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

14,463 篇 会议
653 篇 期刊文献
101 册 图书
40 篇 学位论文
1 篇 科技报告

馆藏范围

15,257 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

10,943 篇 工学
- 10,283 篇 计算机科学与技术...
- 5,409 篇 软件工程
- 1,461 篇 信息与通信工程
- 953 篇 电气工程
- 879 篇 控制科学与工程
- 446 篇 生物工程
- 221 篇 网络空间安全
- 220 篇 化学工程与技术
- 186 篇 机械工程
- 174 篇 生物医学工程（可授...
- 141 篇 电子科学与技术（可...
- 100 篇 仪器科学与技术
- 100 篇 安全科学与工程
2,473 篇 理学
- 1,150 篇 数学
- 649 篇 物理学
- 518 篇 生物学
- 391 篇 统计学（可授理学、...
- 241 篇 系统科学
- 232 篇 化学
2,417 篇 管理学
- 1,748 篇 图书情报与档案管...
- 758 篇 管理科学与工程(可...
- 240 篇 工商管理
- 104 篇 公共管理
1,761 篇 文学
- 1,709 篇 外国语言文学
- 184 篇 中国语言文学
510 篇 医学
- 299 篇 临床医学
- 282 篇 基础医学(可授医学...
- 112 篇 公共卫生与预防医...
277 篇 法学
- 249 篇 社会学
237 篇 教育学
- 224 篇 教育学
100 篇 农学
97 篇 经济学
9 篇 艺术学
7 篇 哲学
4 篇 军事学

主题

3,534 篇 natural language...
1,768 篇 natural language...
952 篇 computational li...
741 篇 semantics
680 篇 machine learning
609 篇 deep learning
520 篇 natural language...
347 篇 computational mo...
336 篇 training
333 篇 accuracy
331 篇 sentiment analys...
329 篇 large language m...
320 篇 feature extracti...
311 篇 data mining
290 篇 speech processin...
261 篇 speech recogniti...
252 篇 transformers
235 篇 neural networks
217 篇 iterative method...
212 篇 support vector m...

机构

85 篇 carnegie mellon ...
51 篇 university of ch...
45 篇 tsinghua univers...
45 篇 carnegie mellon ...
43 篇 zhejiang univers...
43 篇 national univers...
38 篇 nanyang technolo...
36 篇 university of wa...
35 篇 univ chinese aca...
34 篇 university of sc...
34 篇 carnegie mellon ...
33 篇 stanford univers...
32 篇 gaoling school o...
32 篇 school of artifi...
32 篇 alibaba grp peop...
29 篇 tsinghua univ de...
28 篇 harbin institute...
27 篇 language technol...
27 篇 peking universit...
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
27 篇 liu zhiyuan
27 篇 lapata mirella
26 篇 wen ji-rong
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

14,663 篇 英文
481 篇 其他
105 篇 中文
18 篇 法文
15 篇 土耳其文
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 15258 条记录，以下是601-610 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：

Zero-Resource Hallucination Prevention for Large language Models

Zero-Resource Hallucination Prevention for Large Language Mo...

引用

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

作者： Luo, Junyu Xiao, Cao Ma, Fenglong The Pennsylvania State University United States GE Healthcare United States

ISBN: (纸本)9798891761681

The prevalent use of large language models (LLMs) in various domains has drawn attention to the issue of "hallucination", which refers to instances where LLMs generate factually inaccurate or ungrounded information. Existing techniques usually identify hallucinations post-generation that cannot prevent their occurrence and suffer from inconsistent performance due to the influence of the instruction format and model style. In this paper, we introduce a novel pre-detection self-evaluation technique, referred to as SELF-FAMILIARITY, which focuses on evaluating the model's familiarity with the concepts present in the input instruction and withholding the generation of response in case of unfamiliar concepts under the zero-resource setting, where external ground-truth or background information is not available. We also propose a new dataset Concept-7 focusing on the hallucinations caused by limited inner knowledge. We validate SELF-FAMILIARITY across four different large language models, demonstrating consistently superior performance compared to existing techniques. Our findings propose a significant shift towards preemptive strategies for hallucination mitigation in LLM assistants, promising improvements in reliability, applicability, and interpretability. © 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

A Comprehensive Survey of Hallucination in Large language, Image, Video and Audio Foundation Models

A Comprehensive Survey of Hallucination in Large Language, I...

