检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

14,600 篇 会议
627 篇 期刊文献
101 册 图书
37 篇 学位论文

馆藏范围

15,364 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

10,996 篇 工学
- 10,331 篇 计算机科学与技术...
- 5,391 篇 软件工程
- 1,449 篇 信息与通信工程
- 957 篇 电气工程
- 878 篇 控制科学与工程
- 433 篇 生物工程
- 222 篇 网络空间安全
- 218 篇 化学工程与技术
- 185 篇 机械工程
- 177 篇 生物医学工程（可授...
- 141 篇 电子科学与技术（可...
- 101 篇 仪器科学与技术
- 100 篇 安全科学与工程
2,447 篇 理学
- 1,138 篇 数学
- 652 篇 物理学
- 503 篇 生物学
- 379 篇 统计学（可授理学、...
- 240 篇 系统科学
- 231 篇 化学
2,381 篇 管理学
- 1,726 篇 图书情报与档案管...
- 742 篇 管理科学与工程(可...
- 235 篇 工商管理
- 104 篇 公共管理
1,823 篇 文学
- 1,771 篇 外国语言文学
- 169 篇 中国语言文学
503 篇 医学
- 301 篇 临床医学
- 282 篇 基础医学(可授医学...
- 111 篇 公共卫生与预防医...
275 篇 法学
- 245 篇 社会学
237 篇 教育学
- 225 篇 教育学
100 篇 农学
93 篇 经济学
10 篇 艺术学
7 篇 哲学
4 篇 军事学

主题

3,563 篇 natural language...
1,792 篇 natural language...
950 篇 computational li...
752 篇 semantics
678 篇 machine learning
620 篇 deep learning
518 篇 natural language...
376 篇 computational mo...
368 篇 accuracy
355 篇 training
351 篇 sentiment analys...
349 篇 large language m...
337 篇 feature extracti...
313 篇 data mining
289 篇 speech processin...
262 篇 transformers
255 篇 speech recogniti...
234 篇 neural networks
217 篇 iterative method...
216 篇 support vector m...

机构

85 篇 carnegie mellon ...
51 篇 university of ch...
45 篇 tsinghua univers...
44 篇 carnegie mellon ...
42 篇 zhejiang univers...
41 篇 national univers...
35 篇 univ chinese aca...
35 篇 nanyang technolo...
35 篇 carnegie mellon ...
34 篇 university of sc...
34 篇 university of wa...
33 篇 alibaba grp peop...
32 篇 gaoling school o...
32 篇 stanford univers...
30 篇 tsinghua univ de...
30 篇 school of artifi...
28 篇 peking universit...
27 篇 harbin institute...
27 篇 language technol...
26 篇 univ sci & techn...

作者

55 篇 zhou guodong
50 篇 neubig graham
46 篇 liu yang
39 篇 sun maosong
36 篇 zhang min
34 篇 liu qun
31 篇 smith noah a.
29 篇 lapata mirella
28 篇 schütze hinrich
26 篇 wen ji-rong
26 篇 liu zhiyuan
24 篇 chang kai-wei
23 篇 zhou jie
23 篇 yang diyi
23 篇 zhao hai
23 篇 zhao wayne xin
22 篇 wang wei
21 篇 chua tat-seng
20 篇 dredze mark
18 篇 biemann chris

语言

13,828 篇 英文
1,418 篇 其他
123 篇 中文
18 篇 法文
14 篇 土耳其文
2 篇 德文
2 篇 西班牙文
2 篇 俄文

检索条件"任意字段=Conference on empirical methods in natural language processing"

共 15365 条记录，以下是1241-1250 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

FROM WORDS TO WIRES: Generating Functioning Electronic Devices from natural language Descriptions

FROM WORDS TO WIRES: Generating Functioning Electronic Devic...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Jansen, Peter Univ Arizona Tucson AZ 85721 USA

ISBN: (纸本)9798891760615

In this work, we show that contemporary language models have a previously unknown skill - the capacity for electronic circuit design from high-level textual descriptions, akin to code generation. We introduce two benchmarks: PINS100, assessing model knowledge of electrical components, and MICRO25, evaluating a model's capability to design common micro-controller circuits and code in the ARDUINO ecosystem that involve input, output, sensors, motors, protocols, and logic - with models such as GPT-4 and Claude-V1 achieving between 60% to 96% PASS@ 1 on generating full devices. We include six case studies of using language models as a design assistant for moderately complex devices, such as a radiation-powered random number generator, an emoji keyboard, a visible spectrometer, and several assistive devices, while offering a qualitative analysis performance, outlining evaluation challenges, and suggesting areas of development to improve complex circuit design and practical utility. With this work, we aim to spur research at the juncture of natural language processing and electronic design.

