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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91-100 订阅
排序:
FROG: Evaluating Fuzzy Reasoning of Generalized Quantifiers in Large language Models
FROG: Evaluating Fuzzy Reasoning of Generalized Quantifiers ...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Li, Yiyuan Sun, Shichao Liu, Pengfei Shanghai Jiao Tong University China UNC-Chapel Hill United States The Hong Kong Polytechnic University Hong Kong
Fuzzy reasoning is vital due to the frequent use of imprecise information in daily contexts. However, the ability of current large language models (LLMs) to handle such reasoning remains largely uncharted. In this pap... 详细信息
来源: 评论
Exploring the Compositional Deficiency of Large language Models in Mathematical Reasoning Through Trap Problems
Exploring the Compositional Deficiency of Large Language Mod...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Zhao, Jun Tong, Jingqi Mou, Yurong Zhang, Ming Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China
Human cognition exhibits systematic compositionality, the algebraic ability to generate infinite novel combinations from finite learned components, which is the key to understanding and reasoning about complex logic. ... 详细信息
来源: 评论
LIONs: An empirically Optimized Approach to Align language Models
LIONs: An Empirically Optimized Approach to Align Language M...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Yu, Xiao Wu, Qingyang Li, Yu Yu, Zhou Columbia University United States
Alignment is a crucial step to enhance the instruction-following and conversational abilities of language models. Despite many recent work proposing new algorithms, datasets, and training pipelines, there is a lack of... 详细信息
来源: 评论
LoRA-Guard: Parameter-Efficient Guardrail Adaptation for Content Moderation of Large language Models
LoRA-Guard: Parameter-Efficient Guardrail Adaptation for Con...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Elesedy, Hayder Esperança, Pedro M. Oprea, Silviu Vlad Ozay, Mete United Kingdom
Guardrails have emerged as comprehensive method of content moderation for large language models (LLMs), complementing safety alignment from fine-tuning. However, existing model-based guardrails are too memory intensiv... 详细信息
来源: 评论
AnyMAL: An Efficient and Scalable Any-Modality Augmented language Model
AnyMAL: An Efficient and Scalable Any-Modality Augmented Lan...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Moon, Seungwhan Madotto, Andrea Lin, Zhaojiang Nagarajan, Tushar Smith, Matt Jain, Shashank Yeh, Chun-Fu Murugesan, Prakash Heidari, Peyman Liu, Yue Srinet, Kavya Damavandi, Babak Kumar, Anuj FAIR Meta & Meta Reality Labs United States
We present Any-Modality Augmented language Model (AnyMAL), a unified model that reasons over diverse input modality signals (i.e. text, image, video, audio, IMU motion sensor), and generates textual responses. AnyMAL ... 详细信息
来源: 评论
Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large language Models
Let Me Speak Freely? A Study on the Impact of Format Restric...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Tam, Zhi Rui Wu, Cheng-Kuang Tsai, Yi-Lin Lin, Chieh-Yen Lee, Hung-Yi Chen, Yun-Nung Appier AI Research National Taiwan University Taiwan
Structured generation, the process of producing content in standardized formats like JSON and XML, is widely utilized in real-world applications to extract key output information from large language models (LLMs). Thi... 详细信息
来源: 评论
Story Embeddings - Narrative-Focused Representations of Fictional Stories
Story Embeddings - Narrative-Focused Representations of Fict...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Hatzel, Hans Ole Biemann, Chris Universität Hamburg Language Technology Group Germany
We present a novel approach to modeling fictional narratives. The proposed model creates embeddings that represent a story such that similar narratives, that is, reformulations of the same story, will result in simila... 详细信息
来源: 评论
Outcome-Constrained Large language Models for Countering Hate Speech
Outcome-Constrained Large Language Models for Countering Hat...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Hong, Lingzi Luo, Pengcheng Blanco, Eduardo Song, Xiaoying University of North Texas United States Peking University China University of Arizona United States
Automatic counterspeech generation methods have been developed to assist efforts in combating hate speech. Existing research focuses on generating counterspeech with linguistic attributes such as being polite, informa...
来源: 评论
language models and brains align due to more than next-word prediction and word-level information
Language models and brains align due to more than next-word ...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Merlin, Gabriele Toneva, Mariya MPI for Software Systems Saarbrücken Germany
Pretrained language models have been shown to significantly predict brain recordings of people comprehending *** work suggests that the prediction of the next word is a key mechanism that contributes to this *** is no... 详细信息
来源: 评论
Consolidating Ranking and Relevance Predictions of Large language Models through Post-processing
Consolidating Ranking and Relevance Predictions of Large Lan...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Yan, Le Qin, Zhen Zhuang, Honglei Jagerman, Rolf Wang, Xuanhui Bendersky, Michael Oosterhuis, Harrie Google Research Mountain ViewCA94043 United States
The powerful generative abilities of large language models (LLMs) show potential in generating relevance labels for search applications. Previous work has found that directly asking about relevancy, such as "How ... 详细信息
来源: 评论