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Understanding labour market dynamics requires accurately identifying the skills required for and possessed by the workforce.
R. Nogueira, K. Cho, Passage re-ranking with bert, 2020. arXiv:1901.04085
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Scikit-learn: Machine learning in python,
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Esco: Boosting job matching in europe with semantic interoperability,
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The evolution of technical communication: An analysis of industry job postings,
E. Brumberger, C. Lauer, · 2015
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Learning representations for soft skill matching,
L. Sayfullina, E. Malmi, J. Kannala, · 2018
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Skills mismatch: Concepts, measurement and policy approaches,
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The future of jobs report 2020,
V. World Economic Forum, · 2020
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Retrieving skills from job descriptions: A language model based extreme multi-label classification framework,
A. Bhola, K. Halder, A. Prasad, M.-Y. Kan, · 2020
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Language models are few-shot learners,
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K. F. F. Jiechieu, N. Tsopze, · 2021
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Documenting large webtext corpora: A case study on the colossal clean crawled corpus,
J. Dodge, M. Sap, A. Marasović, W. Agnew, G. Ilharco, D. Groeneveld, M. Mitchell, M. Gardner, · 2021
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k-nearest neighbour classifiers-a tutorial,
P. Cunningham, S. J. Delany, · 2021
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Evaluating large language models trained on code,
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. d. O. Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, et al., · 2021
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Jobbert: Understanding job titles through skills,
J.-J. Decorte, J. Van Hautte, T. Demeester, C. Develder, · 2021
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Palm: Scaling language modeling with pathways,
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Escoxlm-r: Multilingual taxonomy-driven pre-training for the job market domain,
M. Zhang, R. van der Goot, B. Plank, · 2023
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Jobxmlc: Extreme multi-label classification of job skills with graph neural networks,
N. Goyal, J. Kalra, C. Sharma, R. Mutharaju, N. Sachdeva, P. Kumaraguru, · 2023
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D. Beauchemin, J. Laumonier, Y. L. Ster, M. Yassine, · 2022
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M. Zhang, K. N. Jensen, B. Plank, · 2022
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2022
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Scaling instruction-finetuned language models,
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Training language models to follow instructions with human feedback,
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Language models of code are few-shot commonsense learners,
A. Madaan, S. Zhou, U. Alon, Y. Yang, G. Neubig, · 2022
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A survey of large language models,
W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, et al., · 2023
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OpenAI, Gpt-4 technical report, 2023. arXiv:2303.08774
2023
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Ul2: Unifying language learning paradigms,
Y. Tay, M. Dehghani, V. Q. Tran, X. Garcia, J. Wei, X. Wang, H. W. Chung, D. Bahri, T. Schuster, S. Zheng, et al., · 2023
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Falcon-40B: an open large language model with state-of-the-art performance (2023)
E. Almazrouei, H. Alobeidli, A. Alshamsi, A. Cappelli, R. Cojocaru, M. Debbah, E. Goffinet, D. Heslow, J. Launay, Q. Malartic, B. Noune, B. Pannier, G. Penedo, · 2023
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Llama: Open and efficient foundation language models,
H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al., · 2023
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Microsoft, Learn how to work with the ChatGPT and GPT-4 models (preview), 2023
2023
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Orca: Progressive learning from complex explanation traces of gpt-4,
S. Mukherjee, A. Mitra, G. Jawahar, S. Agarwal, H. Palangi, A. Awadallah, · 2023
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W.-L. Chiang, Z. Li, Z. Lin, Y. Sheng, Z. Wu, H. Zhang, L. Zheng, S. Zhuang, Y. Zhuang, J. E. Gonzalez, I. Stoica, E. P. Xing, Vicuna: An open-source chatbot impressing gpt-4, 2023. URL: https://lmsys.org/blog/2023-03-30-vicuna/
2023
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