Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Schmid, Helmut"'
Large language models (LLMs) possess extensive parametric knowledge, but this knowledge is difficult to update with new information because retraining is very expensive and infeasible for closed-source models. Knowledge editing (KE) has emerged as a
Externí odkaz:
http://arxiv.org/abs/2406.17764
Autor:
Nie, Ercong, Yuan, Shuzhou, Ma, Bolei, Schmid, Helmut, Färber, Michael, Kreuter, Frauke, Schütze, Hinrich
Despite the predominance of English in their training data, English-centric Large Language Models (LLMs) like GPT-3 and LLaMA display a remarkable ability to perform multilingual tasks, raising questions about the depth and nature of their cross-ling
Externí odkaz:
http://arxiv.org/abs/2402.18397
Large Language Models (LLMs) exhibit strong In-Context Learning (ICL) capabilities when prompts with demonstrations are used. However, fine-tuning still remains crucial to further enhance their adaptability. Prompt-based fine-tuning proves to be an e
Externí odkaz:
http://arxiv.org/abs/2402.11709
Autor:
Ma, Bolei, Nie, Ercong, Yuan, Shuzhou, Schmid, Helmut, Färber, Michael, Kreuter, Frauke, Schütze, Hinrich
Prompt-based methods have been successfully applied to multilingual pretrained language models for zero-shot cross-lingual understanding. However, most previous studies primarily focused on sentence-level classification tasks, and only a few consider
Externí odkaz:
http://arxiv.org/abs/2401.16589
Pretrained multilingual encoder models can directly perform zero-shot multilingual tasks or linguistic probing by reformulating the input examples into cloze-style prompts. This is accomplished by predicting the probabilities of the label words at th
Externí odkaz:
http://arxiv.org/abs/2310.05069
Constituency parsing plays a fundamental role in advancing natural language processing (NLP) tasks. However, training an automatic syntactic analysis system for ancient languages solely relying on annotated parse data is a formidable task due to the
Externí odkaz:
http://arxiv.org/abs/2308.04645
Multilingual pretrained language models (MPLMs) have demonstrated substantial performance improvements in zero-shot cross-lingual transfer across various natural language understanding tasks by finetuning MPLMs on task-specific labelled data of a sou
Externí odkaz:
http://arxiv.org/abs/2307.07880
Autor:
Imani, Ayyoob, Lin, Peiqin, Kargaran, Amir Hossein, Severini, Silvia, Sabet, Masoud Jalili, Kassner, Nora, Ma, Chunlan, Schmid, Helmut, Martins, André F. T., Yvon, François, Schütze, Hinrich
The NLP community has mainly focused on scaling Large Language Models (LLMs) vertically, i.e., making them better for about 100 languages. We instead scale LLMs horizontally: we create, through continued pretraining, Glot500-m, an LLM that covers 511
Externí odkaz:
http://arxiv.org/abs/2305.12182
Multilingual Pretrained Language Models (MPLMs) have shown their strong multilinguality in recent empirical cross-lingual transfer studies. In this paper, we propose the Prompts Augmented by Retrieval Crosslingually (PARC) pipeline to improve the zer
Externí odkaz:
http://arxiv.org/abs/2212.09651
We present a literature and empirical survey that critically assesses the state of the art in character-level modeling for machine translation (MT). Despite evidence in the literature that character-level systems are comparable with subword systems,
Externí odkaz:
http://arxiv.org/abs/2110.08191