30 citations · 70 across the 6 of their papers we have counts for
6 papers
Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning
Lili Yu, Bowen Shi, Ramakanth Pasunuru +24
We present CM3Leon (pronounced "Chameleon"), a retrieval-augmented, token-based, decoder-only multi-modal language model capable of generating and infilling both text and images. C…
Shepherd: A Critic for Language Model Generation
Tianlu Wang, Ping Yu, Xiaoqing Ellen Tan +7
As large language models improve, there is increasing interest in techniques that leverage these models' capabilities to refine their own outputs. In this work, we introduce Shephe…
ALERT: Adapting Language Models to Reasoning Tasks
Ping Yu, Tianlu Wang, Olga Golovneva +6
Current large language models can perform reasonably well on complex tasks that require step-by-step reasoning with few-shot learning. Are these models applying reasoning skills th…
ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning
Olga Golovneva, Moya Chen, Spencer Poff +4
Large language models show improved downstream task performance when prompted to generate step-by-step reasoning to justify their final answers. These reasoning steps greatly impro…
Generative Adversarial Networks for Annotated Data Augmentation in Data Sparse NLU
Olga Golovneva, Charith Peris
Data sparsity is one of the key challenges associated with model development in Natural Language Understanding (NLU) for conversational agents. The challenge is made more complex b…
Evaluating Cross-Lingual Transfer Learning Approaches in Multilingual Conversational Agent Models
Lizhen Tan, Olga Golovneva
With the recent explosion in popularity of voice assistant devices, there is a growing interest in making them available to user populations in additional countries and languages.…