We propose an intuitive LLM prompting framework (AgentKit) for
multifunctional agents. AgentKit offers a unified framework for explicitly
constructing a complex "thought process" from simple natural language prompts.
The basic building block in AgentKit is a node, containing a natural language
prompt for a specific subtask. The user then puts together chains of nodes,
like stacking LEGO pieces. The chains of nodes can be designed to explicitly
enforce a naturally structured "thought process". For example, for the task of
writing a paper, one may start with the thought process of 1) identify a core
message, 2) identify prior research gaps, etc. The nodes in AgentKit can be
designed and combined in different ways to implement multiple advanced
capabilities including on-the-fly hierarchical planning, reflection, and
learning from interactions. In addition, due to the modular nature and the
intuitive design to simulate explicit human thought process, a basic agent
could be implemented as simple as a list of prompts for the subtasks and
therefore could be designed and tuned by someone without any programming
experience. Quantitatively, we show that agents designed through AgentKit
achieve SOTA performance on WebShop and Crafter. These advances underscore
AgentKit's potential in making LLM agents effective and accessible for a wider
range of applications. this https URL

我们提出了一种直观的 LLM 提示框架（AgentKit），用于多功能代理。AgentKit 提供了一个统一的框架，可以从简单的自然语言提示明确地构建复杂的 “思考过程”。

AgentKit: 使用图形工具进行流程工程，无需编码

AgentKit: Flow Engineering with Graphs, not Coding

The zero-shot performance of visual question answering (VQA) models relies
heavily on prompts. For example, a zero-shot VQA for disaster scenarios could
leverage well-designed Chain of Thought (CoT) prompts to stimulate the model's
potential. However, using CoT prompts has some problems, such as causing an
incorrect answer in the end due to the hallucination in the thought process. In
this paper, we propose a zero-shot VQA named Flood Disaster VQA with Two-Stage
Prompt (VQA-TSP). The model generates the thought process in the first stage
and then uses the thought process to generate the final answer in the second
stage. In particular, visual context is added in the second stage to relieve
the hallucination problem that exists in the thought process. Experimental
results show that our method exceeds the performance of state-of-the-art
zero-shot VQA models for flood disaster scenarios in total. Our study provides
a research basis for improving the performance of CoT-based zero-shot VQA.

本论文提出了一种名为洪水灾害 VQA-TSP 的零样本视觉问答模型，通过两阶段提示生成思考过程并使用视觉上下文来减轻思考过程中的幻觉问题，实验结果显示我们的方法在洪水灾害场景中超过了现有零样本 VQA 模型的性能，为改进基于 CoT 的零样本 VQA 性能提供了研究基础。