增强隐私:WebLLM在用户的浏览器中本地处理所有数据,确保用户信息不会被发送到外部服务器。这种设置不仅保护用户数据免受潜在的外部威胁,还确保了与AI的交互完全私密。 离线使用:即使在互联网连接不稳定或无法连接的地区,WebLLM也能独立运行,使其成为远程位置或旅行时用户的宝贵工具。 性能优越:利用用户设备的处理能力...
在LLM中使用Self-Consistency比较简单,关键是如何构造多条推理路径去生成不同的答案,然后找到出现次数最多的答案作为最终的结果。 构造多通推理路径通常具有如下三种方法: 方法一:在LLM使用generate()方法时,生成多个后续结果,然后找到最可能的结果。 方法二:使用不同的随机种子,多次调用LLM的generate()方法,从而获取多...
git clone git@github.com:hao-ai-lab/Consistency_LLM.git cd Consistency_LLM Install dependency: pip install -r requirements.txt pip install flash-attn==2.4.1 Model Weights CLLMs Usage Inference bash applications/run_chat_cllm.sh {model_path} {cllm_type} ...
In this post, we demonstrate the potential of large language model (LLM) debates using a supervised dataset with ground truth. In this LLM debate, we have two debater LLMs, each one taking one side of an argument and defending it based on the previous arguments for N(=3) rounds. The ...
[ICML 2024] CLLMs: Consistency Large Language Models - factor out jacobi forward to utils · hao-ai-lab/Consistency_LLM@5a2a2d6
Large Language Models (LLMs) are extensively used today across various sectors, including academia, research, business, and finance, for tasks such as text generation, summarization, and translation. Despite their widespread adoption, these models often produce incorrect and misleading information, ...
Chain-of-Thought(CoT)使Large Language Models(LLMs)在复杂的推理任务中取得了令人鼓舞的结果。 本文提出了一种新的解码策略——self-consistency,以取代贪婪解码。 self-consistency利用了一种直觉,即一个复杂的推理问题通常允许多种不同的思维方式推导出同一个正确答案。
General Instructions Evaluate the LLMs’ reasoning paths, looking for logical inconsistencies or errors that lower your confidence in their conclusions. Because the questions are very difficult, even for experts, your task is to identify general reasoning flaws, not to assess the correctness of the ...
但我在很多 PE 的教程里都有看到这个方法,所以我这里就简单介绍一下,这个方法来自于 2022 年,Luyu Gao等人的研究,根据他们的研究,在 LLM 模型中,即使使用了前面提到的 Chain of Thought 的方法,也未必能拿到答案,论文中使用的 prompt 是这样的: Q: Roger has5tennisballs.Hebuys2more cans of ...
In today's era of GenAI reliance, the lack of an evaluation metric for factual consistency poses a challenge due to hallucination. Our solution addresses this by offering a unique method to verify the accuracy of LLM model-generated responses, leveraging Claude from Anthropic as the foundation mod...