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pro vyhledávání: '"Cohen, Amir DN"'
In few-shot relation classification (FSRC), models must generalize to novel relations with only a few labeled examples. While much of the recent progress in NLP has focused on scaling data size, we argue that diversity in relation types is more cruci
Externí odkaz:
http://arxiv.org/abs/2412.05434
Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities
Training large language models (LLMs) in low-resource languages such as Hebrew poses unique challenges. In this paper, we introduce DictaLM2.0 and DictaLM2.0-Instruct, two LLMs derived from the Mistral model, trained on a substantial corpus of approx
Externí odkaz:
http://arxiv.org/abs/2407.07080
Recognizing entities in texts is a central need in many information-seeking scenarios, and indeed, Named Entity Recognition (NER) is arguably one of the most successful examples of a widely adopted NLP task and corresponding NLP technology. Recent ad
Externí odkaz:
http://arxiv.org/abs/2310.14282
Identifying texts with a given semantics is central for many information seeking scenarios. Similarity search over vector embeddings appear to be central to this ability, yet the similarity reflected in current text embeddings is corpus-driven, and i
Externí odkaz:
http://arxiv.org/abs/2305.12517
The current supervised relation classification (RC) task uses a single embedding to represent the relation between a pair of entities. We argue that a better approach is to treat the RC task as span-prediction (SP) problem, similar to Question answer
Externí odkaz:
http://arxiv.org/abs/2010.04829
In this work, we aim to connect two research areas: instruction models and retrieval-based models. While instruction-tuned Large Language Models (LLMs) excel at extracting information from text, they are not suitable for semantic retrieval. Similarit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a96eb2670689ce6c24186f17d2a49da6
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