Xuezhi Wang
5 papers · Latest:
Gemini: A Family of Highly Capable Multimodal Models
Gemini is a new family of multimodal AI models excelling in image, audio, video, and text understanding, achieving state-of-the-art results across numerous benchmarks including human-expert level on MMLU.
Large Language Models Can Self-Improve
This paper shows that large language models can improve their reasoning abilities by fine-tuning on their own high-confidence, self-generated answers without any labeled data.
PaLM: Scaling Language Modeling with Pathways
PaLM is a 540-billion parameter Transformer language model that achieves state-of-the-art few-shot learning performance across diverse benchmarks, demonstrating significant benefits from scaling.
Self-Consistency Improves Chain of Thought Reasoning in Language Models
Self-consistency is a new decoding strategy that improves chain-of-thought reasoning in language models by sampling diverse reasoning paths and selecting the most consistent answer.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Chain of thought prompting, which involves providing intermediate reasoning steps in prompts, significantly enhances large language models' performance on complex reasoning tasks.
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