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

14,702 篇 会议
666 篇 期刊文献
101 册 图书
37 篇 学位论文

馆藏范围

15,505 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

11,112 篇 工学
- 10,444 篇 计算机科学与技术...
- 5,444 篇 软件工程
- 1,501 篇 信息与通信工程
- 983 篇 电气工程
- 945 篇 控制科学与工程
- 448 篇 生物工程
- 245 篇 网络空间安全
- 223 篇 化学工程与技术
- 197 篇 机械工程
- 177 篇 生物医学工程（可授...
- 150 篇 电子科学与技术（可...
- 122 篇 安全科学与工程
- 117 篇 交通运输工程
2,492 篇 理学
- 1,158 篇 数学
- 662 篇 物理学
- 518 篇 生物学
- 400 篇 统计学（可授理学、...
- 244 篇 化学
- 240 篇 系统科学
2,429 篇 管理学
- 1,748 篇 图书情报与档案管...
- 779 篇 管理科学与工程(可...
- 236 篇 工商管理
- 119 篇 公共管理
1,832 篇 文学
- 1,776 篇 外国语言文学
- 169 篇 中国语言文学
535 篇 医学
- 310 篇 临床医学
- 285 篇 基础医学(可授医学...
- 125 篇 公共卫生与预防医...
281 篇 法学
- 251 篇 社会学
242 篇 教育学
- 229 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

3,639 篇 natural language...
1,790 篇 natural language...
952 篇 computational li...
754 篇 semantics
700 篇 machine learning
634 篇 deep learning
521 篇 natural language...
378 篇 accuracy
375 篇 computational mo...
356 篇 training
354 篇 large language m...
342 篇 sentiment analys...
329 篇 feature extracti...
311 篇 data mining
290 篇 speech processin...
264 篇 transformers
258 篇 speech recogniti...
238 篇 neural networks
219 篇 support vector m...
217 篇 iterative method...

机构

85 篇 carnegie mellon ...
52 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...
26 篇 univ sci & techn...
26 篇 microsoft resear...

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
33 篇 smith noah a.
28 篇 schütze hinrich
26 篇 wen ji-rong
26 篇 lapata mirella
25 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
21 篇 chua tat-seng
20 篇 dredze mark
18 篇 biemann chris
18 篇 fung pascale

语言

13,014 篇 英文
2,377 篇 其他
131 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 15506 条记录，以下是1381-1390 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：

Video-LLaMA An Instruction-tuned Audio-Visual language Model for Video Understanding

Video-LLaMA An Instruction-tuned Audio-Visual Language Model...

引用

2023 conference on empirical methods in natural language processing: System Demonstrations, EMNLP 2023

作者： Zhang, Hang Li, Xin Bing, Lidong DAMO Academy Alibaba Group China Hupan Lab Hangzhou310023 China

We present Video-LLaMA1 a multi-modal framework that empowers Large language Models (LLMs) with the capability of understanding both visual and auditory content in the video. Video-LLaMA bootstraps cross-modal training from the frozen pre-trained visual & audio encoders and the frozen LLMs. Unlike previous works that complement LLMs to process the visual or audio signals only (Zhu et al., 2023;Liu et al., 2023;Huang et al., 2023a), Video-LLaMA enables video comprehension by tackling two challenges: (1) capturing the temporal changes in visual scenes, (2) integrating audio-visual signals. To counter the first challenge, we propose a Video Q-former to assemble a pre-trained image encoder into our video encoder and introduce a video-to-text generation task to learn video-language correspondence. For the second challenge, we leverage ImageBind (Girdhar et al., 2023), a universal embedding model aligning multiple modalities, as the pre-trained audio encoder and introduce an Audio Q-former on top of ImageBind to learn reasonable auditory query embeddings for the LLM module. To align the output of both visual & audio encoders with LLM’s embedding space, we first train Video-LLaMA on massive video/image-caption pairs and then tune our model with visual-instruction datasets of moderate amount but higher quality. We found Video-LLaMA shows the ability to perceive and comprehend video content and generate meaningful responses grounded in the visual and auditory information presented in the videos. © 2023 Association for Computational Linguistics.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

NEWTON: Are Large language Models Capable of Physical Reasoning?

