အပိုင်း၏ အော်ဒီယို ပျက်နေခြင်းဖြစ်နိုင်သည်။
How to fix it?
ဤအပိုင်းအကြောင်း
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. https://github.com/holmeswww/AgentKit
2024: Yue Wu, Yewen Fan, So Yeon Min, Shrimai Prabhumoye, Stephen McAleer, Yonatan Bisk, Ruslan Salakhutdinov, Yuanzhi Li, Tom M. Mitchell
https://arxiv.org/pdf/2404.11483
2024: Yue Wu, Yewen Fan, So Yeon Min, Shrimai Prabhumoye, Stephen McAleer, Yonatan Bisk, Ruslan Salakhutdinov, Yuanzhi Li, Tom M. Mitchell
https://arxiv.org/pdf/2404.11483
အင်္ဂလိပ်
အမေရိကန်ပြည်ထောင်စု
ဤအပိုင်းထဲတွင်
ပြီးခဲ့သည့် အပိုင်းများကို ရှာဖွေပါ
Papers Read on AI ၏ ယခင်အပိုင်းများကို ရှာဖွေပါ
ဤ PODCAST တွင် အခြားသော အပိုင်းများ
Large language models (LLMs) often produce errors, including factual inaccuracies, biases, and reasoning failures, collectively referred to as"hallucinations". Recent studies have demonstrated that LLMs' internal states encode information regarding the truthfulness of their outputs, and that this i…
We introduce Diagram of Thought (DoT), a framework that models iterative reasoning in large language models (LLMs) as the construction of a directed acyclic graph (DAG) within a single model. Unlike traditional approaches that represent reasoning as linear chains or trees, DoT organizes proposition…
The potential effectiveness of counterspeech as a hate speech mitigation strategy is attracting increasing interest in the NLG research community, particularly towards the task of automatically producing it. However, automatically generated responses often lack the argumentative richness which char…
Large language models (LLMs) often exhibit deficient reasoning or generate hallucinations. To address these, studies prefixed with"Self-"such as Self-Consistency, Self-Improve, and Self-Refine have been initiated. They share a commonality: involving LLMs evaluating and updating themselves. Nonethel…
In a convergence of machine learning and biology, we reveal that diffusion models are evolutionary algorithms. By considering evolution as a denoising process and reversed evolution as diffusion, we mathematically demonstrate that diffusion models inherently perform evolutionary algorithms, natural…
မသက်ဆိုင်ကြောင်းရှင်းလင်းချက်: ဤစာမျက်နှာပေါ်တွင် ထည့်သွင်းထားသော ပေါ့တ်ကာစ်နှင့် အနုပညာလက်ရာများသည် ၎င်း၏ပိုင်ရှင်၏ပိုင်ဆိုင်မှုဖြစ်သည့် Rob မှဖြစ်ပြီး Listen Notes, Inc နှင့် ဆက်နွှယ်ခြင်း သို့မဟုတ် ထောက်ခံထားခြင်းမရှိပါ။
ပြင်ဆင်ပါ
ပေါ့ဒ်ကတ်စ် ဒေတာဘေ့စ်ကို နောက်ဆုံးပေါ်အချိန်နှင့်တပြေးညီဖြစ်စေရန် ကူညီပေးသည့်အတွက် ကျေးဇူးတင်ပါသည်။
