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Found 6 papers, 2 papers with code
Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning
Document-level relation extraction aims to recognize relations among multiple entity pairs from a whole piece of article.
nanoLM: an Affordable LLM Pre-training Benchmark via Accurate Loss Prediction across Scales
With around 14% of the one-time pre-training cost, we can accurately forecast the loss for models up to 52B.
ADBCMM : Acronym Disambiguation by Building Counterfactuals and Multilingual Mixing
To address the above issue, this paper proposes an new method for acronym disambiguation, named as ADBCMM, which can significantly improve the performance of low-resource languages by building counterfactuals and multilingual mixing.
SimCLAD: A Simple Framework for Contrastive Learning of Acronym Disambiguation
In this paper, we propose a Simple framework for Contrastive Learning of Acronym Disambiguation (SimCLAD) method to better understand the acronym meanings.
PSG: Prompt-based Sequence Generation for Acronym Extraction
In this paper, we propose a Prompt-based Sequence Generation (PSG) method for the acronym extraction task.
