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20192026
most citedCompositional Exemplars for In-context Learning

24 citations · 75 across the 24 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.CL2023★ 5 cited

G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model

Jiahui Gao, Renjie Pi, Jipeng Zhang +8

Large language models (LLMs) have shown remarkable proficiency in human-level reasoning and generation capabilities, which encourages extensive research on their application in mat…

cs.CL2023★ 3 cited

Language Versatilists vs. Specialists: An Empirical Revisiting on Multilingual Transfer Ability

Jiacheng Ye, Xijia Tao, Lingpeng Kong

Multilingual transfer ability, which reflects how well the models fine-tuned on one source language can be applied to other languages, has been well studied in multilingual pre-tra…

cs.CL2023

Generating Data for Symbolic Language with Large Language Models

Jiacheng Ye, Chengzu Li, Lingpeng Kong +1

While large language models (LLMs) bring not only performance but also complexity, recent work has started to turn LLMs into data generators rather than task inferencers, where ano…

cs.CL2023★ 1 cited

OpenICL: An Open-Source Framework for In-context Learning

Zhenyu Wu, YaoXiang Wang, Jiacheng Ye +4

In recent years, In-context Learning (ICL) has gained increasing attention and emerged as the new paradigm for large language model (LLM) evaluation. Unlike traditional fine-tuning…

cs.CL2023★ 24 cited

Compositional Exemplars for In-context Learning

Jiacheng Ye, Zhiyong Wu, Jiangtao Feng +2

Large pretrained language models (LMs) have shown impressive In-Context Learning (ICL) ability, where the model learns to do an unseen task via a prompt consisting of input-output…