引用

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

作者： Sahoo, Pranab Meharia, Prabhash Ghosh, Akash Saha, Sriparna Jain, Vinija Chadha, Aman Department of Computer Science and Engineering Indian Institute of Technology Patna India Stanford University United States Amazon GenAI United States

ISBN: (纸本)9798891761681

The rapid advancement of foundation models (FMs) across language, image, audio, and video domains has shown remarkable capabilities in diverse tasks. However, the proliferation of FMs brings forth a critical challenge: the potential to generate hallucinated outputs, particularly in high-stakes applications. The tendency of foundation models to produce hallucinated content arguably represents the biggest hindrance to their widespread adoption in real-world scenarios, especially in domains where reliability and accuracy are paramount. This survey paper presents a comprehensive overview of recent developments that aim to identify and mitigate the problem of hallucination in FMs, spanning text, image, video, and audio modalities. By synthesizing recent advancements in detecting and mitigating hallucination across various modalities, the paper aims to provide valuable insights for researchers, developers, and practitioners. Essentially, it establishes a clear framework encompassing definition, taxonomy, and detection strategies for addressing hallucination in multimodal foundation models, laying the foundation for future research and development in this pivotal area. © 2024 Association for Computational Linguistics.

关键词： FORTH (programming language)

来源：评论

学校读者我要写书评

暂无评论

TAIL: A Toolkit for Automatic and Realistic Long-Context Large language Model Evaluation

TAIL: A Toolkit for Automatic and Realistic Long-Context Lar...

引用

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

作者： Gu, Gefei Zhao, Yilun Ning, Ruoxi Zheng, Yanan Cohan, Arman Yale University United States University of Warterloo Canada Allen Institute for AI India

ISBN: (纸本)9798891761674

As long-context large language models (LLMs) gain increasing attention for their ability to handle extensive inputs, the demand for effective evaluation methods has become critical. Existing evaluation methods, however, fall short: needle-in-a-haystack (NIAH) and its variants are overly simplistic, while creating realistic benchmarks is prohibitively expensive due to extensive human annotation requirements. To bridge this gap, we propose TAIL, an automatic toolkit for creating realistic evaluation benchmarks and assessing the performance of long-context LLMs. With TAIL, users can customize the building of a long-context, document-grounded QA benchmark and obtain visualized performance metrics of evaluated models. TAIL has the advantage of requiring minimal human annotation and generating natural questions based on user-provided long-context documents. We apply TAIL to construct a benchmark encompassing multiple expert domains, such as finance, law, patent, and scientific literature. We then evaluate four state-of-the-art long-context LLMs using this benchmark. Results show that all the evaluated LLMs experience varying degrees of performance degradation as context lengths increase. § https://***/yale-nlp/TAIL. © 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

The Generation Gap: Exploring Age Bias in the Value Systems of Large language Models

The Generation Gap: Exploring Age Bias in the Value Systems ...

引用

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

作者： Liu, Siyang Maturi, Trisha Yi, Bowen Shen, Siqi Mihalcea, Rada The LIT Group Department of Computer Science and Engineering University of Michigan Ann Arbor United States

ISBN: (纸本)9798891761643

We explore the alignment of values in Large language Models (LLMs) with specific age groups, leveraging data from the World Value Survey across thirteen *** a diverse set of prompts tailored to ensure response robustness, we find a general inclination of LLM values towards younger demographics, especially when compared to the US *** a general inclination can be observed, we also found that this inclination toward younger groups can be different across different value ***, we explore the impact of incorporating age identity information in prompts and observe challenges in mitigating value discrepancies with different age *** findings highlight the age bias in LLMs and provide insights for future *** for our analysis are available at https://***/MichiganNLP/Age-Bias-In-LLMs. © 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

DA3: A Distribution-Aware Adversarial Attack against language Models

DA3: A Distribution-Aware Adversarial Attack against Languag...

