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571-580 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：

Pre-training language Models for Comparative Reasoning

Pre-training Language Models for Comparative Reasoning

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Yu, Mengxia Zhang, Zhihan Yu, Wenhao Jiang, Meng Univ Notre Dame Notre Dame IN 46556 USA Tencent AI Seattle Lab Seattle WA USA

ISBN: (纸本)9798891760608

Comparative reasoning is a process of comparing objects, concepts, or entities to draw conclusions, which constitutes a fundamental cognitive ability. In this paper, we propose a novel framework to pre-train language models for enhancing their abilities of comparative reasoning over texts. While there have been approaches for NLP tasks that require comparative reasoning, they suffer from costly manual data labeling and limited generalizability to different tasks. Our approach introduces a novel method of collecting scalable data for text-based entity comparison, which leverages both structured and unstructured data. Moreover, we present a framework of pre-training language models via three novel objectives on comparative reasoning. Evaluation on downstream tasks including comparative question answering, question generation, and summarization shows that our pre-training framework significantly improves the comparative reasoning abilities of language models, especially under low-resource conditions. This work also releases the first integrated benchmark for comparative reasoning.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment Classification

An Effective Deployment of Diffusion LM for Data Augmentatio...

引用

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

作者： Chen, Zhuowei Wang, Lianxi Wu, Yuben Liao, Xinfeng Tian, Yujia Zhong, Junyang Guangdong University of Foreign Studies Guangzhou China Guangzhou Key Laboratory of Multilingual Intelligent Processing Guangzhou China

ISBN: (纸本)9798891761643

Sentiment classification (SC) often suffers from low-resource challenges such as domain-specific contexts, imbalanced label distributions, and few-shot scenarios. The potential of the diffusion language model (LM) for textual data augmentation (DA) remains unexplored, moreover, textual DA methods struggle to balance the diversity and consistency of new samples. Most DA methods either perform logical modifications or rephrase less important tokens in the original sequence with the language model. In the context of SC, strong emotional tokens could act critically on the sentiment of the whole sequence. Therefore, contrary to rephrasing less important context, we propose DiffusionCLS to leverage a diffusion LM to capture in-domain knowledge and generate pseudo samples by reconstructing strong label-related tokens. This approach ensures a balance between consistency and diversity, avoiding the introduction of noise and augmenting crucial features of datasets. DiffusionCLS also comprises a Noise-Resistant Training objective to help the model generalize. Experiments demonstrate the effectiveness of our method in various low-resource scenarios including domain-specific and domain-general problems. Ablation studies confirm the effectiveness of our framework's modules, and visualization studies highlight optimal deployment conditions, reinforcing our conclusions. © 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

ITINERA: Integrating Spatial Optimization with Large language Models for Open-domain Urban Itinerary Planning

ITINERA: Integrating Spatial Optimization with Large Languag...

引用

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

作者： Tang, Yihong Wang, Zhaokai Qu, Ao Yan, Yihao Wu, Zhaofeng Zhuang, Dingyi Kai, Jushi Hou, Kebing Guo, Xiaotong Zhao, Jinhua Zhao, Zhan Ma, Wei Tutu AI University of Hong Kong Hong Kong Shanghai Jiao Tong University China Massachusetts Institute of Technology United States The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798891761667

Citywalk, a recently popular form of urban travel, requires genuine personalization and understanding of fine-grained requests compared to traditional itinerary planning. In this paper, we introduce the novel task of Open-domain Urban Itinerary Planning (OUIP), which generates personalized urban itineraries from user requests in natural language. We then present ITINERA, an OUIP system that integrates spatial optimization with large language models to provide customized urban itineraries based on user needs. This involves decomposing user requests, selecting candidate points of interest (POIs), ordering the POIs based on cluster-aware spatial optimization, and generating the itinerary. Experiments on real-world datasets and the performance of the deployed system demonstrate our system’s capacity to deliver personalized and spatially coherent itineraries compared to current solutions. Source codes of ITINERA are available at https://***/YihongT/ITINERA. © 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

Conditional language Policy: A General Framework for Steerable Multi-Objective Finetuning

Conditional Language Policy: A General Framework for Steerab...

