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Canada-0-MATTRESSES Azienda Directories
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Azienda News:
- ANSWERED: Adaptive Tool-Augmented LLMs with Strategic Error Feedback . . .
In this paper, we propose a framework ANSWERED: adaptive tool-augmented LLMs with Strategic Error Feedback for Compositional Reasoning, which aims to overcome a range of deficiencies in model reasoning, thereby enhancing the model’s ability for composite reasoning
- ANSWERED: Adaptive Tool-Augmented LLMs with Strategic Error Feedback . . .
In this paper, we propose a framework ANSWERED: adaptive tool-augmented LLMs with Strategic Error Feedback for Compositional Reasoning, which aims to overcome a range of deficiencies in model reasoning, thereby enhancing the model’s ability for composite reasoning
- ANSWERED: Adaptive Tool-Augmented LLMs with Strategic Error Feedback . . .
Bibliographic details on ANSWERED: Adaptive Tool-Augmented LLMs with Strategic Error Feedback for Compositional Reasoning
- ANSWERED: Adaptive Tool-Augmented LLMs with Strategic Error Feedback . . .
We explore a general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) -- models which combine pre-trained parametric and non-parametric memory for language generation
- ANSWERED: Adaptive Tool-Augmented LLMs with Strategic . . .
Article "ANSWERED: Adaptive Tool-Augmented LLMs with Strategic Error Feedback for Compositional Reasoning" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as "JST")
- Xichuan Zhang - Home
ANSWERED: Adaptive Tool-Augmented LLMs with Strategic Error Feedback for Compositional Reasoning Yuchen Pan, Xichuan Zhang, Haoyu Zhang, + 4 August 2024Advanced Intelligent Computing Technology and Applications https: doi org 10 1007 978-981-97-5669-8_22 View all Publications
- Zhiyuan Wang | Semantic Scholar
The proposed CReTooL (Complex Reasoning through diversified Tool Learning), designed to improve LLMs' complex reasoning by adaptively leveraging diverse tools, achieves excellent performance in complex reasoning, with strong tool adaptive planning and leveraging capabilities
- Advancing Tool-Augmented Large Language Models: Integrating. . .
This paper presents a method to enhance tool-augmented large language models (LLMs) by leveraging preference data extracted from both successful and failed trajectories
- API-Bank: 一个工具-增强LLMs的综合基准 - 知乎
API-Bank不仅评估现有LLMs使用工具的有效性，而且还提高他们使用这些工具的表现。 实现这一目标的最直接方法是创建适合工具-增强LLMs的高质量训练数据集。 然而，以低成本构建大规模训练数据集，同时满足域多样性和 API 真实性的设计原则，是具有挑战性的。
- Advancing Tool-Augmented Large Language Models via Meta-Verification . . .
Empowering large language models (LLMs) with effective tool utilization capabilities is crucial for enabling AI agents to solve complex problems
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