引用

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

作者： Wang, Yibo Dong, Xiangjue Caverlee, James Yu, Philip S. University of Illinois Chicago United States Texas A&M University United States

ISBN: (纸本)9798891761643

language models can be manipulated by adversarial attacks, which introduce subtle perturbations to input data. While recent attack methods can achieve a relatively high attack success rate (ASR), we've observed that the generated adversarial examples have a different data distribution compared with the original examples. Specifically, these adversarial examples exhibit reduced confidence levels and greater divergence from the training data distribution. Consequently, they are easy to detect using straightforward detection methods, diminishing the efficacy of such attacks. To address this issue, we propose a Distribution-Aware Adversarial Attack (DA3) method. DA3 considers the distribution shifts of adversarial examples to improve attacks' effectiveness under detection methods. We further design a novel evaluation metric, the Non-detectable Attack Success Rate (NASR), which integrates both ASR and detectability for the attack task. We conduct experiments on four widely used datasets to validate the attack effectiveness and transferability of adversarial examples generated by DA3 against both the white-box BERT-BASE and ROBERTA-BASE models and the black-box LLAMA2-7B model. © 2024 Association for Computational Linguistics.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

YesBut: A High-Quality Annotated Multimodal Dataset for evaluating Satire Comprehension capability of Vision-language Models

YesBut: A High-Quality Annotated Multimodal Dataset for eval...

引用

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

作者： Nandy, Abhilash Agarwal, Yash Patwa, Ashish Das, Millon Madhur Bansal, Aman Raj, Ankit Goyal, Pawan Ganguly, Niloy Indian Institute of Technology Kharagpur India University of Massachusetts Amherst United States Haldia Institute of Technology India

ISBN: (纸本)9798891761643

Understanding satire and humor is a challenging task for even current Vision-language models. In this paper, we propose the challenging tasks of Satirical Image Detection (detecting whether an image is satirical), Understanding (generating the reason behind the image being satirical), and Completion (given one half of the image, selecting the other half from 2 given options, such that the complete image is satirical) and release a high-quality dataset YesBut, consisting of 2547 images, 1084 satirical and 1463 non-satirical, containing different artistic styles, to evaluate those tasks. Each satirical image in the dataset depicts a normal scenario, along with a conflicting scenario which is funny or ironic. Despite the success of current Vision-language Models on multimodal tasks such as Visual QA and Image Captioning, our benchmarking experiments show that such models perform poorly on the proposed tasks on the YesBut Dataset in Zero-Shot Settings w.r.t both automated as well as human evaluation. Additionally, we release a dataset of 119 real, satirical photographs for further research. © 2024 Association for Computational Linguistics.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual language Models

Structural Priming Demonstrates Abstract Grammatical Represe...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Michaelov, James A. Arnett, Catherine Chang, Tyler A. Bergen, Benjamin K. Univ Calif San Diego Dept Cognit Sci La Jolla CA 92093 USA Univ Calif San Diego Dept Linguist La Jolla CA 92093 USA

ISBN: (纸本)9798891760608

The Abstract grammatical knowledge-of parts of speech and grammatical patterns-is key to the capacity for linguistic generalization in humans. But how abstract is grammatical knowledge in large language models? In the human literature, compelling evidence for grammatical abstraction comes from structural priming. A sentence that shares the same grammatical structure as a preceding sentence is processed and produced more readily. Because confounds exist when using stimuli in a single language, evidence of abstraction is even more compelling from crosslingual structural priming, where use of a syntactic structure in one language primes an analogous structure in another language. We measure crosslingual structural priming in large language models, comparing model behavior to human experimental results from eight crosslingual experiments covering six languages, and four monolingual structural priming experiments in three non-English languages. We find evidence for abstract monolingual and crosslingual grammatical representations in the models that function similarly to those found in humans. These results demonstrate that grammatical representations in multilingual language models are not only similar across languages, but they can causally influence text produced in different languages.

