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Augmenting In-Context-Learning in LLMs via Automatic Data Labeling and Refinement

Autor: Shtok, Joseph, Alfassy, Amit, Dahood, Foad Abo, Schwartz, Eliyahu, Doveh, Sivan, Arbelle, Assaf

It has been shown that Large Language Models' (LLMs) performance can be improved for many tasks using Chain of Thought (CoT) or In-Context Learning (ICL), which involve demonstrating the steps needed to solve a task using a few examples. However, whi

Externí odkaz: http://arxiv.org/abs/2410.10348

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KVP10k : A Comprehensive Dataset for Key-Value Pair Extraction in Business Documents

Autor: Naparstek, Oshri, Pony, Roi, Shapira, Inbar, Dahood, Foad Abo, Azulai, Ophir, Yaroker, Yevgeny, Rubinstein, Nadav, Lysak, Maksym, Staar, Peter, Nassar, Ahmed, Livathinos, Nikolaos, Auer, Christoph, Amrani, Elad, Friedman, Idan, Prince, Orit, Burshtein, Yevgeny, Goldfarb, Adi Raz, Barzelay, Udi

In recent years, the challenge of extracting information from business documents has emerged as a critical task, finding applications across numerous domains. This effort has attracted substantial interest from both industry and academy, highlighting

Externí odkaz: http://arxiv.org/abs/2405.00505

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