The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866913528143675392 |
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| author | Zhou, Yuxiang Li, Jiazheng Xiang, Yanzheng Yan, Hanqi Gui, Lin He, Yulan |
| author_facet | Zhou, Yuxiang Li, Jiazheng Xiang, Yanzheng Yan, Hanqi Gui, Lin He, Yulan |
| contents | Understanding in-context learning (ICL) capability that enables large language models (LLMs) to excel in proficiency through demonstration examples is of utmost importance. This importance stems not only from the better utilization of this capability across various tasks, but also from the proactive identification and mitigation of potential risks, including concerns regarding truthfulness, bias, and toxicity, that may arise alongside the capability. In this paper, we present a thorough survey on the interpretation and analysis of in-context learning. First, we provide a concise introduction to the background and definition of in-context learning. Then, we give an overview of advancements from two perspectives: 1) a theoretical perspective, emphasizing studies on mechanistic interpretability and delving into the mathematical foundations behind ICL; and 2) an empirical perspective, concerning studies that empirically analyze factors associated with ICL. We conclude by highlighting the challenges encountered and suggesting potential avenues for future research. We believe that our work establishes the basis for further exploration into the interpretation of in-context learning. Additionally, we have created a repository containing the resources referenced in our survey. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_00237 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis Zhou, Yuxiang Li, Jiazheng Xiang, Yanzheng Yan, Hanqi Gui, Lin He, Yulan Computation and Language Understanding in-context learning (ICL) capability that enables large language models (LLMs) to excel in proficiency through demonstration examples is of utmost importance. This importance stems not only from the better utilization of this capability across various tasks, but also from the proactive identification and mitigation of potential risks, including concerns regarding truthfulness, bias, and toxicity, that may arise alongside the capability. In this paper, we present a thorough survey on the interpretation and analysis of in-context learning. First, we provide a concise introduction to the background and definition of in-context learning. Then, we give an overview of advancements from two perspectives: 1) a theoretical perspective, emphasizing studies on mechanistic interpretability and delving into the mathematical foundations behind ICL; and 2) an empirical perspective, concerning studies that empirically analyze factors associated with ICL. We conclude by highlighting the challenges encountered and suggesting potential avenues for future research. We believe that our work establishes the basis for further exploration into the interpretation of in-context learning. Additionally, we have created a repository containing the resources referenced in our survey. |
| title | The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2311.00237 |