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08/07/2023
Simple synthetic data reduces sycophancy in large language models
Sycophancy is an undesirable behavior where models tailor their response...
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05/26/2023
Large Language Models as Tool Makers
Recent research shows the potential of enhancing the problem-solving abi...
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05/24/2023
Flan-MoE: Scaling Instruction-Finetuned Language Models with Sparse Mixture of Experts
The explosive growth of language models and their applications have led ...
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01/31/2023
The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
We study the design decisions of publicly available instruction tuning m...
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10/06/2022
Language Models are Multilingual Chain-of-Thought Reasoners
We evaluate the reasoning abilities of large language models in multilin...
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09/29/2022
Compositional Semantic Parsing with Large Language Models
Humans can reason compositionally when presented with new tasks. Previou...
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05/21/2022
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
We propose a novel prompting strategy, least-to-most prompting, that ena...
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03/21/2022
Self-Consistency Improves Chain of Thought Reasoning in Language Models
We explore a simple ensemble strategy, self-consistency, that significan...
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01/28/2022
Chain of Thought Prompting Elicits Reasoning in Large Language Models
Although scaling up language model size has reliably improved performanc...
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04/06/2020
MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
Natural Language Processing (NLP) has recently achieved great success by...
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09/25/2019
Extreme Language Model Compression with Optimal Subwords and Shared Projections
Pre-trained deep neural network language models such as ELMo, GPT, BERT ...
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04/26/2019
