Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Automatic prompt optimization with 'gradient descent' and beam search

Title	Authors	Year	Url	Publication
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes	Hsieh CY et al.	2023	source	arXiv preprint arXiv:2305.02301
Automatic prompt optimization with 'gradient descent' and beam search	Pryzant R et al.	2023	source	arXiv preprint arXiv:2305.03495
React: Synergizing reasoning and acting in language models	Yao S et al.	2022	source	arXiv preprint arXiv:2210.03629.
Chain-of-thought prompting elicits reasoning in large language models	Wei J et al.	2022	source	Advances in Neural Information Processing Systems
Tree of thoughts: Deliberate problem solving with large language models	Yao S et al.	2023	source	arXiv preprint arXiv:2305.10601
Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes	Arora S et al.	2023	source	arXiv preprint arXiv:2304.09433
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models	Duan H et al.	2023	source	arXiv preprint arXiv:2305.15594
Your language model is secretly a reward model	Rafailov R et al.	2023	source	arXiv preprint arXiv:2305.18290
The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only	Penedo G et al.	2023	source	arXiv preprint arXiv:2306.01116
Attention Is All You Need	Vaswani A et al.	2017	source	Advances in neural information processing systems
title	authors	year	url	publicationVenue