously performs two subtasks of video corpus retrieval and temporal answering grounding in video, and further strengthens the cross language ability and comparative learning performance of the model by subtitle text supplementation, multilingual data augmentation and hard sample selection method. the experimental results on the given dataset show that the proposed method achieves state-of-the-art (SOTA) performance, and ranks first in the mTAGVC track of the MMIVQA challenge.

关键词： Multimodal Multilingual Temporal Answer Grounding Video Retrieval

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Overview of the NLPCC2024 Shared Task 6: Scientific Literature Survey Generation 13th

Overview of the NLPCC2024 Shared Task 6: Scientific Literatu...

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13th international conference on natural language processing and chinese computing

作者： Tian, Yangjie Cul, Xungang Li, Aijia Zhang, He Xu, Ruohua Li, Yunfeng Liu, Ming Kexin Technol Beijing Peoples R China Deakin Univ Docklands Australia

ISBN: (纸本)9789819794423;9789819794430

With the relentless growth in the volume of academic publications and the accelerating speed of scholarly communication, the time researchers dedicate to literature surveys has become increasingly substantial. Automatic literature survey generation offers a valuable solution, liberating researchers from the time-intensive task of manually surveying the literature. We organized the NLPCC2024 Shared Task 6 for scientific literature survey generation. this paper will summarize the task information, the data set, the methods used by participants and the final results. Furthermore, we will discuss key findings and challenges for scientific literature survey generation in the scientific domain.

关键词： Scientific Literature Survey Generation Large language Model Generative Pre-trained Transformer (GPT) In-context Learning Retrieval-Augmented Generation (RAG)

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Steganographic Text Generation Based on Large language Models in Dialogue Scenarios 13th

Steganographic Text Generation Based on Large Language Model...

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13th international conference on natural language processing and chinese computing

作者： Zeng, Qingwei Wang, Kaixi Qingdao Univ Sch Comp Sci & Technol Qingdao 266071 Peoples R China

ISBN: (纸本)9789819794362;9789819794379

the text comprehension and generation capabilities of large language models (LLMs) have become extremely powerful, fulfilling the needs of many application scenarios. And online chatting is very popular, which provides new application scenarios for text steganography. In this paper, we propose a steganography scheme that generates conversational text with specified topics and grammars based on the LLMs. the algorithm maps a secret as prompts input into the LLMs to generate steganographic text. To improve imperceptibility, after statistical analysis of tense usage in different topics, its frequencies are used to code the tenses in variable lengths to ensure that the generated steganographic text aligns the tense of the generated steganographic text with that of normal text. the comprehensive analyses are performed to evaluate the generated steganographic text. the results show that in term of Perplexity, a steganographic text has good imperceptibility, and the metrics such as Kullback-Leibler divergence (KL) and Jensen-Shannon divergence (JS) indicate it is statistically difficult to distinguish the generated steganographic text from normal text, which indicates the steganographic text has strong anti-steganalysis ability.

关键词： Large language models NLP application Text steganography Conversational generation

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chinese Grammar Correction Model Based on Semantic Enhancement and Feedback Mechanism 13th

Chinese Grammar Correction Model Based on Semantic Enhanceme...

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13th international conference on natural language processing and chinese computing

作者： Zhang, Zhujian Zhao, Peiyu Liu, Bo Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China

ISBN: (纸本)9789819794393;9789819794409

the chinese grammar detection and correction system can automatically identify error positions and correct erroneous characters. Currently, there are still challenges in the research, such as the limited size of publicly available chinese error correction datasets, the diversity of chinese grammar error forms, and the difficulty in representing the distribution of grammar errors. Additionally, pre-trained chinese language models lack the ability to differentiate between similar characters or words, affecting the accuracy of chinese sentence detection and correction. In this paper, we propose a model based on grammar enhancement and feedback mechanism. During the model training phase, the Pairwise Character Interaction (PCI) module is used to enhance the grammatical representation of the text encoder. It employs various gating mechanisms based on character pairs to highlight the semantic features of grammatical errors at erroneous character positions. Furthermore, during the testing phase, our model (PCIFM) utilizes a feedback mechanism to edit and iteratively correct erroneous results. the proposed model was evaluated on publicly available CGED datasets, achieving the highest detection F1 scores on the test sets of CGED 2017, CGED 2018, and CGED 2020, respectively.

关键词： chinese Text Error Correction Pairwise Character Interaction Feedback Mechanism

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