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- 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
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