只要给定一些示例作为输入,LLM就可以完成一系列的NLP任务。然而,这些模型经常给出一些意外的行为,如捏造事实、生成带偏见或有害的文本、不遵循用户给出的指令,这是因为多数LLM的优化目标都是预测序列中的下一个token,该目标与“有益且安全地遵循用户的指令”是不同的。因此,我们说,LM的目标是misaligned。在真实应用...
Evaluating and Debugging Generative AI - Finetuning a language model 11 -- 0:17 App Evaluating and Debugging Generative AI - Conclusion 20 -- 14:20 App Building Generative AI Applications with Gradio - NLP tasks interface 22 -- 12:55 App Building Generative AI Applications with Gradio - Cha...
第二步:收集比较数据并训练奖励模型(Reward Model,RM) - 目的:建立一个能评估输出好坏的“奖励模型”。 - 具体步骤: 1. 针对同一个提示(比如“用简单语言解释月球”),让模型生成多个不同的回答(如 A: “月亮是地球的卫星”,B: “月亮是天上的光球”)。 2. 人类标注员对这些回答按优劣排序(比如 D > C...
领域:LLM 最优架构探索 一句话总结:作者对三种主流 LLM 模型架构(Causal Decoder, CD/Non-Causal Decoder, ND/Encoder-Decoder, ED)、两种主流预训练目标(Autoregress, FLM/Masked Language Modeling, MLM)、是否进行多任务微调等进行排列组合实验,找出使得 zero-shot 泛化性最佳的设定,结论如下 如果只做预训练,使...
《ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL》(2024) GitHub: github.com/YifeiZhou02/ArCHer [fig1]《ChatMusician: Fostering Intrinsic Musical Abilities Into LLM》(2024) GitHub: github.com/hf-lin/ChatMusician《DynamicBind: predicting ligand-specific protein-ligand complex ...
His research interests are hardware acceleration and hardware-software co-design for machine learning, with a particular focus on NLP applications. Useful Resources Intel AI Developer Tools and Resources oneAPI unified programming model Official documentation - Intel...
During training, something similar happens where we give the model a sequence of tokens we want to learn. We start by predicting the second token given the first one, then the third token given the first two tokens and so on. Thus, if you want to learn how to predict the sentence ...
Large Language Models (LLMs) are deep neural network models that can generate natural language texts based on a given input or context. They are trained on large amounts of text data from various domains and sources, and they can capture the syntactic an
将LLM 用于自然语言处理 (NLP) 任务已完成 100 XP 9 分钟 常见的自然语言处理 (NLP) 任务利用大型语言模型 (LLM) 的功能来应对各种与语言相关的挑战。这些任务包括:概括:将冗长的文本压缩成简明的摘要。 情绪分析:确定文本的情感基调。 翻译:在各种语言之间转换文本。 零样本分类:在没有先前示例的情况下,...
a model trained on BERT embeddings for each unigram in the input (which can be viewed as running Aug-Linear with only unigrams). We use BERT (bert-base-uncased)3as the LLM for extracting embeddings, after finetuning on each dataset; see Supplementary Table1for details on all models and ...