NEWTON: Are Large Language Models Capable of Physical Reason...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Wang, Yi Ru Du, Jiafei Fox, Dieter Srinivasa, Siddhartha Univ Washington Seattle WA 98195 USA NVIDIA Seattle WA USA

ISBN: (纸本)9798891760615

Large language Models (LLMs), through their contextualized representations, have been empirically proven to encapsulate syntactic, semantic, word sense, and common-sense knowledge. However, there has been limited exploration of their physical reasoning abilities, specifically concerning the crucial attributes for comprehending everyday objects. To address this gap, we introduce NEWTON, a repository and benchmark for evaluating the physics reasoning skills of LLMs. Further, to enable domain-specific adaptation of this benchmark, we present a pipeline to enable researchers to generate a variant of this benchmark that has been customized to the objects and attributes relevant for their application. The NEWTON repository comprises a collection of 2800 object-attribute pairs, providing the foundation for generating infinitescale assessment templates. The NEWTON benchmark consists of 160K QA questions, curated using the NEWTON repository to investigate the physical reasoning capabilities of several mainstream language models across foundational, explicit, and implicit reasoning tasks. Through extensive empirical analysis, our results highlight the capabilities of LLMs for physical reasoning. We find that LLMs like GPT-4 demonstrate strong reasoning capabilities in scenario-based tasks but exhibit less consistency in object-attribute reasoning compared to humans (50% vs. 84%). Furthermore, the NEWTON platform demonstrates its potential for evaluating and enhancing language models, paving the way for their integration into physically grounded settings, such as robotic manipulation. Project site: https: //***

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Efficient Continue Training of Temporal language Model with Structural Information

Efficient Continue Training of Temporal Language Model with ...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Su, Zhaochen Li, Juntao Zhang, Zikang Zhou, Zihan Zhang, Min Soochow Univ Inst Comp Sci & Technol Suzhou Peoples R China Peking Univ Dept Chinese Language & Literature Beijing Peoples R China

ISBN: (纸本)9798891760615

Current language models are mainly trained on snap-shots of data gathered at a particular time, which decreases their capability to generalize over time and model language change. To model the time variable, existing works have explored temporal language models (e.g., TempoBERT) by directly incorporating the timestamp into the training process. While effective to some extent, these methods are limited by the superficial temporal information brought by timestamps, which fails to learn the inherent changes of linguistic components. In this paper, we empirically confirm that the performance of pre-trained language models (PLMs) is closely affiliated with syntactically changed tokens. Based on this observation, we propose a simple yet effective method named Syntax-Guided Temporal language Model (SG-TLM), which could learn the inherent language changes by capturing an intrinsic relationship between the time prefix and the tokens with salient syntactic change. Experiments on two datasets and three tasks demonstrate that our model outperforms existing temporal language models in both memorization and generalization capabilities. Extensive results further confirm the effectiveness of our approach across different model frameworks, including both encoder-only and decoder-only models (e.g., LLaMA). Our code is available at https://***/zhaochen0110/TempoLM.

关键词： Syntactics

来源：评论

学校读者我要写书评

暂无评论

Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large language Models

Unveiling the Flaws: Exploring Imperfections in Synthetic Da...

引用

2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Chen, Jie Zhang, Yupeng Wang, Bingning Zhao, Wayne Xin Wen, Ji-Rong Chen, Weipeng Baichuan Inc. China Gaoling School of Artificial Intelligence Renmin University of China China

ISBN: (纸本)9798891761681

Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs). Studies have shown that synthetic data can effectively improve the performance of LLMs on downstream benchmarks. However, despite its potential benefits, our analysis suggests that there may be inherent flaws in synthetic data. The uniform format of synthetic data can lead to pattern overfitting and cause significant shifts in the output distribution, thereby reducing the model's instruction-following capabilities. Our work delves into these specific flaws associated with question-answer (Q-A) pairs, a prevalent type of synthetic data, and presents a method based on unlearning techniques to mitigate these flaws. The empirical results demonstrate the effectiveness of our approach, which can reverse the instruction-following issues caused by pattern overfitting without compromising performance on benchmarks at relatively low cost. Our work has yielded key insights into the effective use of synthetic data, aiming to promote more robust and efficient LLM training. © 2024 Association for Computational Linguistics.

关键词： Data assimilation

来源：评论

学校读者我要写书评

暂无评论

Leveraging Large language Models Knowledge Enhancement Dual-Stage Fine-Tuning Framework for Recommendation 13th

Leveraging Large Language Models Knowledge Enhancement Dual-...

