Language Models are General-Purpose Interfaces

13 Jun 2022  ·  Yaru Hao, Haoyu Song, Li Dong, Shaohan Huang, Zewen Chi, Wenhui Wang, Shuming Ma, Furu Wei ·

Foundation models have received much attention due to their effectiveness across a broad range of downstream applications. Though there is a big convergence in terms of architecture, most pretrained models are typically still developed for specific tasks or modalities. In this work, we propose to use language models as a general-purpose interface to various foundation models. A collection of pretrained encoders perceive diverse modalities (such as vision, and language), and they dock with a language model that plays the role of a universal task layer. We propose a semi-causal language modeling objective to jointly pretrain the interface and the modular encoders. We subsume the advantages and capabilities from both causal and non-causal modeling, thereby combining the best of two worlds. Specifically, the proposed method not only inherits the capabilities of in-context learning and open-ended generation from causal language modeling, but also is conducive to finetuning because of the bidirectional encoders. More importantly, our approach seamlessly unlocks the combinations of the above capabilities, e.g., enabling in-context learning or instruction following with finetuned encoders. Experimental results across various language-only and vision-language benchmarks show that our model outperforms or is competitive with specialized models on finetuning, zero-shot generalization, and few-shot learning.

PDF Abstract

Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Image Captioning Flickr30k Captions test MetaLM CIDEr 43.3 # 4
SPICE 11.7 # 3
Image Captioning nocaps val MetaLM CIDEr 58.7 # 2
SPICE 8.6 # 2
Visual Question Answering (VQA) OK-VQA MetaLM Accuracy 11.4 # 34
Visual Question Answering (VQA) VQA v2 val MetaLM Accuracy 41.1 # 9


No methods listed for this paper. Add relevant methods here