The Pile is a newly constructed 825 GiB dataset from 22 diverse sources that enables language models to achieve better performance on academic, professional, and cross-domain tasks than models trained on Common Crawl variants.
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DynamicNER is a dynamic-categorization multilingual NER dataset with 155 entity types paired with CascadeNER, a two-stage lightweight LLM method claiming higher fine-grained accuracy.
The paper introduces the Construct Validity Protocol to validate semantic embeddings for social constructs and proposes Counterfactual Neutralization using LLMs to reduce confounding.
citing papers explorer
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The Pile: An 800GB Dataset of Diverse Text for Language Modeling
The Pile is a newly constructed 825 GiB dataset from 22 diverse sources that enables language models to achieve better performance on academic, professional, and cross-domain tasks than models trained on Common Crawl variants.
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DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity Recognition
DynamicNER is a dynamic-categorization multilingual NER dataset with 155 entity types paired with CascadeNER, a two-stage lightweight LLM method claiming higher fine-grained accuracy.
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The Proxy Presumption: From Semantic Embeddings to Valid Social Measures
The paper introduces the Construct Validity Protocol to validate semantic embeddings for social constructs and proposes Counterfactual Neutralization using LLMs to reduce confounding.