arXiv:2305.16896
공식 코드 · Papers with Code
MultiTool-CoT: GPT-3 Can Use Multiple External Tools with Chain of Thought Prompting
원문 초록
Large language models (LLMs) have achieved impressive performance on various reasoning tasks. To further improve the performance, we propose MultiTool-CoT, a novel framework that leverages chain-of-thought (CoT) prompting to incorporate multiple external tools, such as a calculator and a knowledge retriever, during the reasoning process. We apply MultiTool-CoT to the Task 2 dataset of NumGLUE, which requires both numerical reasoning and domain-specific knowledge. The experiments show that our method significantly outperforms strong baselines and achieves state-of-the-art performance.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts
LLM-BABYBENCH: Understanding and Evaluating Grounded Planning and Reasoning in LLMs
1$^{st}$ Place Solution of WWW 2025 EReL@MIR Workshop Multimodal CTR Prediction Challenge
Feature Fusion Revisited: Multimodal CTR Prediction for MMCTR Challenge
Quadratic Interest Network for Multimodal Click-Through Rate Prediction
FinBERT-QA: Financial Question Answering with pre-trained BERT Language Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.