点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 近年来，以LLM、VLM和VLA为代表的基础模型在自动驾驶决策中扮演着越来越重要的角色，吸引了学术界和 工业界越来越多的关注。许多小伙伴们询问是否有系统的分类汇总。本文按照模型类别，对决策的基础模型进行 汇总，后续还将进一步梳理相关算法，并第一时间汇总至『自动驾驶之心知识星球』，欢迎大家一起学习交流~ 基于LLM的方法 基于LLM的方法主要是利用大模型的推理能力描述自动驾驶，输入自动驾驶和大模型结合的早期阶段，但仍然 值得学习~ Distilling Multi-modal Large Language Models for Autonomous Driving LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models CoT-Drive: Efficient Motion Forecasting for Autonomous Driving with LLMs and Chain ...

Core Viewpoint - The article discusses the application of Large Language Models (LLM) in stock research, focusing on factor mining and stock review processes to enhance investment research efficiency and effectiveness [1][6]. Factor Mining Framework - The design of prompts is crucial in guiding the direction of factor creation within the LLM-based framework, impacting the probability of generating high IC factors [2][16]. - Factors generated using LLM have a strong interpretability compared to machine learning factors, and innovative operators can optimize existing factors, achieving an IC_IR of 0.78 during backtesting [3][19]. - The LLM can create new factors that are less correlated with existing ones, enhancing the diversity of investment strategies [20][23]. Stock Review System - The LLM-based stock review system extracts key information from various sources, significantly improving the efficiency of stock reviews by over 70% compared to traditional methods [4][27]. - The system utilizes a retrieval-augmented generation (RAG) approach to compare current information with historical data, providing initial assessments of stock price impacts [25][31]. - The review process can yield valuable insights, although the depth of analysis may be limited, necessitating improvements in prompt design and input quality [30][34]. Performance and Effectiveness - The LLM's stock review framework has shown promising results in predicting stock price movements, particularly with long-term scoring metrics indicating potential future performance [35][37]. - A simple long-only timing strategy based on LLM-generated scores has demonstrated the ability to capture upward price movements effectively, improving annualized returns and reducing maximum drawdown [42][43].