From fa28d393d3878a419f0ebf317db183276dd286cf Mon Sep 17 00:00:00 2001
From: Maxime MORGE <maxime.morge@univ-lille.fr>
Date: Mon, 17 Feb 2025 17:47:47 +0100
Subject: [PATCH] LLM4AAMAS: minor modificiation
---
README.md | 16 ++++++++--------
1 file changed, 8 insertions(+), 8 deletions(-)
diff --git a/README.md b/README.md
index 8679ef9..74ca36c 100644
--- a/README.md
+++ b/README.md
@@ -52,14 +52,6 @@ to generative AAMAS. This list is a work in progress and will be regularly updat
**[A Survey of Large Language Models](https://arxiv.org/abs/2303.18223)**
Wayne Xin Zhao, Kun Zhou, Junyi Li, et al. (2024) Published on *arXiv*
-- Based on the planning and reasoning abilities of LLM, the paper consider
- LLM-based multi-agent systems for complex problem-solving and world
- simulation.
-
- **[Large Language Model based Multi-Agents: A Survey of Progress and
- Challenges](https://arxiv.org/abs/2402.01680)** Taicheng Guo et al. (2024)
- Published on *arXiv* arXiv:2402.01680 [cs.CL]
-
- A framework for achieving strong natural language understanding with a single
task-agnostic model through generative pre-training and discriminative
fine-tuning.
@@ -300,6 +292,14 @@ these top-tier models and their OSS competitors.
## Generative MAS
+Based on the planning and reasoning abilities of LLM, the paper considers
+LLM-based multi-agent systems for complex problem-solving and world
+simulation.
+
+- **[Large Language Model based Multi-Agents: A Survey of Progress and
+ Challenges](https://arxiv.org/abs/2402.01680)** Taicheng Guo et al. (2024)
+ Published on *arXiv* arXiv:2402.01680 [cs.CL]
+
LLMs can simulate realistic perceptions, reasoning, and decision-making, react
adaptively to environments without predefined explicit instructions by adjusting
their responses through contextual learning mechanisms, autonomously generate
--
GitLab