Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Stratos, Karl"'
The glyphic writing system of Chinese incorporates information-rich visual features in each character, such as radicals that provide hints about meaning or pronunciation. However, there has been no investigation into whether contemporary Large Langua
Externí odkaz:
http://arxiv.org/abs/2410.09013
Autor:
Gangadhar, Govind, Stratos, Karl
Standard fine-tuning is considered not as effective as specialized methods for model editing due to its comparatively poor performance. However, it is simple, agnostic to the architectural details of the model being edited, and able to leverage advan
Externí odkaz:
http://arxiv.org/abs/2402.11078
Existing works on coreference resolution suggest that task-specific models are necessary to achieve state-of-the-art performance. In this work, we present compelling evidence that such models are not necessary. We finetune a pretrained seq2seq transf
Externí odkaz:
http://arxiv.org/abs/2310.13774
In multitask retrieval, a single retriever is trained to retrieve relevant contexts for multiple tasks. Despite its practical appeal, naive multitask retrieval lags behind task-specific retrieval in which a separate retriever is trained for each task
Externí odkaz:
http://arxiv.org/abs/2307.00342
A conventional approach to entity linking is to first find mentions in a given document and then infer their underlying entities in the knowledge base. A well-known limitation of this approach is that it requires finding mentions without knowing thei
Externí odkaz:
http://arxiv.org/abs/2110.02369
Autor:
Zhang, Wenzheng, Stratos, Karl
The choice of negative examples is important in noise contrastive estimation. Recent works find that hard negatives -- highest-scoring incorrect examples under the model -- are effective in practice, but they are used without a formal justification.
Externí odkaz:
http://arxiv.org/abs/2104.06245
Biomedical entity linking is the task of identifying mentions of biomedical concepts in text documents and mapping them to canonical entities in a target thesaurus. Recent advancements in entity linking using BERT-based models follow a retrieve and r
Externí odkaz:
http://arxiv.org/abs/2103.05028
We propose to tackle data-to-text generation tasks by directly splicing together retrieved segments of text from "neighbor" source-target pairs. Unlike recent work that conditions on retrieved neighbors but generates text token-by-token, left-to-righ
Externí odkaz:
http://arxiv.org/abs/2101.08248
Mikolov et al. (2013a) observed that continuous bag-of-words (CBOW) word embeddings tend to underperform Skip-gram (SG) embeddings, and this finding has been reported in subsequent works. We find that these observations are driven not by fundamental
Externí odkaz:
http://arxiv.org/abs/2012.15332
Dataless text classification is capable of classifying documents into previously unseen labels by assigning a score to any document paired with a label description. While promising, it crucially relies on accurate descriptions of the label set for ea
Externí odkaz:
http://arxiv.org/abs/2012.04194