关键词： Random number generation

来源：评论

学校读者我要写书评

暂无评论

Large language Models for Propaganda Span Annotation

Large Language Models for Propaganda Span Annotation

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Hasanain, Maram Ahmad, Fatema Alam, Firoj Qatar Computing Research Institute HBKU Qatar

ISBN: (纸本)9798891761681

The use of propagandistic techniques in online content has increased in recent years aiming to manipulate online audiences. Fine-grained propaganda detection and extraction of textual spans where propaganda techniques are used, are essential for more informed content consumption. Automatic systems targeting the task over lower resourced languages are limited, usually obstructed by lack of large scale training datasets. Our study investigates whether Large language Models (LLMs), such as GPT-4, can effectively extract propagandistic spans. We further study the potential of employing the model to collect more cost-effective annotations. Finally, we examine the effectiveness of labels provided by GPT-4 in training smaller language models for the task. The experiments are performed over a large-scale in-house manually annotated dataset. The results suggest that providing more annotation context to GPT-4 within prompts improves its performance compared to human annotators. Moreover, when serving as an expert annotator (consolidator), the model provides labels that have higher agreement with expert annotators, and lead to specialized models that achieve state-of-the-art over an unseen Arabic testing set. Finally, our work is the first to show the potential of utilizing LLMs to develop annotated datasets for propagandistic spans detection task prompting it with annotations from human annotators with limited expertise. All scripts and annotations will be shared with the community. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Construction and Application of Text Classification Model under natural language processing 24

Construction and Application of Text Classification Model un...

引用

International conference on Modeling, natural language processing and Machine Learning (CMNM)

作者： Sun, Zhongnuo Gao, Pan Technol Vocat Coll Dezhou Dezhou Lectromech Engn Sch Yucheng 251200 Shandong Peoples R China

ISBN: (纸本)9798400709760

With the prevalence of the Internet and various types of social media, our daily life is surrounded by a huge amount of text information, which can provide us with the convenience of accessing information and communication, but also brings challenges in management and maintenance. In the face of the current practical needs, traditional machine learning methods, such as TF-IDF, SVM, Naive Bayes need to rely on manual completion of the feature design, and can not solve the problem of data sparsity and high-dimensional feature vectors, so that the accuracy of text classification is insufficient. In this regard, based on the deep learning algorithm, this paper will propose a set of Chinese text classification model based on Word2vec and TextCNN to realize the automatic classification function of a large number of texts. Practice has proved that the test precision of this classification model is 94.12%, the recall is 92.19%, and the F1 value is 93.11%. Compared with the traditional TF-IDF, SVM and Naive Bayes methods, all the evaluation indexes are obviously improved, and the actual application effect is better than that of the original TextCNN. The overall design is in line with expectations and has certain application and promotion value.

关键词： natural language processing Text classification Convolutional neural network Word2vec TextCNN

来源：评论

学校读者我要写书评

暂无评论

Knowledge-Augmented language Model Verification

Knowledge-Augmented Language Model Verification

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Baek, Jinheon Jeong, Soyeong Kang, Minki Park, Jong C. Hwang, Sung Ju Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798891760608

Recent language Models (LMs) have shown impressive capabilities in generating texts with the knowledge internalized in parameters. Yet, LMs often generate the factually incorrect responses to the given queries, since their knowledge may be inaccurate, incomplete, and outdated. To address this problem, previous works propose to augment LMs with the knowledge retrieved from an external knowledge source. However, such approaches often show suboptimal text generation performance due to two reasons: 1) the model may fail to retrieve the knowledge relevant to the given query, or 2) the model may not faithfully reflect the retrieved knowledge in the generated text. To overcome these, we propose to verify the output and the knowledge of the knowledge-augmented LMs with a separate verifier, which is a small LM that is trained to detect those two types of errors through instruction-finetuning. Then, when the verifier recognizes an error, we can rectify it by either retrieving new knowledge or generating new text. Further, we use an ensemble of the outputs from different instructions with a single verifier to enhance the reliability of the verification processes. We validate the effectiveness of the proposed verification steps on multiple question answering benchmarks, whose results show that the proposed verifier effectively identifies retrieval and generation errors, allowing LMs to provide more factually correct outputs. Our code is available at https://***/JinheonBaek/KALMV.