引用

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

作者： Wang, Kaiwen Kidambi, Rahul Sullivan, Ryan Agarwal, Alekh Dann, Christoph Michi, Andrea Gelmi, Marco Li, Yunxuan Gupta, Raghav Dubey, Avinava Ramé, Alexandre Ferret, Johan Cideron, Geoffrey Hou, Le Yu, Hongkun Ahmed, Amr Mehta, Aranyak Hussenot, Léonard Bachem, Olivier Leurent, Edouard Google United States

ISBN: (纸本)9798891761681

Reward-based finetuning is crucial for aligning language policies with intended behaviors (e.g., creativity and safety). A key challenge is to develop steerable language models that trade-off multiple (conflicting) objectives in a flexible and efficient manner. This paper presents Conditional language Policy (CLP), a general framework for finetuning language models on multiple objectives. Building on techniques from multi-task training and parameter-efficient finetuning, CLP learn steerable models that effectively trade-off conflicting objectives at inference time. Notably, this does not require training or maintaining multiple models to achieve different trade-offs between the objectives. Through extensive experiments and ablations on two summarization datasets, we show that CLP learns steerable language models that outperform and Pareto-dominate the existing approaches for multi-objective finetuning. © 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

Enhancing Advanced Visual Reasoning Ability of Large language Models

Enhancing Advanced Visual Reasoning Ability of Large Languag...

引用

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

作者： Li, Zhiyuan Liu, Dongnan Zhang, Chaoyi Wang, Heng Xue, Tengfei Cai, Weidong School of Computer Science The University of Sydney Australia

ISBN: (纸本)9798891761643

Recent advancements in Vision-language (VL) research have sparked new benchmarks for complex visual reasoning, challenging models' advanced reasoning ability. Traditional Vision-language Models (VLMs) perform well in visual perception tasks while struggling with complex reasoning scenarios. Conversely, Large language Models (LLMs) demonstrate robust text reasoning capabilities;however, they lack visual acuity. To bridge this gap, we propose Complex Visual Reasoning Large language Models (CVR-LLM), capitalizing on VLMs' visual perception proficiency and LLMs' extensive reasoning capability. Unlike recent multimodal large language models (MLLMs) that require a projection layer, our approach transforms images into detailed, context-aware descriptions using an iterative self-refinement loop and leverages LLMs' text knowledge for accurate predictions without extra training. We also introduce a novel multimodal in-context learning (ICL) methodology to enhance LLMs' contextual understanding and reasoning. Additionally, we introduce Chain-of-Comparison (CoC), a step-by-step comparison technique enabling contrasting various aspects of predictions. Our CVR-LLM presents the first comprehensive study across a wide array of complex visual reasoning tasks and achieves SOTA performance among all. © 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

Pushdown Layers: Encoding Recursive Structure in Transformer language Models

Pushdown Layers: Encoding Recursive Structure in Transformer...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Murty, Shikhar Sharma, Pratyusha Andreas, Jacob Manning, Christopher D. Stanford Univ Dept Comp Sci Stanford CA 94305 USA MIT CSAIL Cambridge MA USA

ISBN: (纸本)9798891760608

Recursion is a prominent feature of human language, and fundamentally challenging for self-attention due to the lack of an explicit recursive-state tracking mechanism. Consequently, Transformer language models poorly capture long-tail recursive structure and exhibit sample-inefficient syntactic generalization. This work introduces Pushdown Layers, a new self-attention layer that models recursive state via a stack tape that tracks estimated depths of every token in an incremental parse of the observed prefix. Transformer LMs with Pushdown Layers are syntactic language models that autoregressively and synchronously update this stack tape as they predict new tokens, in turn using the stack tape to softly modulate attention over tokens-for instance, learning to "skip" over closed constituents. When trained on a corpus of strings annotated with silver constituency parses, Transformers equipped with Pushdown Layers achieve dramatically better and 3-5x more sample-efficient syntactic generalization, while maintaining similar perplexities. Pushdown Layers are a drop-in replacement for standard self-attention. We illustrate this by finetuning GPT2-medium with Pushdown Layers on an automatically parsed WikiText-103, leading to improvements on several GLUE text classification tasks.

关键词： Syntactics

来源：评论

学校读者我要写书评

暂无评论

Make Some Noise: Unlocking language Model Parallel Inference Capability through Noisy Training

Make Some Noise: Unlocking Language Model Parallel Inference...

引用

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

作者： Wang, Yixuan Luo, Xianzhen Wei, Fuxuan Liu, Yijun Zhu, Qingfu Zhang, Xuanyu Yang, Qing Xu, Dongliang Che, Wanxiang Harbin Institute of Technology Harbin China Science Technology Co. Ltd. China