引用

13th International conference on natural language processing and Chinese Computing

作者： Zeng, Biqing Shi, Hao Li, Yangyu Li, Ruizhe Deng, Huimin South China Normal Univ Sch Software Foshan Peoples R China South China Normal Univ Aberdeen Inst Data Sci & Artificial Intelligence Foshan Peoples R China Univ Aberdeen Dept Comp Sci Aberdeen Scotland Guangdong AIB Polytech Sch Comp Sci Guangzhou Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Large language models(LLMs) have exhibited notable general-purpose task-solving abilities in language understanding and generation, including processing recommendation tasks. The majority of existing research relies on training-free recommendation models that treat LLMs as reasoning engines and directly given the recommended task response. This approach heavily relies on pre-trained knowledge and may lead to excessive costs. As such, we propose a two-stage fine-tuning framework leveraging LLaMA2 and GPT-4 Knowledge Enhancement for recommendation. In particular, we use GPT-4 Instruction-Following data to tune the LLM in first-stage instruction tuning process, achieving lower training costs and better inference performance. In the second stage, through a elaborately designed prompt template, we fine-tune LLM from the first stage in a few-shot setting by interactive sequences based on user ratings. To validate the effectiveness of our framework, we compare against state-of-the-art baseline methods on benchmark datasets. The results demonstrate that our framework has promising recommendation capabilities. Our experiments are executed on a single RTX4090 with LLaMA2-7B.

关键词： Large language models Knowledge Enhancement Recommendation Instruction Tuning

来源：评论

学校读者我要写书评

暂无评论

Generative Spoken language Model based on continuous word-sized audio tokens

Generative Spoken Language Model based on continuous word-si...

引用

2023 conference on empirical methods in natural language processing, EMNLP 2023

作者： Algayres, Robin Adi, Yossi Nguyen, Tu Anh Copet, Jade Synnaeve, Gabriel Sagot, Benoit Dupoux, Emmanuel ENS INRIA INSERM UPEC PSL Research University France The Hebrew University of Jerusalem Israel Meta AI United States

ISBN: (纸本)9798891760608

In NLP, text language models based on words or subwords are known to outperform their character-based counterparts. Yet, in the speech community, the standard input of spoken LMs are 20ms or 40ms-long discrete units (shorter than a phoneme). Taking inspiration from word-based LM, we introduce a Generative Spoken language Model (GSLM) based on word-size continuous-valued audio embeddings that can generate diverse and expressive language output. This is obtained by replacing lookup table for lexical types with a Lexical Embedding function, the cross entropy loss by a contrastive loss, and multinomial sampling by k-NN sampling. The resulting model is the first generative language model based on word-size continuous embeddings. Its performance is on par with discrete unit GSLMs regarding generation quality as measured by automatic metrics and subjective human judgements. Moreover, it is five times more memory efficient thanks to its large 200ms units. In addition, the embeddings before and after the Lexical Embedder are phonetically and semantically interpretable.. ©2023 Association for Computational Linguistics.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Leveraging Grammar Induction for language Understanding and Generation

Leveraging Grammar Induction for Language Understanding and ...

引用

2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Kai, Jushi Hou, Shengyuan Huang, Yusheng Lin, Zhouhan Shanghai Jiao Tong University China

ISBN: (纸本)9798891761681

Grammar induction has made significant progress in recent years. However, it is not clear how the application of induced grammar could enhance practical performance in downstream tasks. In this work, we introduce an unsupervised grammar induction method for language understanding and generation. We construct a grammar parser to induce constituency structures and dependency relations, which is simultaneously trained on downstream tasks without additional syntax annotations. The induced grammar features are subsequently incorporated into Transformer as a syntactic mask to guide self-attention. We evaluate and apply our method to multiple machine translation tasks and natural language understanding tasks. Our method demonstrates superior performance compared to the original Transformer and other models enhanced with external parsers. Experimental results indicate that our method is effective in both from-scratch and pre-trained scenarios. Additionally, our research highlights the contribution of explicitly modeling the grammatical structure of texts to neural network models. © 2024 Association for Computational Linguistics.

