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检索条件"任意字段=Proceedings of the Conference on Empirical Methods in Natural Language Processing"
7707 条 记 录,以下是81-90 订阅
排序:
SLM as Guardian: Pioneering AI Safety with Small language Models
SLM as Guardian: Pioneering AI Safety with Small Language Mo...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Kwon, Ohjoon Jeon, Donghyeon Choi, Nayoung Cho, Gyu-Hwung Jo, Hwiyeol Kim, Changbong Lee, Hyunwoo Kang, Inho Kim, Sun Park, Taiwoo Naver Corporation Korea Republic of Emory University United States NAVER Search US Korea Republic of
Most prior safety research of large language models (LLMs) has focused on enhancing the alignment of LLMs to better suit the safety requirements of their use cases. However, internalizing such safeguard features into ... 详细信息
来源: 评论
Position Engineering: Boosting Large language Models through Positional Information Manipulation
Position Engineering: Boosting Large Language Models through...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: He, Zhiyuan Jiang, Huiqiang Wang, Zilong Yang, Yuqing Qiu, Luna Qiu, Lili Microsoft Research United States
The performance of large language models (LLMs) is significantly influenced by the quality of the prompts provided. In response, researchers have developed enormous prompt engineering strategies aimed at modifying the... 详细信息
来源: 评论
Predicate Debiasing in Vision-language Models Integration for Scene Graph Generation Enhancement
Predicate Debiasing in Vision-Language Models Integration fo...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Wang, Yuxuan Liu, Xiaoyuan Nanyang Technological University Singapore
Scene Graph Generation (SGG) provides basic language representation of visual scenes, requiring models to grasp complex and diverse semantics between objects. This complexity and diversity in SGG leads to underreprese... 详细信息
来源: 评论
PSC: Extending Context Window of Large language Models via Phase Shift Calibration
PSC: Extending Context Window of Large Language Models via P...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Zhu, Wenqiao Xu, Chao Wang, Lulu Wu, Jun HiThink Research Singapore
Rotary Position Embedding (RoPE) is an efficient position encoding approach and is widely utilized in numerous large language models (LLMs). Recently, a lot of methods have been put forward to further expand the conte... 详细信息
来源: 评论
Stable language Model Pre-training by Reducing Embedding Variability
Stable Language Model Pre-training by Reducing Embedding Var...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Chung, Woojin Hong, Jiwoo An, Na Min Thorne, James Yun, Se-Young KAIST AI Korea Republic of
Stable pre-training is essential for achieving better-performing language models. However, tracking pre-training stability by calculating gradient variance at every step is impractical due to the significant computati... 详细信息
来源: 评论
Large language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak Attacks
Large Language Models Are Involuntary Truth-Tellers: Exploit...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Zhou, Yue Zou, Henry Peng Di Eugenio, Barbara Zhang, Yang University of Illinois Chicago United States MIT-IBM Watson AI Lab IBM Research United States
We find that language models have difficulties generating fallacious and deceptive reasoning. When asked to generate deceptive outputs, language models tend to leak honest counterparts but believe them to be false. Ex... 详细信息
来源: 评论
Towards Aligning language Models with Textual Feedback
Towards Aligning Language Models with Textual Feedback
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Lloret, Saüc Abadal Dhuliawala, Shehzaad Murugesan, Keerthiram Sachan, Mrinmaya Department of Computer Science ETH Zürich Switzerland IBM Research United States
We present ALT (ALignment with Textual feedback), an approach that aligns language models with user preferences expressed in *** argue that text offers greater expressiveness, enabling users to provide richer feedback... 详细信息
来源: 评论
Do We Need language-Specific Fact-Checking Models? The Case of Chinese
Do We Need Language-Specific Fact-Checking Models? The Case ...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Zhang, Caiqi Guo, Zhijiang Vlachos, Andreas Language Technology Lab University of Cambridge United Kingdom Department of Computer Science and Technology University of Cambridge United Kingdom
This paper investigates the potential benefits of language-specific fact-checking models, focusing on the case of Chinese using CHEF dataset. To better reflect real-world fact-checking, we first develop a novel Chines... 详细信息
来源: 评论
Exploring Nested Named Entity Recognition with Large language Models: methods, Challenges, and Insights
Exploring Nested Named Entity Recognition with Large Languag...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Kim, Hongjin Kim, Jai-Eun Kim, Harksoo Konkuk University Korea Republic of Saltlux Korea Republic of
Nested Named Entity Recognition (NER) poses a significant challenge in natural language processing (NLP), demanding sophisticated techniques to identify entities within entities. This research investigates the applica... 详细信息
来源: 评论
language Concept Erasure for language-invariant Dense Retrieval
Language Concept Erasure for Language-invariant Dense Retrie...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Huang, Zhiqi Yu, Puxuan Ravfogel, Shauli Allan, James Capital One United States Snowflake Inc. United States Bar-Ilan University Israel University of Massachusetts Amherst United States
Multilingual models aim for language-invariant representations but still prominently encode language identity. This, along with the scarcity of high-quality parallel retrieval data, limits their performance in retriev... 详细信息
来源: 评论