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Listen "Ep 25: Boost Reasoning of Your Local LLM - Simple Chain-of-Thought Techniques That Work"
Episode Synopsis
Summary:
AI News Update: Explore OpenELM, a fully transparent model utilizing public datasets, setting a new standard in AI openness.
DBCopilot Breakthrough: Delve into how DBCopilot is scaling natural language querying to massive databases, transforming NL to SQL models.
Chain-of-Thought Evolution: Examine the progression from zero-shot and few-shot learning to automated Chain-of-Thought (CoT) and the innovative chain of agents concept.
Tune in to uncover how Chain-of-Thought is reshaping AI problem-solving. Don’t miss out on the latest techniques and developments—subscribe now!
This description aims to attract professionals and industry experts interested in the forefront of AI and reasoning technologies. Let me know if this fits your needs or if there are any modifications you'd like to consider!
AI News:
[2404.14619] OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework
[2312.03463] DBCopilot: Scaling Natural Language Querying to Massive Databases
References for main topic:
[2201.11903] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
[2005.11401] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
[2005.14165] Language Models are Few-Shot Learners
[2203.11171] Self-Consistency Improves Chain of Thought Reasoning in Language Models
[2205.11916] Large Language Models are Zero-Shot Reasoners
[2210.03493] Automatic Chain of Thought Prompting in Large Language Models
[2404.14963] Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Perfect Reasoners
[2404.14812] Pattern-Aware Chain-of-Thought Prompting in Large Language Models
[2404.15676] Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs
AI News Update: Explore OpenELM, a fully transparent model utilizing public datasets, setting a new standard in AI openness.
DBCopilot Breakthrough: Delve into how DBCopilot is scaling natural language querying to massive databases, transforming NL to SQL models.
Chain-of-Thought Evolution: Examine the progression from zero-shot and few-shot learning to automated Chain-of-Thought (CoT) and the innovative chain of agents concept.
Tune in to uncover how Chain-of-Thought is reshaping AI problem-solving. Don’t miss out on the latest techniques and developments—subscribe now!
This description aims to attract professionals and industry experts interested in the forefront of AI and reasoning technologies. Let me know if this fits your needs or if there are any modifications you'd like to consider!
AI News:
[2404.14619] OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework
[2312.03463] DBCopilot: Scaling Natural Language Querying to Massive Databases
References for main topic:
[2201.11903] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
[2005.11401] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
[2005.14165] Language Models are Few-Shot Learners
[2203.11171] Self-Consistency Improves Chain of Thought Reasoning in Language Models
[2205.11916] Large Language Models are Zero-Shot Reasoners
[2210.03493] Automatic Chain of Thought Prompting in Large Language Models
[2404.14963] Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Perfect Reasoners
[2404.14812] Pattern-Aware Chain-of-Thought Prompting in Large Language Models
[2404.15676] Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs
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