关键词： Syntactics

来源：评论

学校读者我要写书评

暂无评论

UReader: Universal OCR-free Visually-situated language Understanding with Multimodal Large language Model

UReader: Universal OCR-free Visually-situated Language Under...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Ye, Jiabo Hu, Anwen Xu, Haiyang Ye, Qinghao Yan, Ming Xu, Guohai Li, Chenliang Tian, Junfeng Qian, Qi Zhang, Ji Jin, Qin He, Liang Lin, Xin Huang, Fei East China Normal Univ Shanghai Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China Renmin Univ China Beijing Peoples R China

ISBN: (纸本)9798891760615

Text is ubiquitous in our visual world, conveying crucial information, such as in documents, websites, and everyday photographs. In this work, we propose UReader, a first exploration of universal OCR-free visually-situated language understanding based on the Multimodal Large language Model (MLLM). By leveraging the shallow text recognition ability of the MLLM, we only finetuned 1.2% parameters and the training cost is much lower than previous work following domain-specific pretraining and finetuning paradigms. Concretely, UReader is jointly finetuned on a wide range of Visually-situated language Understanding tasks via a unified instruction format. To enhance the visual text and semantic understanding, we further apply two auxiliary tasks with the same format, namely text reading and key points generation tasks. We design a shape-adaptive cropping module before the encoder-decoder architecture of MLLM to leverage the frozen low-resolution vision encoder for processing high-resolution images. Without downstream finetuning, our single model achieves state-of-the-art ocr-free performance in 8 out of 10 visually-situated language understanding tasks, across 5 domains: documents, tables, charts, natural images, and webpage screenshots. Codes and instruction-tuning datasets are released at https://***/LukeForeverYoung/UReader.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

MalayMMLU: A Multitask Benchmark for the Low-Resource Malay language

MalayMMLU: A Multitask Benchmark for the Low-Resource Malay ...

引用

2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Poh, Soon Chang Yang, Sze Jue Tan, Jeraelyn Ming Li Chieng, Lawrence Leroy Tze Yao Tan, Jia Xuan Yu, Zhenyu Foong, Chee Mun Chan, Chee Seng Universiti Malaya Malaysia YTL AI Labs Malaysia

ISBN: (纸本)9798891761681

Large language Models (LLMs) and Large Vision language Models (LVLMs) exhibit advanced proficiency in language reasoning and comprehension across a wide array of languages. While their performance is notably robust in well-resourced languages, their capabilities in low-resource languages, such as Bahasa Melayu (hereinafter referred to as Malay), remain less explored due to a scarcity of dedicated studies and benchmarks. To enhance our understanding of LLMs/LVLMs performance in Malay, we introduce the first multi-task language understanding benchmark specifically for this language, named MalayMMLU. This benchmark comprises 24,213 questions spanning both primary (Year 1-6) and secondary (Form 1-5) education levels in Malaysia, encompassing 5 broad topics that further divided into 22 subjects. We conducted an empirical evaluation of 44 LLMs/LVLMs, assessing their proficiency in both Malay and the nuanced contexts of Malaysian culture using this benchmark. The benchmark and evaluation code are available at https://***/UMxYTL-AI-Labs/MalayMMLU. © 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

An empirical Study on Multiple Knowledge from ChatGPT for Emotion Recognition in Conversations

An Empirical Study on Multiple Knowledge from ChatGPT for Em...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Tu, Geng Liang, Bin Qin, Bing Wong, Kam-Fai Xu, Ruifeng Harbin Inst Technol Shenzhen Peoples R China Guangdong Prov Key Lab Novel Secur Intelligence T Guangzhou Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Harbin Inst Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798891760615

Multiple knowledge (e.g., co-reference, topics, emotional causes, etc) has been demonstrated effective for emotion detection. However, exploring this knowledge in Emotion Recognition in Conversations (ERC) is currently a blank slate due to the lack of annotated data and the high cost involved in obtaining such knowledge. Fortunately, the emergence of Large language Models (LLMs) holds promise in filling this void. Therefore, we propose a Multiple Knowledge Fusion Model (MKFM) to effectively integrate such knowledge generated by LLMs for ERC and empirically study its impact on the model. Experimental results on three public datasets have demonstrated the effectiveness of multiple knowledge for ERC. Furthermore, we conduct a detailed analysis of the contribution and complementarity of this knowledge(1).

关键词： Emotion Recognition

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 << < 135 136 137 138 139 140 141 142 143 144 > >>

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：