85 citations · 119 across the 6 of their papers we have counts for
6 papers
The ART of LLM Refinement: Ask, Refine, and Trust
Kumar Shridhar, Koustuv Sinha, Andrew Cohen +6
In recent years, Large Language Models (LLMs) have demonstrated remarkable generative abilities, but can they judge the quality of their own generations? A popular concept, referre…
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…
Variation of Gender Biases in Visual Recognition Models Before and After Finetuning
Jaspreet Ranjit, Tianlu Wang, Baishakhi Ray +1
We introduce a framework to measure how biases change before and after fine-tuning a large scale visual recognition model for a downstream task. Deep learning models trained on inc…
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru +15
Recent work has shown that fine-tuning large pre-trained language models on a collection of tasks described via instructions, a.k.a. instruction-tuning, improves their zero and few…
The Impact of Traceability on Software Maintenance and Evolution: A Mapping Study
Fangchao Tian, Tianlu Wang, Peng Liang +3
Software traceability plays a critical role in software maintenance and evolution. We conducted a systematic mapping study with six research questions to understand the benefits, c…