关键词： Errors

来源：评论

学校读者我要写书评

暂无评论

An empirical Evaluation of Out-of-Distribution Detection Using Pretrained language Models 5

An Empirical Evaluation of Out-of-Distribution Detection Usi...

引用

5th International conference on Control and Robotics (ICCR)

作者： Yoon, Byungmu Kim, Jaeyoung Gachon Univ Seongnam Si Gyeonggi Do South Korea

ISBN: (纸本)9798350307627

In natural language processing (NLP) tasks, detecting out-of-distribution (OOD) samples is essential to safely deploy a language model in real-world problems. Recently, several studies report that pre-trained language models (PLMs) accurately detect OOD data compared to LSTM, but we empirically find that PLMs show sub-par OOD detection performance when (1) OOD samples have similar semantic representation to in-distribution (IND) samples and (2) PLMs are finetuned under data scarcity settings. To alleviate above issues, state-of-the-art uncertainty quantification (UQ) methods can be used, but the comprehensive analysis of UQ methods with PLMs has received little consideration. In this work, we investigate seven UQ methods with PLMs and show their effectiveness in the text classification task.

关键词： OOD Detection Pre-trained language Models(PLMs) Uncertainty Quantification(UQ)methods

来源：评论

学校读者我要写书评

暂无评论

Fisher Information-based Efficient Curriculum Federated Learning with Large language Models

Fisher Information-based Efficient Curriculum Federated Lear...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Liu, Ji Ren, Jiaxiang Jin, Ruoming Zhang, Zijie Zhou, Yang Valduriez, Patrick Dou, Dejing HiThink Research Zhejiang Hangzhou China Auburn University Auburn United States Kent State University Kent United States University of Texas at San Antonio San Antonio United States Inria University of Montpellier CNRS LIRMM France LNCC Petropolis Brazil Fudan University Shanghai China BEDI Cloud Beijing China

ISBN: (纸本)9798891761643

As a promising paradigm to collaboratively train models with decentralized data, Federated Learning (FL) can be exploited to fine-tune Large language Models (LLMs). While LLMs correspond to huge size, the scale of the training data significantly increases, which leads to tremendous amounts of computation and communication costs. The training data is generally non-Independent and Identically Distributed (non-IID), which requires adaptive data processing within each device. Although Low-Rank Adaptation (LoRA) can significantly reduce the scale of parameters to update in the fine-tuning process, it still takes unaffordable time to transfer the low-rank parameters of all the layers in LLMs. In this paper, we propose a Fisher Information-based Efficient Curriculum Federated Learning framework (FibecFed) with two novel methods, i.e., adaptive federated curriculum learning and efficient sparse parameter update. First, we propose a fisher information-based method to adaptively sample data within each device to improve the effectiveness of the FL fine-tuning process. Second, we dynamically select the proper layers for global aggregation and sparse parameters for local update with LoRA so as to improve the efficiency of the FL fine-tuning process. Extensive experimental results based on 10 datasets demonstrate that FibecFed yields excellent performance (up to 45.35% in terms of accuracy) and superb fine-tuning speed (up to 98.61% faster) compared with 17 baseline approaches). Our code will be publicly available. © 2024 Association for Computational Linguistics.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

ICU: Conquering language Barriers in Vision-and-language Modeling by Dividing the Tasks into Image Captioning and language Understanding

ICU: Conquering Language Barriers in Vision-and-Language Mod...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Wu, Guojun Univ Zurich Dept Computat Linguist Zurich Switzerland

ISBN: (纸本)9798891760615

Most multilingual vision-and-language (V&L) research aims to accomplish multilingual and multimodal capabilities within one model. However, the scarcity of multilingual captions for images has hindered the development. To overcome this obstacle, we propose ICU, Image Caption Understanding, which divides a V&L task into two stages: a V&L model performs image captioning in English, and a multilingual language model (mLM), in turn, takes the caption as the alt text and performs cross-lingual language understanding. The burden of multilingual processing is lifted off V&L model and placed on mLM. Since the multilingual text data is relatively of higher abundance and quality, ICU can facilitate the conquering of language barriers for V&L models. In experiments on two tasks across 9 languages in the IGLUE benchmark, we show that ICU can achieve new state-of-the-art results for five languages, and comparable results for the rest.

