Survey on large language models alignment research
With the rapid development of artificial intelligence technology, large language models have been widely applied in numerous fields. However, the potential of large language models to generate inaccurate, misleading, or even harmful contents has raised concerns about their reliability. Adopting alig...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2024-06-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024151/ |
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| _version_ | 1841528841588178944 |
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| author | LIU Kunlin QU Xinji TAN Fang KANG Honghui ZHAO Shaowei SHI Rong |
| author_facet | LIU Kunlin QU Xinji TAN Fang KANG Honghui ZHAO Shaowei SHI Rong |
| author_sort | LIU Kunlin |
| collection | DOAJ |
| description | With the rapid development of artificial intelligence technology, large language models have been widely applied in numerous fields. However, the potential of large language models to generate inaccurate, misleading, or even harmful contents has raised concerns about their reliability. Adopting alignment techniques to ensure the behavior of large language models is consistent with human values has become an urgent issue to address. Recent research progress on alignment techniques for large language models were surveyed. Common methods for collecting instruction data and human preference datasets were introduced, research on supervised tuning and alignment adjustments was summarized, commonly used datasets and methods for model evaluation were discussed, and future research directions were concluded. |
| format | Article |
| id | doaj-art-44a90e4ec6b94a73a4fcf63e4aa892ab |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2024-06-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-44a90e4ec6b94a73a4fcf63e4aa892ab2025-01-15T03:33:33ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012024-06-014017319464852434Survey on large language models alignment researchLIU KunlinQU XinjiTAN FangKANG HonghuiZHAO ShaoweiSHI RongWith the rapid development of artificial intelligence technology, large language models have been widely applied in numerous fields. However, the potential of large language models to generate inaccurate, misleading, or even harmful contents has raised concerns about their reliability. Adopting alignment techniques to ensure the behavior of large language models is consistent with human values has become an urgent issue to address. Recent research progress on alignment techniques for large language models were surveyed. Common methods for collecting instruction data and human preference datasets were introduced, research on supervised tuning and alignment adjustments was summarized, commonly used datasets and methods for model evaluation were discussed, and future research directions were concluded.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024151/large language modelalignment techniquetunereinforcement learning |
| spellingShingle | LIU Kunlin QU Xinji TAN Fang KANG Honghui ZHAO Shaowei SHI Rong Survey on large language models alignment research Dianxin kexue large language model alignment technique tune reinforcement learning |
| title | Survey on large language models alignment research |
| title_full | Survey on large language models alignment research |
| title_fullStr | Survey on large language models alignment research |
| title_full_unstemmed | Survey on large language models alignment research |
| title_short | Survey on large language models alignment research |
| title_sort | survey on large language models alignment research |
| topic | large language model alignment technique tune reinforcement learning |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024151/ |
| work_keys_str_mv | AT liukunlin surveyonlargelanguagemodelsalignmentresearch AT quxinji surveyonlargelanguagemodelsalignmentresearch AT tanfang surveyonlargelanguagemodelsalignmentresearch AT kanghonghui surveyonlargelanguagemodelsalignmentresearch AT zhaoshaowei surveyonlargelanguagemodelsalignmentresearch AT shirong surveyonlargelanguagemodelsalignmentresearch |