One-shot visual language understanding with cross-modal translation and LLMs

Add to your list(s) Download to your calendar using vCal

• Fangyu Liu, University of Cambridge
• Thursday 23 February 2023, 11:00-12:00
• GR04, English Faculty Building, 9 West Road, Sidgwick Site.

If you have a question about this talk, please contact Marinela Parovic.

This talk has been canceled/deleted

Visual language such as charts and plots is ubiquitous in the human world. Comprehending plots and charts requires strong reasoning skills. Prior state-of-the-art (SOTA) models require at least tens of thousands of training examples and their reasoning capabilities are still much limited, especially on complex human-written queries. We present the first one-shot solution to visual language reasoning. We decompose the challenge of visual language reasoning into two steps: (1) plot-to-text translation, and (2) reasoning over the translated text. The key in this method is a modality conversion module, named as DePlot, which translates the image of a plot or chart to a linearized table. The output of DePlot can then be directly used to prompt a pretrained large language model (LLM), exploiting the few-shot reasoning capabilities of LLMs. To obtain DePlot, we standardize the plot-to-table task by establishing unified task formats and metrics, and train DePlot end-to-end on this task. DePlot can then be used off-the-shelf together with LLMs in a plug-and-play fashion. Compared with a SOTA model finetuned on thousands of data points, DePlot+LLM with just one-shot prompting achieves a 29.4% improvement over finetuned SOTA on human-written queries from the task of chart QA.

This talk is part of the Language Technology Lab Seminars series.

This talk is included in these lists:

This talk is not included in any other list

Note that ex-directory lists are not shown.