Self-training improves few-shot learning in legal artificial intelligence tasks.

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Title: Self-training improves few-shot learning in legal artificial intelligence tasks.
Authors: Zhou, Yulin1,2,3, Qin, Yongbin1,2,3, ybqin@gzu.edu.cn, Huang, Ruizhang1,2,3, Chen, Yanping1,2,3, Lin, Chuan1,2,3, Zhou, Yuan4
Source: Artificial Intelligence & Law; Sep2025, Vol. 33 Issue 3, p809-825, 17p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 188151179
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PubType: Academic Journal
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  Data: Self-training improves few-shot learning in legal artificial intelligence tasks.
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  Data: <searchLink fieldCode="AU" term="%22Zhou%2C+Yulin%22">Zhou, Yulin</searchLink><relatesTo>1,2,3</relatesTo><br /><searchLink fieldCode="AU" term="%22Qin%2C+Yongbin%22">Qin, Yongbin</searchLink><relatesTo>1,2,3</relatesTo>, <i>ybqin@gzu.edu.cn</i><br /><searchLink fieldCode="AU" term="%22Huang%2C+Ruizhang%22">Huang, Ruizhang</searchLink><relatesTo>1,2,3</relatesTo><br /><searchLink fieldCode="AU" term="%22Chen%2C+Yanping%22">Chen, Yanping</searchLink><relatesTo>1,2,3</relatesTo><br /><searchLink fieldCode="AU" term="%22Lin%2C+Chuan%22">Lin, Chuan</searchLink><relatesTo>1,2,3</relatesTo><br /><searchLink fieldCode="AU" term="%22Zhou%2C+Yuan%22">Zhou, Yuan</searchLink><relatesTo>4</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Artificial+Intelligence+%26+Law%22">Artificial Intelligence & Law</searchLink>; Sep2025, Vol. 33 Issue 3, p809-825, 17p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=188151179
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      – Type: doi
        Value: 10.1007/s10506-024-09403-z
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      – Code: eng
        Text: English
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        PageCount: 17
        StartPage: 809
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      – TitleFull: Self-training improves few-shot learning in legal artificial intelligence tasks.
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            NameFull: Zhou, Yulin
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            NameFull: Qin, Yongbin
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            NameFull: Huang, Ruizhang
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            NameFull: Chen, Yanping
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            – D: 01
              M: 09
              Text: Sep2025
              Type: published
              Y: 2025
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              Value: 33
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