KIT-19: A Comprehensive Korean Instruction Toolkit on 19 Tasks for Fine-Tuning Korean Large Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Instruction tuning on large language models is an essential process for models to function well and achieve high performance in the specific tasks. |
| Approach: | They propose to use KIT-19 as an instruction dataset for the development of LLM in Korean to demonstrate its effectiveness. |
| Outcome: | The proposed model outperforms existing Korean LLMs. |
Similar Papers
Demystifying Instruction Mixing for Fine-tuning Large Language Models (2024.acl-srw)
Copied to clipboard
| Challenge: | Instruction tuning is effective for aligning large language models with human instructions, but the procedure to optimizing the mixing of instruction datasets is still unclear. |
| Approach: | They categorize instructions into three primary types: NLP downstream tasks, coding, and general chat. |
| Outcome: | The proposed method improves performance of large language models (LLMs) but it is difficult to combine different instruction datasets to optimize overall performance. |
Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai (2024.acl-srw)
Copied to clipboard
| Challenge: | Xue et al., 2024) have demonstrated that large language models can perform at human level across multitudes of tasks and domains. |
| Approach: | They propose a seed-free framework for generating synthetic instruction-tuning data that incorporates fluency, diversity, and cultural context. |
| Outcome: | The proposed framework achieves competitive performance using only 5,000 instructions compared to state-of-the-art Thai LLMs trained on hundreds of thousands of instructions. |
Dynamics of Instruction Fine-Tuning for Chinese Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Instruction tuning is a burgeoning method to elicit the general intelligence of Large Language Models. |
| Approach: | They investigate the effects of data quantity, model size, and data construction methods on instruction tuning for Chinese LLMs. |
| Outcome: | The proposed model includes over 40,000 high-quality instruction instances covering ten underlying abilities. |
An Empirical Study of Instruction-tuning Large Language Models in Chinese (2023.findings-emnlp)
Copied to clipboard
| Challenge: | emergence of ChatGPT validates the potential of large language models (LLMs) in artificial general intelligence (AGI) however, the closed source of LLMs coupled with the requirement for massive computing resources has deterred researchers from reaching the LLM training stage. |
| Approach: | They propose to use Chinese instruction-tuning LLMs as a cookbook for customizing LLM models that can better respond to Chinese instructions. |
| Outcome: | The proposed LLM can be used to customize Chinese LLMs that can better respond to Chinese instructions. |
Data and Model Centric Approaches for Expansion of Large Language Models to New languages (2025.emnlp-tutorials)
Copied to clipboard
| Challenge: | Existing LLMs mainly support English alongside a handful of high resource languages . this leaves a major gap for most low-resource languages despite increasing pace of research . |
| Approach: | This tutorial examines approaches to expand the language coverage of LLMs . they look at tokenizer training, pre-training, instruction tuning, alignment, evaluation, etc. |
| Outcome: | This tutorial examines approaches to expand the language coverage of LLMs . it provides an efficient and viable path to bring LLM technologies to low-resource languages . |
SIFT-50M: A Large-Scale Multilingual Dataset for Speech Instruction Fine-Tuning (2025.acl-long)
Copied to clipboard
Prabhat Pandey, Rupak Vignesh Swaminathan, K V Vijay Girish, Arunasish Sen, Jian. Xie, Grant Strimel, Andreas Schwarz
| Challenge: | Recent years have witnessed significant advancements in integrating speech and audio capabilities into large language models. |
| Approach: | They propose a 50M-example dataset for instruction fine-tuning and pre-training of speech-text large language models (LLMs) the dataset spans five languages and enables a diverse range of speech understanding and controllable speech generation instructions. |
| Outcome: | The proposed dataset outperforms existing speech-text LLMs on instruction-following benchmarks while achieving competitive performance on foundational speech tasks. |
InstructCoder: Instruction Tuning Large Language Models for Code Editing (2024.acl-srw)
Copied to clipboard
| Challenge: | InstructCoder is the first instruction-tuning dataset designed to adapt LLMs for general-purpose code editing. |
| Approach: | They propose to use Large Language Models to edit code based on user instructions . they use a dataset to adapt LLMs to general-purpose code editing . |
| Outcome: | The proposed model can significantly improve code editing performance compared to proprietary models . the proposed model is based on a human-written execution-based benchmark . |
Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean (2024.lrec-main)
Copied to clipboard
ChangSu Choi, Yongbin Jeong, Seoyoon Park, Inho Won, HyeonSeok Lim, SangMin Kim, Yejee Kang, Chanhyuk Yoon, Jaewan Park, Yiseul Lee, HyeJin Lee, Younggyun Hahm, Hansaem Kim, KyungTae Lim
| Challenge: | Large language models (LLMs) use pretraining to predict the subsequent word, but less-resourced languages are being overlooked. |
| Approach: | They propose to expand the MLLM vocabularies to enhance expressiveness and use bilingual data for pretraining to align the high- and less-resourced languages. |
| Outcome: | The proposed model outperforms existing models in qualitative analyses compared to Korean monolingual models. |
EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models (2024.acl-demos)
Copied to clipboard
Yixin Ou, Ningyu Zhang, Honghao Gui, Ziwen Xu, Shuofei Qiao, Runnan Fang, Lei Li, Zhen Bi, Guozhou Zheng, Huajun Chen
| Challenge: | Large Language Models (LLMs) have improved performance across tasks and domains . instruction tuning is a crucial technique to enhance the capabilities of LLMs - but there is no standard open-source instruction processing framework available for the community . |
| Approach: | They propose an open-source instruction tuning framework for Large Language Models that modularizes instruction generation, selection, prompting and their combination and interaction. |
| Outcome: | The proposed framework is open-source and available on Github. |
Smaller Language Models are capable of selecting Instruction-Tuning Training Data for Larger Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Instruction tuning language models can be expensive and expensive to train . current methods require extensive training on large datasets, resulting in high training costs. |
| Approach: | They propose a novel approach to selecting training data based on the learning percentage of the samples. |
| Outcome: | The proposed model performs better on models ranging from 1B to 13B in size compared to training on the entire dataset. |