EPFL团队成功申请到GPT-4-Base版访问权限，用于研究上下文学习能否让大模型跟随指令。现有的SOTA方法URIAL无法全面弥补指令微调模型的差距。因此，他们尝试通过增加示例数量、使用贪心搜索算法以及消除融练实验等方式提高上下文学习效果。研究结果表明，不同的示例适用于不同的模型，且上下文学习是实现聊天机器人或AI助手的关键。

\section*{Title: Success of EPFL Team's GPT-4-Base Access}

\begin{center}

\textbf{\large EPFL Team Successfully Applies for GPT-4-Base Version Access,aiming to Research上下文 Learning能否 Improve Model's Response}\end{center}

\subsection*{Summary of the Study}
The EPFL team successfully applied for access to the GPT-4-Base version in order to research the potential of context-based learning in improving the performance of large language models such as Chatbots and AI assistants. This study aims to identify different examples that can be used to optimize model responses, and evaluate how context-based learning contributes to this process.
\subsection*{Current State of SOTA Methods}
To understand the current state of SOTA methods for context-based learning, we compare the performance of different models on various datasets. The results show that URIAL (Unsupervised Representation Learning for Information Retrieval) cannot fully address the limitations of direct supervised learning models. Therefore, the EPFL team decided to explore alternative approaches.
\subsection*{Adding More Examples and Using Heuristics}
One approach the EPFL team explored was adding more examples to training data. By providing a larger number of diverse inputs, they hoped to encourage the model to learn more robust patterns and relationships within those inputs. Another approach was using heuristics to select input examples that were most relevant to the model's task.
\subsection*{Coping with Overfitting with MDialogo}
To mitigate overfitting, the EPFL team also tried incorporating MDialogo, a method developed by EPFL researchers to handle similar tasks involving both natural language and technical terms. MDialogo involves providing relevant information about the text to help the model better understand the context.
\subsection*{Conclusion}
Overall, the EPFL team's investigation has shown promising results. By exploring different example sets and techniques, they hope to improve the effectiveness of context-based learning in enhancing the performance of large language models. As more research is conducted, it is likely that this area will continue to grow and develop in exciting ways.
\section*{References}
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\section*{Keywords: GPT-4-Base, Context-based learning, Large language models, Chatbots, AI assistants, Example sets, Heuristics, Overfitting}