关键词： Syntactics

来源：评论

学校读者我要写书评

暂无评论

Target-Aware language Modeling via Granular Data Sampling

Target-Aware Language Modeling via Granular Data Sampling

引用

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

作者： Chang, Ernie Lin, Pin-Jie Li, Yang Zhao, Changsheng Kim, Daeil Rabatin, Rastislav Liu, Zechun Shi, Yangyang Chandra, Vikas AI at Meta United States Virginia Tech United States Iowa State University United States

ISBN: (纸本)9798891761643

language model pretraining generally targets a broad range of use cases and incorporates data from diverse sources. However, there are instances where we desire a model that excels in specific areas without markedly compromising performance in other areas. A cost-effective and straightforward approach is sampling with low-dimensional data features, which allows to select large-scale pretraining data for domain-specific use cases. In this work, we revisit importance sampling with n-gram features consisting of multi-granular tokens, which strikes a good balance between sentence compression and representation capabilities. We observed the sampled data to have a high correlation with the target downstream task performance while preserving its effectiveness on other tasks. This leads to the proposed data sampling paradigm where language models can be pretrained more efficiently on selected documents. On eight benchmarks we demonstrate with ∼1% of the data, pretrained models perform on par with the full RefinedWeb data and outperform randomly selected samples for model sizes ranging from 125M to 1.5B. © 2024 Association for Computational Linguistics.

关键词： Modeling languages

来源：评论

学校读者我要写书评

暂无评论

Temporally Consistent Factuality Probing for Large language Models

Temporally Consistent Factuality Probing for Large Language ...

引用

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

作者： Bajpai, Ashutosh Goyal, Aaryan Anwer, Atif Chakraborty, Tanmoy Indian Institute of Technology Delhi India Wipro Research India

ISBN: (纸本)9798891761643

The prolific use of Large language Models (LLMs) as an alternate knowledge base requires them to be factually consistent, necessitating both correctness and consistency traits for paraphrased queries. Recently, significant attempts have been made to benchmark datasets and metrics to evaluate LLMs for these traits. However, structural simplicity (subject-relation-object) and contemporary association in their query formulation limit the broader definition of factuality and consistency. In this study, we introduce TeCFaP, a novel Temporally Consistent Factuality Probe task to expand the consistent factuality probe in the temporal dimension. To this end, we propose TEMP-COFAC, a high-quality dataset of prefix-style English query paraphrases. Subsequently, we extend the definitions of existing metrics to represent consistent factuality across temporal dimension. We experiment with a diverse set of LLMs and find most of them performing poorly on TeCFaP. Next, we propose a novel solution CoTSeLF (Consistent-Time-Sensitive Learning Framework) combining multi-task instruction tuning (MT-IT) with consistent-time-sensitive reinforcement learning (CTSRL) to improve temporally consistent factuality in LLMs. Our experiments demonstrate the efficacy of CoTSeLF over several baselines. © 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

GPT-RE: In-context Learning for Relation Extraction using Large language Models

GPT-RE: In-context Learning for Relation Extraction using La...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Wan, Zhen Cheng, Fei Mao, Zhuoyuan Liu, Qianying Song, Haiyue Li, Jiwei Kurohashi, Sadao Kyoto Univ Kyoto Japan Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798891760608

In spite of the potential for ground-breaking achievements offered by large language models (LLMs) (e.g., GPT-3) via in-context learning (ICL), they still lag significantly behind fully-supervised baselines (e.g., fine-tuned BERT) in relation extraction (RE). This is due to the two major shortcomings of ICL for RE: (1) low relevance regarding entity and relation in existing sentence-level demonstration retrieval approaches for ICL;and (2) the lack of explaining input-label mappings of demonstrations leading to poor ICL effectiveness. In this paper, we propose GPT-RE to successfully address the aforementioned issues by (1) incorporating task-aware representations in demonstration retrieval;and (2) enriching the demonstrations with gold label-induced reasoning logic. We evaluate GPT-RE on four widely-used RE datasets and observe that GPT-RE achieves improvements over not only existing GPT-3 baselines, but also fully-supervised baselines as in Figure 1. Specifically, GPT-RE achieves SOTA performances on the Semeval and SciERC datasets, and competitive performances on the TACRED and ACE05 datasets. Additionally, a critical issue of LLMs revealed by previous work, the strong inclination to wrongly classify NULL examples into other pre-defined labels, is substantially alleviated by our method. We show an empirical analysis.(1)

关键词： Demonstrations

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 << < 57 58 59 60 61 62 63 64 65 66 > >>

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：