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This study constructed a Japanese chat dataset for tuning large language models (LLMs), which consist of about 8.4 million records.
In: Language Resources and Evaluation, pp. 2420–2423 (2008)
Isahara, H., Bond, F., Uchimoto, K., Utiyama, M., Kanzaki, K.: Development of the Japanese WordNet · 2008
Earlier work this paper cites.
In: Theory and Applications of Ontology: Computer Applications, pp. 231–243. Springer (2010)
Fellbaum, C.: WordNet · 2010
Earlier work this paper cites.
In: 2016 Conference of The Oriental Chapter of International Committee for Coordination and Standardization of Speech Databases and Assessment Techniques, pp. 1–6 (2016)
Riza, H., Purwoadi, M., Uliniansyah, T., Ti, A.A., Aljunied, S.M., Mai, L.C., Thang, V.T., Thai, N.P., Chea, V., Sam, S., et al.: Introduction of the asian language treebank · 2016
Earlier work this paper cites.
In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation, pp. 461–466 (2018)
Katsuta, A., Yamamoto, K.: Crowdsourced Corpus of Sentence Simplification with Core Vocabulary · 2018
Earlier work this paper cites.
In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation, pp. 1153–1160 (2018)
Maruyama, T., Yamamoto, K.: Simplified Corpus with Core Vocabulary · 2018
Earlier work this paper cites.
Language Resources and Evaluation Conference pp. 1133–1137 (2018)
Pryzant, R., Chung, Y., Jurafsky, D., Britz, D.: JESC: Japanese-English Subtitle Corpus · 2018
Earlier work this paper cites.
URL https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training (2018) · 2018
Earlier work this paper cites.
In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, pp. 4171–4186. Association for Computational Linguistics (2019)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding · 2019
Earlier work this paper cites.
URL https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners (2019) · 2019
Earlier work this paper cites.
URL https://www2.nict.go.jp/astrec-att/member/mutiyama/paranatcom/
Utiyama, M.: ParaNatCom — Parallel English-Japanese abstract corpus made from Nature Communications articles (2019) · 2019
Cited alongside, same era.
Advances in Neural Information Processing Systems 33
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: Language models are few-shot learners · 2020
Cited alongside, same era.
In: SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–16 (2020)
Rajbhandari, S., Rasley, J., Ruwase, O., He, Y.: ZeRO: Memory Optimizations toward Training Trillion Parameter Models · 2020
Cited alongside, same era.
In: Proceedings of the 12th Language Resources and Evaluation Conference, pp. 3640–3649 (2020)
Song, H., Dabre, R., Fujita, A., Kurohashi, S.: Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation · 2020
Cited alongside, same era.
DOI 10.5281/zenodo.5371628
URL https://arxiv.org/abs/2211.05100
Scao, T.L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A.S., Yvon, F., Gallé, M., et al.: Bloom: A 176b-parameter open-access multilingual language model (2022) · 2022
Later among the works it cites.
URL https://github.com/databrickslabs/dolly
Databricks: Dolly (2023) · 2023
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URL https://bard.google.com/
Google: Bard (2023) · 2023
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URL https://openai.com/blog/chatgpt/
OpenAI: ChatGPT (2023) · 2023
Closest in time.
URL https://arxiv.org/abs/2303.08774
OpenAI: GPT-4 Technical Report (2023) · 2023
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URL https://github.com/tatsu-lab/stanford_alpaca
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., Hashimoto, T.B.: Stanford Alpaca: An Instruction-following LLaMA model (2023) · 2023
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Gao, L., Tow, J., Biderman, S., Black, S., DiPofi, A., Foster, C., Golding, L., Hsu, J., McDonell, K., Muennighoff, N., Phang, J., Reynolds, L., Tang, E., Thite, A., Wang, B., Wang, K., Zou, A.: A Framework for Few-shot Language Model Evaluation (2021) · 2021
Cited alongside, same era.
URL https://arxiv.org/abs/2106.09685
Hu, E.J., yelong shen, Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: LoRA: Low-Rank Adaptation of Large Language Models (2022) · 2022
Cited alongside, same era.
In: Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering, pp. 83–92 (2022)
Kodama, T., Tanaka, R., Kurohashi, S.: Construction of Hierarchical Structured Knowledge-based Recommendation Dialogue Dataset and Dialogue System · 2022
Cited alongside, same era.
In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 2957–2966 (2022)
Kurihara, K., Kawahara, D., Shibata, T.: JGLUE: Japanese General Language Understanding Evaluation · 2022
Cited alongside, same era.
URL https://github.com/huggingface/peft
Mangrulkar, S., Gugger, S., Debut, L., Belkada, Y., Paul, S.: PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods (2022) · 2022
Cited alongside, same era.
Closest in time.
URL https://arxiv.org/abs/2302.13971
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models (2023) · 2023
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Vicuna: Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality (2023) · 2023
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