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本课程基于学术语篇结构和文体特征，结合实例，讲授如何阅读学术语篇、抓取关键信息、概括文章内容，以及选择研究课题、进行文献搜索和评判、撰写开题报告、文献综述、摘要和正文等，训练概括、下定义、图表描述、对比、因果等写作技能，帮助学生掌握学术论文写作规范，培养学术具备基本的科研能力、良好的批判性思维能力和自主学习能力，养成理性评估、合理引用、避免学术剽窃的学术素养。
私人
2
33
本课程基于学术语篇结构和文体特征，结合实例，讲授如何阅读学术语篇、抓取关键信息、概括文章内容，以及选择研究课题、进行文献搜索和评判、撰写开题报告、文献综述、摘要和正文等，训练概括、下定义、图表描述、对比、因果等写作技能，帮助学生掌握学术论文写作规范，培养学术具备基本的科研能力、良好的批判性思维能力和自主学习能力，养成理性评估、合理引用、避免学术剽窃的学术素养。
学完了111
帅Evals
Evals is a framework for evaluating LLMs (large language models) or systems built using LLMs as components. It also includes an open-source registry of challenging evals.
We now support evaluating the behavior of any system including prompt chains or tool-using agents, via the Completion Function Protocol.
With Evals, we aim to make it as simple as possible to build an eval while writing as little code as possible. An “eval” is a task used to evaluate the quality of a system’s behavior. To get started, we recommend that you follow these steps:
To get set up with evals, follow the setup instructions below.
Running evals
Learn how to run existing evals: run-evals.md.
Familiarize yourself with the existing eval templates: eval-templates.md.
Writing evals
Important: Please note that we are currently not accepting Evals with custom code! While we ask you to not submit such evals at the moment, you can still submit modelgraded evals with custom modelgraded YAML files.
Walk through the process for building an eval: build-eval.md
See an example of implementing custom eval logic: custom-eval.md.
Writing CompletionFns
Write your own completion functions: completion-fns.md
If you think you have an interesting eval, please open a PR with your contribution. OpenAI staff actively review these evals when considering improvements to upcoming models.Evals
Evals is a framework for evaluating LLMs (large language models) or systems built using LLMs as components. It also includes an open-source registry of challenging evals.
We now support evaluating the behavior of any system including prompt chains or tool-using agents, via the Completion Function Protocol.
With Evals, we aim to make it as simple as possible to build an eval while writing as little code as possible. An “eval” is a task used to evaluate the quality of a system’s behavior. To get started, we recommend that you follow these steps:
To get set up with evals, follow the setup instructions below.
Running evals
Learn how to run existing evals: run-evals.md.
Familiarize yourself with the existing eval templates: eval-templates.md.
Writing evals
Important: Please note that we are currently not accepting Evals with custom code! While we ask you to not submit such evals at the moment, you can still submit modelgraded evals with custom modelgraded YAML files.
Walk through the process for building an eval: build-eval.md
See an example of implementing custom eval logic: custom-eval.md.
Writing CompletionFns
Write your own completion functions: completion-fns.md
If you think you have an interesting eval, please open a PR with your contribution. OpenAI staff actively review these evals when considering improvements to upcoming models.Evals
Evals is a framework for evaluating LLMs (large language models) or systems built using LLMs as components. It also includes an open-source registry of challenging evals.
We now support evaluating the behavior of any system including prompt chains or tool-using agents, via the Completion Function Protocol.
With Evals, we aim to make it as simple as possible to build an eval while writing as little code as possible. An “eval” is a task used to evaluate the quality of a system’s behavior. To get started, we recommend that you follow these steps:
To get set up with evals, follow the setup instructions below.
Running evals
Learn how to run existing evals: run-evals.md.
Familiarize yourself with the existing eval templates: eval-templates.md.
Writing evals
Important: Please note that we are currently not accepting Evals with custom code! While we ask you to not submit such evals at the moment, you can still submit modelgraded evals with custom modelgraded YAML files.
Walk through the process for building an eval: build-eval.md
See an example of implementing custom eval logic: custom-eval.md.
Writing CompletionFns
Write your own completion functions: completion-fns.md
If you think you have an interesting eval, please open a PR with your contribution. OpenAI staff actively review these evals when considering improvements to upcoming models.Evals
Evals is a framework for evaluating LLMs (large language models) or systems built using LLMs as components. It also includes an open-source registry of challenging evals.
We now support evaluating the behavior of any system including prompt chains or tool-using agents, via the Completion Function Protocol.
With Evals, we aim to make it as simple as possible to build an eval while writing as little code as possible. An “eval” is a task used to evaluate the quality of a system’s behavior. To get started, we recommend that you follow these steps:
To get set up with evals, follow the setup instructions below.
Running evals
Learn how to run existing evals: run-evals.md.
Familiarize yourself with the existing eval templates: eval-templates.md.
Writing evals
Important: Please note that we are currently not accepting Evals with custom code! While we ask you to not submit such evals at the moment, you can still submit modelgraded evals with custom modelgraded YAML files.
Walk through the process for building an eval: build-eval.md
See an example of implementing custom eval logic: custom-eval.md.
Writing CompletionFns
Write your own completion functions: completion-fns.md
If you think you have an interesting eval, please open a PR with your contribution. OpenAI staff actively review these evals when considering improvements to upcoming models.
我的感受
实在太有用了，就是网页容易崩啊……