[2] Llama2 team. Llama 2: Open foundation and fine-tuned chat models. Arxiv, 2307.09288, 2023b. [3] Shouyuan Chen, Sherman Wong, Liangjian Chen, and Yuandong Tian. Extending context window of large language models via positional interpolation. Arxiv, 2306.15595, 2023. [4] Szymon Tworkowski...
emozilla, 2023. Dynamically Scaled RoPE further increases performance of long context LLaMA with zero fine-tuning.link Peng et al, 2023. YaRN: Efficient Context Window Extension of Large Language Models.link Press et al, 2022. Train Short, Test Long: Attention with linear biases enables input l...
Using this methodology, they employed a large context window filled with numerous examples, including...
23年6月,Meta在《EXTENDING CONTEXT WINDOW OF LARGE LANGUAGE MODELS VIA POSITION INTERPOLATION》中就提出了针对RoPE的线性插值方法PI(Position Interpolation),可以把2k的基础模型扩展到32k,并在1k个step的训练下就达到很好的效果。 In contrast, LLaMA models that are extended via direct fine-tuning only saw a...
论文标题:LLM Maybe LongLM:Self-Extend LLM Context Window Without Tuning 论文地址:https://arxiv.org/abs/2401.01325 这篇论文提出了一种非常简单的技术(只有 4 行代码),无需任何微调便能扩展 LLM 的上下文处理能力。 论文标题:A Comprehensive Study of Knowledge Editing for Large Language Models 论文地址:...
RAG or Long Context Window? 数据分析相关技术 LLM在表格数据理解任务中的应用思考 ReAcTable论文解读 Chain-of-Table论文解读 Rethinking Tabular Data Understanding with Large Language Models阅读笔记 论文解读:GPT用于表格数据理解任务的探索 Agent LLM Agent简介 多模态 LLM用于多模态任务系列一:HuggingGPT LLM用于多...
这些记忆对于智能体有效的进行推理规划是必要的;根据这个原则,我们可以有效地设计智能体的记忆模块来应对不同的记忆需求。具体而言,LLMs受其Transformer架构的上下文窗口(Context Window)信息长度限制,适合于短期记忆。通过记忆存储,比如外部存储,智能体可以根据需要快速查询和检索的长期记忆信息。
[3] Shouyuan Chen, Sherman Wong, Liangjian Chen, and Yuandong Tian. Extending context window of large language models via positional interpolation. Arxiv, 2306.15595, 2023. [4] Szymon Tworkowski, Konrad Staniszewski, Mikolaj Pacek, Yuhuai Wu, Henryk Michalewski, and Piotr Milos. Focused transform...
在NBCE 之前,能够不微调地扩展 Context 长度的方案是 Parallel Context Window(下面简称 PCW),出自论文《Parallel Context Windows for Large Language Models》[3]和《Structured Prompting: Scaling In-Context Learning to 1,000 Examples》[4],两篇论文是同一时期不同作者的工作,但所提的方法只有细微的差别,因此...
("Reuse the client between requests. When doing anything with large ""volumes of async API calls, setting this to false can improve stability."),)_client:Optional[Any]=PrivateAttr()def__init__(self,model:str=DEFAULT_MODEL,reuse_client:bool=True,api_key:Optional[str]=None,**kwargs:Any,...