关键词： Modeling languages

来源：评论

学校读者我要写书评

暂无评论

Are Large language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisions

Are Large Language Models Good Classifiers? A Study on Edit ...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Ruan, Qian Kuznetsov, Ilia Gurevych, Iryna Technical University of Darmstadt Germany

ISBN: (纸本)9798891761643

Classification is a core NLP task architecture with many potential applications. While large language models (LLMs) have brought substantial advancements in text generation, their potential for enhancing classification tasks remains underexplored. To address this gap, we propose a framework for thoroughly investigating fine-tuning LLMs for classification, including both generation- and encoding-based approaches. We instantiate this framework in edit intent classification (EIC), a challenging and underexplored classification task. Our extensive experiments and systematic comparisons with various training approaches and a representative selection of LLMs yield new insights into their application for EIC. We investigate the generalizability of these findings on five further classification tasks. To demonstrate the proposed methods and address the data shortage for empirical edit analysis, we use our best-performing EIC model to create Re3-Sci2.0, a new large-scale dataset of 1,780 scientific document revisions with over 94k labeled edits. The quality of the dataset is assessed through human evaluation. The new dataset enables an in-depth empirical study of human editing behavior in academic writing. We make our experimental framework, models and data2 publicly available. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Aligning language Models to Explicitly Handle Ambiguity

Aligning Language Models to Explicitly Handle Ambiguity

引用

29th conference on empirical methods in natural language processing

作者： Kim, Hyuhng Joon Kim, Youna Park, Cheonbok Kim, Junyeob Park, Choonghyun Yoo, Kang Min Lee, Sang-goo Kim, Taeuk Seoul Natl Univ Seoul 1 South Korea NAVER Cloud Seongnam South Korea KAIST AI Daejeon South Korea NAVER AI Lab Seongnam South Korea IntelliSys Seoul South Korea Hanyang Univ Seoul South Korea

ISBN: (纸本)9798891761643

In interactions between users and language model agents, user utterances frequently exhibit ellipsis (omission of words or phrases) or imprecision (lack of exactness) to prioritize efficiency. This can lead to varying interpretations of the same input based on different assumptions or background knowledge. It is thus crucial for agents to adeptly handle the inherent ambiguity in queries to ensure reliability. However, even state-of-the-art large language models (LLMs) still face challenges in such scenarios, primarily due to the following hurdles: (1) LLMs are not explicitly trained to deal with ambiguous utterances;(2) the degree of ambiguity perceived by the LLMs may vary depending on the possessed knowledge. To address these issues, we propose Alignment with Perceived Ambiguity (APA), a novel pipeline that aligns LLMs to manage ambiguous queries by leveraging their own assessment of ambiguity (i.e., perceived ambiguity). Experimental results on question-answering datasets demonstrate that APA empowers LLMs to explicitly detect and manage ambiguous queries while retaining the ability to answer clear questions. Furthermore, our finding proves that APA excels beyond training with gold-standard labels, especially in out-of-distribution scenarios. The data and code are available at https://***/heyjoonkim/APA.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Query-OPT: Optimizing Inference of Large language Models via Multi-Query Instructions in Meeting Summarization

Query-OPT: Optimizing Inference of Large Language Models via...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Laskar, Md Tahmid Rahman Khasanova, Elena Fu, Xue-Yong Chen, Cheng Bhushan, Shashi T.N. Dialpad Inc VancouverBC Canada

ISBN: (纸本)9798891761667

This work focuses on the task of query-based meeting summarization, in which the summary of a context (meeting transcript) is generated in response to a specific query. When using Large language Models (LLMs) for this task, usually a new call to the LLM inference endpoint/API is triggered for each new query, even if the context stays the same. However, repeated calls to the LLM inference endpoints would significantly increase the costs of using them in production, making LLMs impractical for many real-world use cases. To address this problem, in this paper, we investigate whether combining the queries for the same input context in a single prompt to minimize repeated calls can be successfully used in meeting summarization. In this regard, we conduct extensive experiments by comparing the performance of various popular LLMs: GPT-4, Gemini-1.5, Claude-3, LLaMA-2, Mistral, Phi-3, and Qwen-2 in single-query and multi-query settings. We observe that 100% reliability in generating the response in the expected format is usually limited to certain closed-source LLMs, with most open-source LLMs lagging behind (except a few 7B parameters LLMs like Mistral and Phi-3). We conclude that multi-query prompting could be useful to significantly optimize the inference costs in meeting summarization. © 2024 Association for Computational Linguistics.

关键词： Structured Query language

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 121 122 123 124 125 126 127 128 129 130 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：