Update README.md
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README.md
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@@ -37,95 +37,4 @@ Dr. Yunsung Ji (Saxo), a data scientist at Linkbricks, a company specializing in
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It is a Korean language model trained to handle complex Korean logic problems through Korean-Chinese-English-Japanese cross-training data and logical data, and Tokenizer uses the base model without word expansion.
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www.linkbricks.com, www.linkbricks.vc
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@inproceedings{park2024open,
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title={Open Ko-LLM Leaderboard: Evaluating Large Language Models in Korean with Ko-H5 Benchmark},
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author={Chanjun Park and Hyeonwoo Kim and Dahyun Kim and Seonghwan Cho and Sanghoon Kim and Sukyung Lee and Yungi Kim and Hwalsuk Lee},
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year={2024},
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booktitle={The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) }
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}
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@software{eval-harness,
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author = {Gao, Leo and
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Tow, Jonathan and
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Biderman, Stella and
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Black, Sid and
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DiPofi, Anthony and
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Foster, Charles and
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Golding, Laurence and
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Hsu, Jeffrey and
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McDonell, Kyle and
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Muennighoff, Niklas and
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Phang, Jason and
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Reynolds, Laria and
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Tang, Eric and
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Thite, Anish and
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Wang, Ben and
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Wang, Kevin and
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Zou, Andy},
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title = {A framework for few-shot language model evaluation},
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month = sep,
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year = 2021,
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publisher = {Zenodo},
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version = {v0.0.1},
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doi = {10.5281/zenodo.5371628},
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url = {https://doi.org/10.5281/zenodo.5371628},
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}
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@misc{rein2023gpqagraduatelevelgoogleproofqa,
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title={GPQA: A Graduate-Level Google-Proof Q&A Benchmark},
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author={David Rein and Betty Li Hou and Asa Cooper Stickland and Jackson Petty and Richard Yuanzhe Pang and Julien Dirani and Julian Michael and Samuel R. Bowman},
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year={2023},
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eprint={2311.12022},
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archivePrefix={arXiv},
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primaryClass={cs.AI},
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url={https://arxiv.org/abs/2311.12022},
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}
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@article{sakaguchi2021winogrande,
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title={Winogrande: An adversarial winograd schema challenge at scale},
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author={Sakaguchi, Keisuke and Bras, Ronan Le and Bhagavatula, Chandra and Choi, Yejin},
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journal={Communications of the ACM},
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volume={64},
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number={9},
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pages={99--106},
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year={2021},
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publisher={ACM New York, NY, USA}
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}
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@article{cobbe2021training,
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title={Training verifiers to solve math word problems},
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author={Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and Chen, Mark and Jun, Heewoo and Kaiser, Lukasz and Plappert, Matthias and Tworek, Jerry and Hilton, Jacob and Nakano, Reiichiro and others},
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journal={arXiv preprint arXiv:2110.14168},
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year={2021}
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}
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article{paech2023eq,
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title={Eq-bench: An emotional intelligence benchmark for large language models},
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author={Paech, Samuel J},
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journal={arXiv preprint arXiv:2312.06281},
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year={2023}
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}
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@misc{zhou2023instructionfollowingevaluationlargelanguage,
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title={Instruction-Following Evaluation for Large Language Models},
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author={Jeffrey Zhou and Tianjian Lu and Swaroop Mishra and Siddhartha Brahma and Sujoy Basu and Yi Luan and Denny Zhou and Le Hou},
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year={2023},
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eprint={2311.07911},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2311.07911},
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}
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@article{lee2024kornat,
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title={KorNAT: LLM Alignment Benchmark for Korean Social Values and Common Knowledge},
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author={Lee, Jiyoung and Kim, Minwoo and Kim, Seungho and Kim, Junghwan and Won, Seunghyun and Lee, Hwaran and Choi, Edward},
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journal={arXiv preprint arXiv:2402.13605},
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year={2024}
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}
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It is a Korean language model trained to handle complex Korean logic problems through Korean-Chinese-English-Japanese cross-training data and logical data, and Tokenizer uses the base model without word expansion.
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www.linkbricks.com, www.linkbricks.vc
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