공대생 도전 일지

https://arxiv.org/abs/2405.16510 Planning with Multi-Constraints via Collaborative Language AgentsThe rapid advancement of neural language models has sparked a new surge of intelligent agent research. Unlike traditional agents, large language model-based agents (LLM agents) have emerged as a promising paradigm for achieving artificial general intelligearxiv.org 여러 제약 조건이 있는 복잡한 작업 계획에 대해 실행 가능하거..

https://arxiv.org/abs/2305.04091 Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language ModelsLarge language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. To tackle multi-step reasoning tasks, few-shot chain-of-thought (CoT) prompting includes a few manually crafted step-by-step reasoning demonstratiarxiv.org 드디..

https://arxiv.org/abs/2403.03101 KnowAgent: Knowledge-Augmented Planning for LLM-Based AgentsLarge Language Models (LLMs) have demonstrated great potential in complex reasoning tasks, yet they fall short when tackling more sophisticated challenges, especially when interacting with environments through generating executable actions. This inadequacyarxiv.org https://zjunlp.github.io/project/KnowAg..

https://arxiv.org/abs/2402.02716 Understanding the planning of LLM agents: A surveyAs Large Language Models (LLMs) have shown significant intelligence, the progress to leverage LLMs as planning modules of autonomous agents has attracted more attention. This survey provides the first systematic view of LLM-based agents planning, coveringarxiv.org 첫 번째 Planning Survey논문이라네요Task Decomposition은 분할 정..

https://arxiv.org/abs/2410.00467 Dynamic Planning for LLM-based Graphical User Interface AutomationThe advent of large language models (LLMs) has spurred considerable interest in advancing autonomous LLMs-based agents, particularly in intriguing applications within smartphone graphical user interfaces (GUIs). When presented with a task goal, these agentarxiv.org 기존 ReAct방식은 너무 길어져서 GUI Agent나 현실..