ISBN: (纸本)9798891761643

Existing speculative decoding methods typically require additional model structure and training processes to assist the model for draft token generation. This makes the migration of acceleration methods to the new model more costly and more demanding on device memory. To address this problem, we propose the Make Some Noise (MSN) training framework as a replacement for the supervised fine-tuning stage of the large language model. The training method simply introduces some noise at the input for the model to learn the denoising task. It significantly enhances the parallel decoding capability of the model without affecting the original task capability. In addition, we propose a tree-based retrieval-augmented Jacobi (TR-Jacobi) decoding strategy to further improve the inference speed of MSN models. Experiments in both the general and code domains have shown that MSN can improve inference speed by 2.3-2.7x times without compromising model performance. The MSN model also achieves comparable acceleration ratios to the SOTA model with additional model structure on Spec-Bench. © 2024 Association for Computational Linguistics.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP

GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic ...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Khondaker, Md Tawkat Islam Waheed, Abdul Nagoudi, El Moatez Billah Abdul-Mageed, Muhammad Univ British Columbia Deep Learning & Nat Language Proc Grp Vancouver BC Canada MBZUAI Dept Nat Language Proc Abu Dhabi U Arab Emirates MBZUAI Dept Machine Learning Abu Dhabi U Arab Emirates

ISBN: (纸本)9798891760608

ChatGPT's emergence heralds a transformative phase in NLP, particularly demonstrated through its excellent performance on many English benchmarks. However, the model's efficacy across diverse linguistic contexts remains largely uncharted territory. This work aims to bridge this knowledge gap, with a primary focus on assessing ChatGPT's capabilities on Arabic languages and dialectal varieties. Our comprehensive study conducts a large-scale automated and human evaluation of ChatGPT, encompassing 44 distinct language understanding and generation tasks on over 60 different datasets. To our knowledge, this marks the first extensive performance analysis of ChatGPT's deployment in Arabic NLP. Our findings indicate that, despite its remarkable performance in English, ChatGPT is consistently surpassed by smaller models that have undergone finetuning on Arabic. We further undertake a meticulous comparison of ChatGPT and GPT-4's Modern Standard Arabic (MSA) and Dialectal Arabic (DA), unveiling the relative shortcomings of both models in handling Arabic dialects compared to MSA. Although we further explore and confirm the utility of employing GPT-4 as a potential alternative for human evaluation, our work adds to a growing body of research underscoring the limitations of ChatGPT.

关键词： natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Efficiently Computing Susceptibility to Context in language Models

Efficiently Computing Susceptibility to Context in Language ...

引用

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

作者： Liu, Tianyu Du, Kevin Sachan, Mrinmaya Cotterell, Ryan ETH zurich Switzerland

ISBN: (纸本)9798891761681

One strength of modern language models is their ability to incorporate information from a user-input context when answering queries. However, they are not equally sensitive to the subtle changes to that context. To quantify this, Du et al. (2024) gives an information-theoretic metric to measure such sensitivity. Their metric, susceptibility, is defined as the degree to which contexts can influence a model's response to a query at a distributional level. However, exactly computing susceptibility is difficult and, thus, Du et al. (2024) fall back on a Monte Carlo approximation. Due to the large number of samples required, the Monte Carlo approximation is inefficient in practice. As a faster alternative, we propose Fisher susceptibility, an efficient method to estimate the susceptibility based on Fisher information. empirically, we validate that Fisher susceptibility is comparable to Monte Carlo estimated susceptibility across a diverse set of query domains despite its being 70× faster. Exploiting the improved efficiency, we apply Fisher susceptibility to analyze factors affecting the susceptibility of language models. We observe that larger models are as susceptible as smaller ones. © 2024 Association for Computational Linguistics.

关键词：

来源：评论

学校读者我要写书评

暂无评论

language Models in Dialogue: Conversational Maxims for Human-AI Interactions

Language Models in Dialogue: Conversational Maxims for Human...

引用

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

作者： Miehling, Erik Nagireddy, Manish Sattigeri, Prasanna Daly, Elizabeth M. Piorkowski, David Richards, John T. IBM Research United States

ISBN: (纸本)9798891761681

Modern language models, while sophisticated, exhibit some inherent shortcomings, particularly in conversational settings. We claim that many of the observed shortcomings can be attributed to violation of one or more conversational principles. By drawing upon extensive research from both the social science and AI communities, we propose a set of maxims - quantity, quality, relevance, manner, benevolence, and transparency - for describing effective human-AI conversation. We first justify the applicability of the first four maxims (from Grice) in the context of human-AI interactions. We then argue that two new maxims, benevolence (concerning the generation of, and engagement with, harmful content) and transparency (concerning recognition of one's knowledge boundaries, operational constraints, and intents), are necessary for addressing behavior unique to modern human-AI interactions. We evaluate the degree to which various language models are able to understand these maxims and find that models possess an internal prioritization of principles that can significantly impact their ability to interpret the maxims accurately. © 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

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 << < 54 55 56 57 58 59 60 61 62 63 > >>

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：