Challenge: GPT-3 has been used to train large-scale language models on hundreds of billion scale data.
Approach: They propose a Korean variant of GPT-3 that uses Korean tokens to train in-context models.
Outcome: The proposed method shows state-of-the-art zero-shot and few-shot learning on downstream tasks in Korean.

Similar Papers

On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model (2022.naacl-main)

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Challenge: Recent studies on large-scale in-context language models have reported successful in-const zero- and few-shot learning ability.
Approach: They investigate the effects of the pretraining corpus on in-context learning in a Korean-centric model.
Outcome: The study shows that pretraining corpus size does not determine in-context learning ability . the findings suggest that in-constext learning is not always competitive .
On the Multilingual Capabilities of Very Large-Scale English Language Models (2022.lrec-1)

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Challenge: Generative Pre-trained Transformers (GPTs) have been scaled to unprecedented sizes in the history of machine learning.
Approach: They investigate the potential and limits of Generative Pre-trained Transformers in three tasks . they find it can be almost as useful for many languages as it is for English .
Outcome: The proposed model can perform tasks in five different languages, and its potential is explored . it can learn from a few examples "via text interaction" and is scalable to many languages .
GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation (2021.findings-emnlp)

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Challenge: Recent studies report that prompt-based direct classification eliminates the need for fine-tuning but lacks data and inference scalability.
Approach: They propose a data augmentation technique that leverages large-scale language models to generate real text samples from a mixture of real samples.
Outcome: The proposed method outperforms existing methods on diverse classification tasks.
Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish (2022.lrec-1)

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Challenge: a prerequisite for building large-scale generative models for other languages is access to large amounts of high-quality text data and powerful computational resources.
Approach: They present a 3.5 billion parameter autoregressive language model, trained on a 100 GB Swedish corpus.
Outcome: The proposed model performs well on a 100 GB Swedish corpus and is competent in comparison with existing models of similar size.
The Power of Scale for Parameter-Efficient Prompt Tuning (2021.emnlp-main)

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Challenge: Unlike discrete text prompts used by GPT-3, soft prompts are learned through backpropagation and can be tuned to incorporate signals from any number of labeled examples.
Approach: They propose a mechanism for learning "soft prompts" to condition frozen language models to perform specific downstream tasks.
Outcome: The proposed method outperforms fewshot learning using GPT-3 and matches the quality of model tuning as models exceed billions of parameters.
MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization (2023.findings-emnlp)

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Challenge: Existing research emphasizes the importance of adapting prompts to specific tasks, rather than specific LLMs.
Approach: They propose a model-adaptive prompt optimizer method that optimizes original prompts for each LLM in downstream tasks.
Outcome: The proposed method can optimize prompts for an LLM in downstream tasks.
HyperT5: Towards Compute-Efficient Korean Language Modeling (2023.acl-industry)

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Challenge: Pretraining and fine-tuning language models is a common practice in NLP, but deploying general-purpose language models without the abundant computation or data resources is proving difficult.
Approach: They propose a sequence-to-sequence language model architecture that can be more practical and compute-efficient than the decoder-oriented approach.
Outcome: The proposed language model outperforms competing models in Korean benchmarks and is more efficient in low-resource settings.
Few-shot Learning with Multilingual Generative Language Models (2022.emnlp-main)

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Challenge: Large-scale generative language models such as GPT-3 are competitive few-shot learners.
Approach: They train multilingual generative language models on a corpus covering a diverse set of languages and study their few- and zero-shot learning capabilities.
Outcome: The proposed model outperforms GPT-3 on 171 out of 182 directions with 32 training examples and surpasses the official supervised baseline in 45 directions.
A Survey of Post-Training Scaling in Large Language Models (2025.acl-long)

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Challenge: Large language models (LLMs) have demonstrated proficiency in understanding and generating human natural languages.
Approach: They propose a framework for scaling large language models using supervised fine-tuning, RLxF and test-time compute methodologies.
Outcome: The proposed model can be used to understand and generate human natural languages.
It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners (2021.naacl-main)

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Challenge: Pretraining ever-larger language models on massive corpora requires enormous amounts of compute.
Approach: They propose to convert textual inputs into cloze questions that contain a task description . they also exploit unlabeled data to improve their performance .
Outcome: The proposed model outperforms GPT-3 with PET/iPET with cloze questions and unlabeled data.

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